KNIME helps individuals and organizations make sense of data

KNIME

knime

Hello, in the first article of this series, I will examine the KNIME data science software. I will also explain how to install the KNIME application, which is written in Java and built on Eclipse. This application can prepare data science and artificial intelligence applications with workflow-style visual programming, and it can be configured on widely used GNU/Linux systems.  I will review its usage with a machine learning application and save the application I prepared in a Github repo. KNIME KNIME Analytics is a data science environment written in Java. This software allows visual programming in the form of a workflow using various nodes and is an application that allows to development of a data mining application even without knowing advanced coding. It has a very diverse and rich plugin center and is used quite often in academia as well. It is an extensible data science platform where user-created scripts and codes can be used as well as visual programming. KNIME is a cross-platform software that supports installation on many operating systems. You can click the to download. In this article, I will review KNIME for GNU/Linux distributions only. link Configuration You can extract the archive of the KNIME application to the desired location. tar.gz ~$ tar xvf knime-analytic-?.?.?.tar.gz -C /opt/knime All libraries and plugins for KNIME are available in the directory. There is a binary file named and you can start the application using it. But it needs to work. Knime java JAVA 11 KNIME Analytics works with Java 11 and higher. For this reason, you can install either or packages for KNIME. Depending on the distribution you use, you can easily download and install these JDKs from the main repository. openjdk-11 openjdk-latest For example, for RHEL based ones; ~$ sudo dnf/yum install java-11-openjdk # or java-latest-openjdk Or Debian based ones; ~$ sudo apt install openjdk-11-jdk # or default-jre Another option I can suggest here is the liberica JDK versions published by Belsoft. These are open-source and freely distributed Java JDK versions. You can download and install JDK with many options such as standard and full package. You can find installation scripts or archives [on this page] ( ) for many operating systems. For KNIME Analytics, standard JDK-11-lts is sufficient. For Linux, we need to download the tar.gz package and extract it to a directory. Then the and variables are updated and the Java installation is completed. https://bell-sw.com/pages/downloads/#mn JAVA_HOME PATH ~$ mkdir -p ~/libertica-jdks
~$ tar xvf /path/to/downloaded-jdk.tar.gz -C ~/libertica-jdks/

~$ export JAVA_HOME=~/libertica-jdks/jdk-11.?.?
~$ export PATH=$PATH:JAVA_HOME/bin
## after this
~$ java -version JDK is available in this session. If you want to use it in this location all the time, you should add the lines exported for JAVA_HOME and PATH to your or file if you are using zsh. .bashrc .zshrc After the Java installation, it is sufficient to go to the directory where KNIME is installed and run the script. If you have more than one JDK installed on your system, you can specifically give the path to JDK-11 with . ./knime ./knime -v /path/to/jdk-11/bin If you want to create a for this; dektop entry [Desktop Entry]
Type=Application
Name=KNIME Analytic Platform
Description=Data Science Environment
Exec=/path/to/knime-folder/knime_4.?.?/knime
Icon=/rpath/to/knime-folder/knime_4.?.?/icon.svg
Categories=Development;Science;
terminal=false You can create a file named and save the entry to the relevant location. If you have a problem with the exec command. You can give a shell script that runs the ./knime script as a parameter in the command. If you are using Wayland as a display server, you may have problems with the application. For this reason, you can add the line to your configuration file or or whatever shell you are using, or inside your script file that starts KNIME. If you're already using x11, you won't have any problems. For example, the shell script that starts KNIME; ~/.local/share/application/knime.desktop exec export GDK_BACKEND=x11 bashrc zshrc #/bin/bash
export GDK_BACKEND=x11
./knime -v /path/to/java-jdk-11/bin may form. Extension Installations The KNIME application had many extensions. The high number of users and its extensibility are the most important factors that bring it to the top, among other data mining applications. You can open the interface by selecting from the menu in the application. You can install it by searching for the add-on you want. For example, you can install the plugin that should be used to connect to the Twitter API and pull data. It will be installed with the necessary dependency packages. The application will need to be restarted before it can read new plugins. Install KNIME Extensions... File KNIME twitter connector Then you can search for the node you want from the section and add it to your project. Node Repository Machine Learning Application with KNIME In this section, a ready-made dataset will be used to prepare a machine learning application. A data mining workflow will be created with KNME nodes by using the dataset named in the . The project is created with from the menu. HCV Data Dataset UCI Machine Learning Repository new project File Node Repository I add my file to the project by searching or from the section. Similarly, I search for the transactions I want to do and add them to my project and run them, and at the end of each operation, I give the output as the input of the next node. file reader csv reader Node Repository When I right-click on the nodes, I configure the operation and then run it. When I right-click again, I can see the output of the node as a result of the operation under the menu. The node below outputs the normalized table and model. Normalize The operations performed in this application can be seen as follows. The operations performed in this application can be seen as follows. In the application, the data in the CSV file was first read with the node and the categorical values ​​such as were given to the node to be converted into numbers. The output of this node is given to the node to extract the Id and categorical gender values. The output dataset is given to the node. The dataset with 0-1 normalization was given to the node to be divided into 70% training and 30% testing. A model was created by giving the training dataset from the parts to the node. This model and test dataset was tested by giving it to the node. And the output of the node has been transformed into a table with the comparison matrix and score values ​​related to the performance in the node. csv reader sex category to number column filter Normalize Partitioning RProp MLP Learner MultiLayerPerceptron Predicter Scorer When our perceptron model was tested with 1 hidden layer and 10 neurons, it was seen that our comparison matrix and performance values, respectively; It seems to be a very successful model, here the dataset was not raw data. The performance of the data preprocessing on the data set was 98% accurate. Preprocessing is an important factor for model performance. If you create a model with the raw data set, you can see that you cannot achieve high success. Due to the preprocessing of this dataset; Reviewed with R Sample dataset review with Smote Outlier cleaning with R boxplot Second-time dataset with SMOTE balancing You can find this project and its dataset in the repo in the link. Resources and Project Github repo HCV Data Dataset Originally published . here

How to Install the KNIME Analytics Data Science Software

Machine learning is the future. But will machines ever extinct humans? That’s a far-fetched idea for sure! As a machine learning developer, you surely want to succeed in your goals. That’s where open-source tools for machine learning comes in. The machine learning open-source community is active. If you are into open-source, you will notice that there are plenty of . This also includes tools that you can use in your projects. machine learning resources In this article, we will go through the ten must-know open source tools for machine learning. These tools will help you improve your workflow. 10 Must-Know Open Source Tools for Machine Learning For non-programmers If you are new to programming or machine learning, you need to use tools that match your experience. For beginners, we suggest using these two tools. Knime is an excellent tool that lets you do end-to-end data science workflows without the need to write a single line of code. Knime : To help newbies, it comes with a drag-and-drop interface. The interface is clear, and you can create and productionize using it. The first step is to do data gathering and transforming using the tool. Once done, you can then create a model and visualize it. When it comes to production, you can deploy and manage your data science project. Lastly, you can leverage your implementation by producing insights using Knime. Uber Ludwig is another excellent tool for beginners. With it, you can quickly test and train deep learning models. Writing code is optional, and you can use the drag and drop interface to your advantage. Uber Ludwig : To get started, you need to first train your data by loading your CSV file. By using the pre-trained model, you can then predict the output targets. Lastly, you can visualize your data using the available visualization options. If you are a beginner in programming, you can also use their extensive API and train models in Python. Below is the code for training models using API. Fascinated? Check out the user guide here. For Model-Deployment Model deployment is one of the critical aspects of machine learning. To help you in the process, we have listed the following tools. TensorFlow.js lets you build and deploy machine learning models directly from the web. It uses JavaScript for running on the web. - TensorFlow.js You can also utilize Node.js for it. With it, you can not only run existing models but also retrain existing models. It offers intuitive APIs that let you build and train models using JavaScript, and that too on a web browser. You can also check out TensorFlow Lite if you want to develop on the mobile. MLFlow lets you tackle end-to-end machine learning lifecycle. It comes with three major components. MLFlow : MLflow Tracking - takes care of experiments by recording and comparing results and parameters MLflow Projects - let you package projects into reusable form for other members MLflow Models - helps you to deploy and manage ML libraries in diverse platforms Another amazing feature of MLFlow is that it is library-agnostic. This means that you can use it with other machine learning library without any compatibility issues. To achieve library-agonistic behavior, it utilizes REST API and CLI. For NLP, Computer Vision and Audio There are other, handy tools that you can utilize for doing different things in machine learning. If you are looking for state-of-the-art object detection algorithm, then you can not go wrong with Detectron. Detectron: It is developed by Facebook and is part of their AI Research software system. It utilizes Caffe2 deep learning framework and Python. SimpleCV, an open-source framework, lets you build a computer vision application. It is similar to OpenCV and enables you to get access to high-level computer vision libraries. This means that you do not have to worry about intricate concepts. SimpleCV : With it, you can make computer vision projects without investing too much time on basics. After all, it is named as SimpleCV for a reason. Tesseract OCR is a powerful Optical Character Recognition software that lets you recognize languages. Tesseract OCR: It supports more than 100+ languages and can also be programmed to recognize new languages. For Reinforcement learning If you want to train smart agents, then you need to take help of reinforcement learning. Open AI Gym lets you train your agents to do almost anything, including walking, playing games, and so on. It does it with the help of easy-to-use reinforcement learning tasks suite. Open AI Gym : Unity offers their open source unity plugin, the Unity ML agents that let you develop smart agents that can be used within games. Unity ML Agents : For Data Mining If you wish to gather data for your data science project, you can use the following tool. Weka is used for data mining tasks. It does it with the help of machine learning algorithm designed for data mining. With it, you can find a lot of things, including classification, preparation, regression, clustering, visualization, and association rules mining. Weka: The project is open-source, and GNU licensed. Conclusion Machine learning is transforming how we interact with the world. It is making our lives easier and ensuring that we build a futuristic world. are also great tools that you can use to do machine learning models. Python Sklearn and Keras So, which machine learning open source tools do you like? Comment below and let us know.

10 Must-Try Open Source Tools for Machine Learning

253 Stories To Learn About Data Analysis

Forty-four percent of startups in 2022 because they ran out of cash. Extremely high burn rates, insufficient funding, poor financial planning, and overhead expenses — these things can cripple even the most brilliant companies. shut down Before joining DeckRobot, I had spent more than 5 years working in fast-growing startups, overseeing their finances and operations. This experience has led me to a conclusion: All startups tend to undermine themselves in a similar way. So I've developed basic rules to help reduce recurring expenses and fine-tune their operations. Know and track your key metrics Analyzing financial data from the very beginning provides a clear picture of a startup's financial health; it helps identify areas of high and unnecessary costs. The three key metrics to analyze from the beginning to pinpoint areas of improvement are CAC, CLV, and Gross Margin. It is the cost a business incurs to acquire a new customer. The formula for calculating it: CAC = Total Sales and Marketing Costs / Number of New Customers Acquired. Customer Acquisition Cost (CAC). If you spend $200,000 per month on advertisements that bring in 1,000 clients, your CAC amounts to $20. The definition of a "good" CAC can vary depending on industry standards, business objectives, and the competitive environment. For reference, the benchmark CAC for e-commerce is , for B2B SaaS it is $239, and for real estate, it stands at $791. $86 To determine whether this is an acceptable or unfavorable CAC, you'd need to consider other factors, including the expected CLV for a typical customer. This metric represents an estimated amount of revenue a business can generate from a single customer. Customer Lifetime Value (CLV or LTV). Its formula: CLV = (Average Purchase Value x Average Purchase Frequency) x Average Customer Lifespan. It’s considered healthy when CLV exceeds CAC. If it doesn’t, consider re-evaluating your marketing strategy. It's the percentage of revenue remaining after subtracting the cost of goods or services sold. Its formula: Gross Margin = (Total Revenue - Cost of Sales) / Total Revenue. Gross Margin. Imagine you are running a SaaS company and you want to calculate your Gross Margin. Let’s say, your total subscription revenue is $300,000. Your expenses: Hosting and maintenance costs ($60,000), customer support costs ($30,000), other expenses ($10,000). In this example, Gross Margin for the SaaS business is 66.67%. This means that, after accounting for the direct costs and other relevant operating expenses, you retain approximately 66.67% of your total SaaS subscription revenue as a gross profit at this stage. A healthy gross margin is 50-70%. Boost efficiency at your workplace Start by outlining your critical business processes and identifying bottlenecks — points where the workflow is slowed down. For instance, a developer might be overloaded with tasks, leading to missed deadlines and a decline in work quality. In such instances, introducing a part-time developer could prove beneficial. Another bottleneck example: Your employees are manually doing work that could be automated. Collecting sales and marketing data, creating email campaigns, and customer support –– these are time-consuming tasks that often can be automated. A recent suggests that automation can reduce business costs by 50-60%. study How to automate startup processes: Organize and track issues. Use JIRA, Asana, Trello, or similar services to monitor your team's performance and prevent chaos. Set up continuous integration (CI). Use Jenkins, AWS CodePipeline, CircleCI, or other CI systems that can detect issues early in the code development process, preventing costly bug fixes later on. Keep track of your finances. Monitor your income and expenses and gain insights with QuickBooks, FreshBooks, Wave, Xero, Zoho Books, or similar tools. Optimize marketing and sales. Consider using Salesforce, HubSpot, GrowBots, and Zapier to improve your marketing and sales efforts. Eliminate unnecessary expenses Every penny counts for a startup. You should regularly review your expenses and cut excessive spending. Here's what you can do to reduce costs. Labor costs consume up to of a startup's expenses. At the initial stage, most of the tasks can be managed by three employees: CEO, CTO, and CMO. Rethink your hiring strategy. 70% Early-stage startups spend of the planned annual revenue on marketing. That’s a lot, so be careful to allocate these resources wisely. Monitor the performance of your ad campaigns and confirm they yield results. Review your marketing expenses. 10% You can save a significant portion of your budget if you don’t rent a space and pay utility bills. If you need to meet in person, consider booking a meeting room at a coworking space for a few days a week, as it's a more cost-effective option. Or simply decide which team should work in person and arrange a space for the specific group. Give up your office or make it hybrid. . Maybe you don’t need a Zoom subscription if you are already paying for Slack (or just use Google Meet, which is free). See if there’s a way to use free versions of services or a single tool for various purposes. You don’t need that many subscriptions . In 95% of instances , your negotiations won't be significantly more successful just because you spend thousands of dollars on a flight, accommodations, and a fancy restaurant dinner. Choose calls over business trips (made it up, but you get the point) Start using cloud computing, analytics tools Instead of investing in expensive hardware to store your data, use cloud computing. Since these services operate on a pay-as-you-go or subscription model, you won't be charged for resources you don’t use. This could save you around of your startup’s budget. 20% Investing in data analytics tools is also beneficial for your business's finances. Poor-quality data causes of marketers to waste budget funds, while a lack of supply chain visibility poses the biggest challenge for of chief supply chain officers. Use analytics to interpret your metrics effectively, make data-driven decisions, and reduce costs. 37% 84% Initially, a simple Excel table can suffice for calculating and visualizing data. But when your startup grows and there’s more data, move to more sophisticated tools. It can be Microsoft Power BI, SAS, Apache Spark, Tableau, or Knime. A $9.99/month subscription to these services is never a waste of money.

Simple Steps to Improve Your Startup's Unit Economics and Reduce Costs

আপনার স্টার্টআপের ইউনিট অর্থনীতির উন্নতি এবং খরচ কমানোর সহজ পদক্ষেপ

Einfache Schritte zur Verbesserung der Stückökonomie Ihres Startups und zur Kostensenkung

Pasos sencillos para mejorar la economía unitaria de su startup y reducir los costos

Étapes simples pour améliorer l'économie de l'unité de votre startup et réduire les coûts

आपके स्टार्टअप की इकाई अर्थव्यवस्था में सुधार और लागत कम करने के लिए सरल कदम

スタートアップのユニットエコノミクスを改善し、コストを削減するための簡単な手順

스타트업의 단위 경제성을 개선하고 비용을 절감하는 간단한 단계

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Простые шаги для улучшения юнит-экономики вашего стартапа и сокращения затрат

Girişiminizin Birim Ekonomisini İyileştirmek ve Maliyetleri Azaltmak için Basit Adımlar

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Introduction How many times have you heard the word "AI" in 2023? There is a probability that you must have heard it too many times, especially with the popularity of ChatGPT. AI, which is an acronym for Artificial Intelligence, is a fast-growing phenomenon in today's world. According to statistics, . From education and health to entertainment and business, AI is reshaping our environment and our way of life. the global AI market is valued at over $136 billion and is projected to increase by over 13x over the next seven years At the same time, it is opening up new possibilities and posing new risks to the continued growth, welfare, and survival of mankind. In the modern world, AI is a rapidly expanding phenomenon. Statistics show that the global AI market is already worth over and is expected to grow by more than 13 times over the following seven years. $136 billion AI’s Basic Working Principle… Artificial intelligence is basically the capacity of a digital computer or computer-controlled robot to carry out actions frequently performed by intelligent individuals. Systems and tools that are built with AI can recognize, combine, and deduce information from a variety of data sources, including audio, photos, videos, text, and more, and they can also learn from this information. They can learn from prior experiences, generalize, use reasoning, find meaning, and adapt to new circumstances, which helps them become more intelligent. AI Tools Across 11 Different Sectors in the Modern World Most industries throughout the world—if not all—use AI tools and technology in some capacity. Although it mostly has some beneficial effects, there is concern that given how quickly AI is expanding and improving, and it may have the ability to eliminate many occupations. How accurate is that? But first, let's examine some of the well-known industries that have begun making major use of AI to make this determination. 1. Finance There are powerful AI tools that are used by a variety of financial organizations and even individuals in the areas of financial planning and analysis, assessing credit risk, fraud detection, trading, and investments, optimizing insurance claims processing, sales, and customer services, and digitizing historical files and documents, amongst other applications in the field of finance. Some of the tools include: A virtual assistant tool in the Bank of America mobile app that assists users with a variety of banking operations like checking balances, paying bills, and sending money, as well as balance reconciliation. Capital One also has a similar virtual assistant called  Eno, which notifies users of suspected fraud, price increases, or payment reminders. Erica: Enova uses AI technology to aid users have access to credit facilities and helps financial institutions to solve the challenges surrounding borrowers’ assessment through the provision of sound financial analytics. : Enova An AI-powered platform using data points to provide financial institutions with the data to conduct assessments on borrowers with little or no credit information : Zest : A technology that makes use of artificial intelligence to accurately process financial records like pay stubs, tax returns, and other related ones at a faster speed. Ocrolus provides a predictive model that helps financial institutions solve issues surrounding marketing, wealth management, fraudulent credit card transactions, underwriting, and business growth. : DataRobot an identity verification system that uses ML and AI to analyze and verify applicants’ data to ensure that the KYC details are accurate. : Socure 2. Blockchain Several techniques, including strengthening data security and privacy, improving data quality and trust, optimizing data processing and storage, creating data insights and value, integrating data across platforms and domains, etc., are employed by AI to reinvent and optimize the blockchain sector. Some instances include: - using AI and formal verification to authenticate the security of a blockchain, smart contracts, and web3 dApps. CertiK - which certifies and verifies valuable pieces of art and NFTs on the blockchain using AI. Verisart - which leverages AI to allow quicker transactions, more scalability, and less energy use. Matrix AI - employing artificial intelligence to examine cryptocurrency market movements and offer market insights for individual investors. Token Metrics - which employs AI to build a decentralized network of AI services that can communicate with one another and trade value on the blockchain. SingularityNET 3. Transport AI is revolutionizing transportation and supplying driving assistance. Some of the tools include: This company uses AI to power its autonomous vehicles and taxis for urban transportation. : Waymo Rapid Flow Technologies uses traffic management to deliver adaptive traffic signal control systems that lower congestion and pollution. This is done by analyzing traffic patterns, predicting traffic flow, and optimizing traffic lights using data and algorithms. : Rapid Flow Technologies Using AI, Uber apps are designed to find compatible drivers and passengers, calculate prices and arrival times, and offer safety features. These enhance passenger transportation services by personalizing travel options, streamlining pricing and scheduling, and enhancing customer satisfaction. : Uber Clear uses AI to build biometric identification verification services that let users use their faces or eyes as boarding credentials in airports.  This expedites airport security and boarding procedures in the aviation industry. : Clear With AI, Volocopter has been able to build its drone Taxis which will safely traverse the airspace, avoid obstructions, and land at designated places within a busy urban city. It offers a faster transportation system through congested streets by flying in the lower airspace with zero-emission. : Volocopter 4. Data Analysis One of the key industries using AI and machine learning is data analysis, which does so in a variety of ways, such as by analyzing unstructured data, producing data insights, streamlining data processing, integrating data across platforms and domains, and others. A few of the numerous AI tools that are used in data analysis include: A software program that forecasts data performance using artificial intelligence and suggests subjects based on prediction scores. : Tableau An AI tool designed by Microsoft for Data Analytics, Data Visualization, and Business Intelligence : Power BI A platform to analyze user data and offer insights and recommendations : Research AI An open-source tool to build customized data pipelines, workflow, and solutions for a range of data analysis task : KNIME An AI tool for analyzing and indexing a huge volume of data in real-time : Splunk 5. Software Engineering The coding and design industry is being reinvented and optimized by AI in several ways. Currently, programmers can generate, edit, optimize, check, and transform codes with the use of artificial intelligence (AI). Some of the AI tools available at their disposal include: A tool called , which can create code in common development environments, is , which is based on the GPT-3 standard. GitHub Copilot powered by the OpenAI Codex model A program called ** **utilizes artificial intelligence to examine code and identify flaws that would prevent it from running. Embold This tool is an app that provides programmers with comprehensive facilities to build, edit, and test codes and applications. Examples of popular IDEs include VS code, Eclipse, IntelliJ IDEA, PyCharm, Xcode, Android studio, NetBeans, Atom, Sublime Text, and Vim. The integrated development environment (IDE): An auto-completion tool that can be integrated into IDEs to improve productivity. This tool employs deep learning algorithms to predict the rest of the code a programmer is typing. : Deep TabNine : This AI-powered coding assistant using natural language, can generate codes, complete the function of the codes, and generate the summary of a function. CodeT5 Other AI tools that aid programmers include Google Bard, ChatGPT, AskCodi, Mutable, IBM Watson Code Assistant, Code-LMS, WPCode, and Amazon CrowdWhisperer. 6. Entertainment Although technically speaking, humans make up the majority of the characters, artists, and actors in the entertainment industry, artificial intelligence (AI) has begun to play crucial roles in the sector and is used in significant areas like generating, analyzing, optimizing, moderating, and personalizing realistic content for their audiences by using data and algorithms. Some platforms for entertainment that use AI are: aids artists in writing the lyrics of a song. It can write songs in over 100 languages. Lyrical Labs offers video analytics and insights across a variety of platforms, including YouTube, Facebook, Instagram, and TikTok. Tubular Labs is an AI-powered platform using machine learning algorithms to match licensed music with visual content. SyncFloor This is an AI video creation tool that can convert text data to video content. Lumen5: Other global organizations that inculcated AI into their products are Netflix__,__ which uses AI to adapt its streaming quality and bandwidth use to the customers' devices and networks; YouTube, which employs AI to search and eliminate content that breaks its community standards; and Spotify which utilizes AI for curating customized podcasts and playlists for its subscribers depending on their listening patterns. 7. Manufacturing This industry makes use of AI in several areas, including boosting predictive maintenance, streamlining manufacturing procedures, automating production duties, fostering production innovation, and more. The following are some instances of production platforms using AI in their operations: offers AI-based visual inspection solutions for a range of industries, including textiles, electronics, and the automobile. Landing AI for a variety of industries, including energy, mining, and transportation. Uptake leverages AI to offer predictive maintenance solutions. Uptake __c__an give solutions for the optimization of processes in a variety of industries, including the chemical, steel, and cement industries, by utilizing AI. __ m __ Petuu offers robotic picking solutions for a range of industries, including e-commerce, shipping, and the food industry. Covariant For generative design solutions across a range of industries, including architecture, engineering, and construction, leverages artificial intelligence (AI). Autodesk 8. Gaming AI may be used to create intelligent, human-like NPCs that communicate with players, forecast player behavior to improve game design and testing, provide lifelike images and audio, improve gameplay, and increase immersion, among other things. By using data and algorithms, it is also utilized to create, analyze, optimize, moderate, and personalize realistic material for their games. Several instances include: AI is used by to deliver statistics and insights for games across a range of platforms, including PC, mobile, and console. Unity AI is used by to recommend the best times to release mobile games as well as their prices and marketing techniques. Google Play In multiplayer games, employs AI to detect cheaters and block them. Steam 9. Healthcare AI activities in the healthcare industry are increasing by the day. Using systems like Natural Language Processing, Machine Learning, and deep learning algorithms, its impact is greatly felt, especially in domains such as; illness detection and patient care improvement, forecasting and epidemic prevention, medication discovery and development, personalizing and optimizing therapies, and much more. Some notable instances of AI in healthcare  include: The use of AI by to help oncologists diagnose and treat cancer. IBM Watson Health AI-powered tool to track and predict infectious disease outbreaks globally. BlueDot tool used to organize searches across billions of chemicals for possible drugs to speed up drug development. Atomwise Provision of individualized treatment plans by to provide patients with chronic diseases. Wellframe The Future In the years and decades to come, AI will become the centrepiece of innovation in the world, ushering in growth and influencing the world in ways that pave the way for enormous possibilities as well as threats to humanity's progress, prosperity, and the very existence. Possible outcomes may include: More individuals and businesses making use of AI's capabilities and applications as it becomes more widely available and democratized. It is expected that its interaction and synergy with other technologies like cloud computing, 5G, IoT, blockchain, and quantum computing will birth new realities. Advancement to the point where it can generate its content, goods, and services without human intervention. This advancement may accelerate to the level where it will be able to learn from its own experiences and those of other AIs, leading to novel kinds of intelligence and cooperation. A greater influence, transformation, and disruption across many fields and businesses. In conclusion, the development of AI has an inevitable future. It all comes down to how researchers plan, build, implement, and regulate AI systems. How well we adjust to the new opportunities and threats that AI presents will tell how deeply AI will twist our realities. To create the kind of future we want for artificial intelligence, we must be proactive, collaborative, and inclusive.

AI Tools That Are Reshaping The Modern World Industries

There are four main data structures: structured data, unstructured data, semi-structured data, and metadata. Structured data is information that is formatted and organized for readability within relational databases. On the other hand, unstructured data’s format is undefined, not well-organized, and not usable by relational databases. In this article, I will explore structured vs. unstructured data, semi-structured data, how to convert unstructured data, and AI’s impact on data management solutions. What is structured data? Structured data is highly organized data formatted to integrate with a relational database easily. Structured data resides in tabular formats, such as SQL databases. As I mentioned previously, structured data is typically collected from spreadsheets and DBMS systems and is primarily, but not always, quantitative in nature. Because structured data complies with relational databases, it also easily integrates with AI technology, specifically machine learning. While structured data is widely regarded as the most sought-after and user-friendly data type, structured data is the least common data structure type. This is because structured data is collected from spreadsheets, relational database management systems (RDBMS), etc. If we were to think about where most of our data is collected, we would notice that public web sources and other more informal collection methods are the most common, even in the world of finance. However, while structured data accounts for only about , it is still extremely valuable, particularly to companies and investors. For example, secondary data usually falls under the 20% of cleaned and structured data. 20% of total global data Let’s take a closer look at structured data. How is structured data managed? The management of structured data was created by . IBM needed to develop a language that defined how each data point within a larger database relates to one another, creating the Structured Query Language, known more widely as SQL. IBM in the 1970s This language helped companies replace outdated paper-based business intelligence processes with digital processes. This digitalization has since enhanced data analysis and improved business operations by reducing costs and improving efficiency, among other benefits. Today we are seeing companies leverage the digitalization of such structured data for building AI-based tools and data-driven decision-making. “Structured data is well-organized and formatted, making it easy to find in relational databases. Unstructured data is difficult to capture, process, and evaluate since it lacks a predefined format or organization.” - Jason Mitchell, CTO of Smart Billions What is unstructured data? Unstructured data is not organized or formatted in a predefined data model. It is stored as media files or NoSQL databases. Typically qualitative in nature, unstructured data includes a variety of data types such as text, numbers, booleans, and enumerations. Because unstructured data is collected from a variety of sources, raw unstructured data in its unfiltered form is disorganized and complex. Therefore, it’s harder to analyze unstructured data. Despite its complexity, unstructured data, when integrated, organized, and analyzed properly, provides companies with high-quality qualitative data and business insights. This then raises the question, if unstructured data is so complicated, why is it so sought after? The answer is simple: unstructured data accounts for approximately 80% of data worldwide. More specifically, unstructured data is sourced from a variety of sources, including social media, documents, PDFs, audio files, video files, sensor data, and more. The variety of source types and loose formatting definitions require unstructured data to be processed and transformed for further analysis or integration. How is unstructured data managed? As I mentioned before, unstructured data accounts for the majority of data while also being the most complex, unorganized, and typically largest in size. However, because it is so valuable, data scientists have been looking for unstructured data management and storage solutions. Today, AI technology, automation, and cloud technology have been the key to such solutions. More specifically, unstructured data is stored using servers and cloud-based technology and is managed with natural language processing and data mining. Still, some companies refuse to manage unstructured data. They simply expand the storage space and fill it with unstructured data. However, unstructured data requires management in order to save storage space and have the ability to ingest other types of data. What is semi-structured data? Semi-structured data is a combination of structured and unstructured data. Semi-structured data has some organization and formatting but not enough to integrate into relational databases smoothly. Semi-structured data’s organization is minimal and is organized with tags, attributes, and other semantic markers. However, the resulting organization does not meet the standard of a relational database but can be altered in a way that can fit into easy-to-read tables and spreadsheets, making it a subset of unstructured data. How is semi-structured data managed? Similar to how unstructured data is managed and stored, semi-structured data is managed using servers, cloud-based technology, natural language processing, and text data mining. Ultimately, as we create more and more unstructured and semi-structured data, we will begin to see new trends in data management solutions. Structured vs. unstructured data: key differences Let’s take a closer look at the key differences and similarities between structured and unstructured data. Converting unstructured data As our global data sphere continues to grow, businesses will look for solutions to unlock the full potential of all data structures. Currently, data scientists can extract insights from unstructured and semi-structured data using a conversion process. Let’s take a quick look at this process. How to convert unstructured data 1. Analyze your data sources Before you begin the conversion process, you must identify and analyze what data you want to convert. This will require that you access a data lake, where raw unstructured data is stored, and decide what datasets to pull from. 2. Set clear goals and objectives This step can be done in tandem with the first step. Setting clear goals and objectives about what you want to extract from your data and how you might use and access it in the future will provide guidance for the rest of the process. 3. Evaluate and select the processing tools Once there are clear objectives, and your data sources have been analyzed, you can select which processing tools will work best. These processing tools include text mining, data extraction tools, and natural language processing tools. 4. Clear and tag the data Now that you have chosen the processing tool, you must clean and formulate the data according to the chosen tool’s guidelines. This might involve deleting extraneous symbols, whitespaces, and duplicate data. This step will help your processing tools understand the basic organization and entities within your data. 5. Data extraction: Text mining & Natural language processing (NLP) The final step involves running your data through the selected processing tool: text mining or NLP. Text mining involves sifting through textual data for significant words and phrases and extracting key features of each document or data file. NLP involves utilizing AI to sift through and decipher the natural languages (human languages) within textual data. Data structures and AI Our global data footprint is expanding at an exponential rate. According to a study by Seagate, the global data sphere is expected to reach , a massive growth from 45 zettabytes reported in 2019. 175 zettabytes by 2025 As our data footprint grows, companies are finding more ways to leverage this data for crucial business practices such as lead generation, market analysis, and business and investment intelligence, just to name a few. Likewise, as our data grows, the challenges surrounding the usability and hidden potential of data have become apparent. This is primarily due to the varying structures of data that are created and consequently collected. Ultimately, data scientists have turned to AI and machine learning for these solutions. Further, artificial intelligence utilizes both structured and unstructured data. More specifically, structured data utilizes machine learning, while unstructured data utilizes text mining and natural language processing (NLP), all of which are AI-based processes. Scientists have created AI-based tools that can extract valuable business insights from nearly all data structures. For example, companies can purchase semi-structured data from data providers, prepare said data for analysis or enrich their current datasets, and then extract valuable insights from the data with AI, analysis, or other automated data processing tools. “Structured data is utilized in machine learning and drives machine learning algorithms. Unstructured data is used in text mining and natural language processing.” - Veronica Miller, Cybersecurity Expert at VPN overview Top tools for structured and unstructured data analysis Structured data tools Best for content marketing teams that have little to no technical knowledge. Schema. It tests whether your URL or a code snippet has any issues. It’s very useful while installing schema into your website. Google’s Structured Data Testing Tool. It helps create data for articles, FAQs, breadcrumbs, and more. It also offers features such as SEO, SERP, and crawling, among others. Merkle. It also assists in creating data for articles, FAQs, and more. The RankRanger Structured Data Tool. Unstructured data tools An easy-to-use tool that allows for basic quantitative data analysis and visualization. Excel. A data visualization tool that allows making comprehensive data visuals for reporting. Tableu. An advanced data science platform that is capable of building predictive models and is compatible with large amounts of data. RapidMinder. An open-source data platform best for experienced IT professionals that are interested in creating specialized tools. KNIME. An advanced data visualization tool for business intelligence. Power BI. Wrapping up In all, understanding the various data structures is beneficial in informing companies about the importance of data management. In order to unlock the full potential of data, companies must maximize their data management and storage processes of both structured and unstructured data. As our global data sphere continues to grow at exponential rates, data scientists and companies will continue to develop management and storage for all types of data structures. Also published . here

Understanding the Main Differences between Structured and Unstructured Data

Exploring the Top Data Science and Machine Learning (DSML) Platforms of 2022 

Data science has been democratized for the most part. AI is now mainstream! Data science is no longer the exclusive province of large companies with deep pockets. AI has finally made its way into our everyday lives, and it’s only a matter of time before you start seeing it becoming more widely used across all industries, not just finance! With that came a new breed of data scientists, citizen data scientists. Who is a citizen data scientist? A citizen data scientist is a person who works in fields yet creates or generates models that incorporate predictive or prescriptive analytics. other than statistics and analytics Citizen data scientists are found in virtually every industry, from healthcare to retail. Related: Data Science Will Be Democratized (In Less Than 10 Years.) In the past, many would have been called business analysts or knowledge workers because they rely on their insight and understanding to make decisions. In today’s world, they use predictive . They may not implement all analytic techniques, but they can tell if the results make sense. analytics as part of their day job Why is this important? Organizations are increasingly using analytics to optimize every aspect of their businesses, from pricing decisions to hiring strategies. However, many organizations find that IT isn’t keeping up with the demand to incorporate analytics into day-to-day operations. Citizen data scientists fill that gap. Citizen data scientists do not need to be trained in analytics or mathematics, nor do they have to have deep IT knowledge about the used systems. They have to know what results are reasonable and can ask for help when needed. What are the roles and responsibilities of a citizen data scientist? The job usually don’t include dealing with data. They are mainstream workers who leverage the power of predictive analytics to make better decisions that affect their job performance, such as product placements or customer targeting. responsibilities of a citizen data scientist Citizen data scientists are usually not expected to build elaborate models because they work with other people’s data. Instead, they evaluate results and interpret them for managers or other non-technical staff members. Often citizen data scientists work in isolation with limited opportunities to compare notes or share best practices. Collaborating with peers is an excellent way to learn new techniques and gain further insights. What is the best approach to becoming a citizen data scientist? You don’t need to be an expert in statistics or analytics. Still, you should have some exposure to techniques such as regression analysis if you want to become a citizen data scientist. However, it is more vital that you understand the business applications of predictive analytics. For example, linear regression might not be the best technique for your application, but you should be able to tell when a model is overfitting. It also helps if you have some programming ability, but it isn’t necessary. You don’t need to know how to program in R or Python, but being familiar with SQL and Excel will help. For example, writing a joint statement in SQL can be crucial in implementing a successful model. How to become a citizen data scientist? Here’s a 5 step approach. Improve your data literacy Data literacy is the ability to understand and communicate analytical techniques and their insights. In addition to interpreting data and drawing insights, a good data literate person asks the right questions to creatively solve the problem. The best way to improve data literacy is to collaborate with a data scientist or analyst. Platforms such as Kaggle make it possible. On the other hand, you could also try a good data literacy online course. Here’s a list of programs you could try: on LinkedIn learning; Data Fluency: Exploring and Describing Data from Tableau eLearning; Data Literacy for All , and; Healthcare Data Literacy on Coursera by edX. Data Literacy Foundations Pick the right tools for the job. To be a successful citizen data scientist, you need to learn the basics of data scientists’ tools. Basic programming skills are important as they can help transform unstructured or unorganized data into structured and coherent datasets. But having a good understanding of no-code platforms such as , , and not to mention, is essential. Tableau KNIME Excel is one of the most used tools in data visualization. You can connect your data sets with Tableau and use its drag-and-drop interface to create interactive visualizations. It’s also possible to start with predefined templates or data sets. Tableau is another open-source platform that allows users to create processes, explore, and mine information using various tools. It offers numerous modules for data querying, transformation, analysis of big data sets on distributed architectures. KNIME Related: How to Become a Terrific Data Scientist (+Engineer) Without Coding is best known as the king of spreadsheets. It offers powerful features for data wrangling, and most people find it easy to use. It is an excellent tool for beginners in data science. Excel A good understanding of is also essential. While various tools offer wizards for beginners, it is necessary to understand its basics to query data sets effectively. SQL Find data science projects to practice. It is a good idea to practice your data science skills on actual data. There are various platforms where you can find exciting datasets for research and experimentation. Try analyzing the same data using different tools and compare the insights provided by each of them. Most enterprise data science problems are already solved and easy to replicate. You can find real data as well as their solution. There are also various data science virtual competitions in which you can participate. For instance, provides a platform for data scientists to compete in challenges with real-world datasets and prize money. Kaggle Ask for data access and replicate your success. Once you have practiced sufficiently with real-world projects, it’s easy to approach company executives and ask for data. Applying what you have learned by participating in challenges or practicing on projects will help you communicate the knowledge you have gained. Before pitching to higher-ups, it’s important to do your research. Get familiar with company data and know how much access the company employees currently have. That way, you can justify why they need your skills! Master the art of “Data Storytelling.” To become a successful citizen data scientist, you needn’t have advanced degrees in Math or Computer Science. Instead, pick up an analytical mind and learn to create compelling stories with data. Being a citizen data scientist requires more than technical skills; it means understanding the business problem and finding insights that others can use in the organization. The best way to do this is to speak to people and find out what issues they need solving and how they want these results presented. To create a great data story, there are five essential components to focus on: Your audience — Who will you be talking to? What do they know, or what don’t they know? The happenings — What is the purpose of your communication, what happened in reality? Anecdotes — What context do you give the audience to help them better understand what happened? Analysis — How can your insights be explained using simple terms? Conclusions — How does this impact the audience? What should they take away from this conversation? Final Thoughts As AI becomes more and more mainstream in the enterprise, non-technical people are playing a more prominent role than ever before. Citizen data scientists have emerged to help bridge the gap between IT professionals who may not be as skilled with algorithms or machine learning techniques vs. business stakeholders that know how to identify insights from large datasets but lack technical skills. This article has discussed where you can start, what tools and techniques you should learn, and how to pitch your results when presenting them back to executives. What is your opinion on citizen data scientists? Let me know in the comments. Thanks for reading, say Hi to me on , , and . LinkedIn Twitter Medium Please share it in your network if you find this helpful. Also published behind a paywall on: https://towardsdatascience.com/how-to-become-a-citizen-data-scientist-294660da0494

What is a Citizen Data Scientist and How Do You Become One?

Data engirds the entire world. Data is evolving just like any other thing on this globe. Being a part of this tech-oriented world, today we human beings create as much information in just 2 days as we did since the beginning of time till 2003. Amazed? Well, there’s more. The number of data industries store and capture magnifies every 1.2 years. Nonetheless, in this modern age of technological innovations and computational advancements, we upload 200 thousand photos on Facebook, generate 278 thousand tweets on Twitter,  1.8 million Facebook likes, and send 204 million emails every second! Facebook users share pieces of content among them each day. Talking of Google, alone it processes approximately 40,000 search queries every second, making it more than 3.5 billion in a single day. The data centers of this era occupy an area of land equivalent to the size of almost 6000 football fields. Hence, data evolution is unpredictable. 30 billion Do you know that bad data can cost an organization up to of its revenue? Astonishing isn’t it? But the question arises - how to dodge it? How to process that vast amount of data? How to clean it? Analyze it? How to form connections, patterns, trends, and correlations out of it? Here’s when big data technologies get developers’ and IT experts’ back. 20% Recently, big data has been on the tip of the tongue of almost everyone, paving the way from hype to mainstream. Undoubtedly, efficient and accurate data management for enterprises is crucial to stay competitive in this tech-driven era. Thanks to the emergence of revolutionary artificial intelligence and innovative machine learning algorithms an essential sub-field called Big Data can come into existence. From healthcare to manufacturing, from retail to the entertainment industry, big data is everywhere. Big data helps IT experts deal with several sets of complex real-time data analytics. Big data is defined by its qualities, also called 4 V’s - Veracity, Variety, Velocity, and Volume. Installation of big data technologies in the computer systems of developers and IT experts help to transform data into business insights. Moreover, big data technologies are categorized into of efficient utilization of data analytics, data mining, data visualization, and data storage 4 major fields Below is the list of the 10 most evolving big data technologies emerging prominently in 2022 and upcoming years. So without further ado, let’s glide right into it. Elasticsearch is a free open search distributed analytics engine. It includes structured, unstructured, geospatial, numerical, and textual types of data. It is built on Apache Lucene, known for its scalability, speed, REST APIs, and distributed nature. Elasticsearch Language Support Elasticsearch supports the following programming languages: Ruby Python Perl PHP .NET (C#) Go Java Javascript (Node.js) Hadoop is a very popular open-source framework or data platform which was developed and deployed in Java. The purpose of Hadoop is to store, analyze, and process vast sets of unstructured data. Cutting-edge big data technologies engirdled the world with the data splitting from digital media. However, Apache Hadoop was one of those inventions that exhibited this wave of modernization. Hadoop Language Support Hadoop supports several programming languages. Some of them are as follow: R PHP C++ Python Perl MongoDB is a distributed document-oriented database. It aims to facilitate the data management of structured, semi-structured, or unstructured data in real-time for application developers. It also helps to store data in documents similar to JSON to allow dynamic and flexible schemas. It provides a dominant query language for indexing, ad hoc queries, graph search, text search, geo-based search, aggregation, and many other facilities. MongoDB Language Support MongoDB supports a broad range of popular programming languages. Here are a few of them: Erlang Go Scala Ruby Python PHP Perl Node.js Java C# C C++ Tableau A robust big data technology, can be connected to numerous open-source databases. It provides free public options to create a proper visualization. The platform offers several amazing features such as integration with over 250 applications, assistance to solve real-time big data analytics issues, moderate speed to improve extensive operation, and more. Tableau Language Support Tableau SDK can be implemented using any of the following languages: Python 2 Java C C++ Cassandra is a reliable, robust, free, and open-source wide column store distributed NoSQL database management system. It is designed to handle an extensive amount of data across several commodity servers, providing high availability and scalability with not even a single chance of risk or failure. Apache Cassandra Language Support Cassandra supports Cassandra query language (SQL) to communicate with Cassandra Apache database. RapidMiner The top-notch big data platform, , delivers transformational business insights to several industries. It plays a pivotal role in upskilling organizations’ extensibility and portability. RapidMiner is popular among researchers and non-programmers because of its compatibility with Flask, NodeJS, Android, iOS, and more. RapidMiner Language Support RapidMiner Studio currently supports The following languages: English Japanese Qlik offers efficient, raw, and transparent data association aligned automatically with data association. Integration of predictive and embedded analysis assists data analysts to identify potential market trends. Moreover, it helps to distinguish better in-depth insights for better workflow. Qlik Language Support Qlik Sense currently supports the following languages: Brazilian Portuguese Traditional Chinese Simplified Chinese Japanese Korean German Russian Italian French Dutch Turkish Polish Swedish Spanish English KNIME Konstanz Information Miner or is an open-source and free reporting, data analytics, and integration platform. KNIME integrates several components for data mining and machine learning via its modular data pipelining “Lego of analytics” concept. KNIME Language Used KNIME is written in Java. KNIME is based on Eclipse. Splunk transforms a tremendous amount of machine-generated data into times series events to answer operational and business questions in real-time. Splunk’s Search Processing Language (SPL) is at the core of the Splunk platform. The immense capabilities of SPL empower everyone to ask any question regarding any machine data. Splunk enterprise consists of two major services: Splunk Web Services(splunkweb) and Splunk Daemon(splunkd). The Splunk platform Language Used Splunk Web Services: XML, Python, AJAX Splunk Daemon: C++ R is a programming language and an ecosystem used for statistical graphics and computing. It is a GNU project just like the S programming language and environment.  R provides a broad range of statistical techniques including clustering, classification, time series analysis, classical statistical tests, linear modeling, nonlinear modeling, and more. It also provides highly extensible graphical techniques. Its strength which makes it stand out is the ease of producing well-designed publication-quality plots including mathematical formulas and symbols. R Stay Informed of What’s Coming Up! Consequently, big data is evolving and will continue to evolve with more applications and acquisitions of existing big data technologies and new solutions associated with data mining, cloud integration, big data security, and more. The general manager and vice president at Intel, Wei Li, that claimed “Big data and its associated buzz words such as artificial intelligence, machine learning, and deep learning are becoming more sophisticated over time. We are yet to see more potential beyond retail trend analyses, fraud detection devices, and self-driving cars.” Another prediction regarding big data is the acceleration of “actional data” or “fast data”. Unlike big data that typically relies on NoSQL databases and Hadoop, fast data processes real-time streams to analyze data promptly. This brings more value to IT experts and developers to make important strategic decisions when data arrives. According to a prediction by IDC, approximately of the world’s data will be utilized in real-time by the year 2025. Moreover, organizations will make the information more accurate, actionable, and standardized by processing data through analytical platforms. 30% At the heart of it all, big data also has a dark side. Several tech giants are facing heat from the public and government regarding the issue of data privacy. Laws that govern people’s right to their data will result in restricted albeit honest data collection. Likewise, the rapid growth in online data exposing us to cyberattacks every second day will amplify the significance of cybersecurity in the approaching years.

10 Most Evolving Big Data Technologies to Catch Up on in 2022

Companies across every industry rely on big data to make strategic decisions about their business, which is why data analyst roles are constantly in demand. Even as we transition to more automated data collection systems, data analysts remain a crucial piece in the data puzzle. Not only do they build the systems that extract and organize data, but they also make sense of it –– identifying patterns, trends, and formulating actionable insights. If you think that an , you might be wondering what to focus on in the first 90 days on the job. What skills should you have going in and what should you focus on developing in order to advance in this career path? entry-level data analyst role might be right for you Let’s take a look at the most important things you need to know. 1. Develop the Most Important Hard Skill: SQL is a free, open-source programming language and arguably the most important language for data analysts to learn. Luckily, it’s also the easiest to grasp, especially if you’re new to programming since it utilizes English words. SQL SQL is crucial for assessing and manipulating data and will give you a solid understanding of relational databases. Not only will SQL help you interpret data, but it will also prepare you for learning more complex programming languages down the line. SQL also integrates with many database management systems like MySQL, Microsoft SQL Server, and Oracle Database, to name a few. Check out to make sure you’re familiar with this language before your first day on the job. Udacity’s free SQL for Data Analysis course 2. Question Everything More than any technical requirement, having an inquisitive nature is vital to succeeding as an entry-level data analyst and beyond. A significant component of data analysis is noticing when things seem out of the ordinary or need further investigation. Asking questions, wondering why, and digging deeper is how you find incredibly valuable insights. “You have to be curious and inquisitive, and enjoy not knowing how to solve a problem, not knowing the answer, and working through that to get to a usable solution or usable actionable answer,” says , a lead data scientist at URSA Inc., a data analytics and visualization company. Matthew May Demonstrating this skill in your first 90 days of the job will help you stand out and make your value abundantly clear. Always be on the lookout for data that doesn’t make sense or seems off. Identifying any problems with data before presenting reports to stakeholders will ensure that you build trust with the company and solidify your skills. “Data science is constantly evolving and there will be new concepts and new algorithms to learn every year. A curious attitude is what I need. I need somebody who isn’t afraid of saying, ‘I don’t know. I’ll research it.’ says , the principal data scientist at . Dr. Rosaria Silipo KNIME 3. Be a Storyteller Producing reports is a big part of the day-to-day as an entry-level data analyst, but writing an effective report involves much more than just putting numbers on a page. Drawing meaningful conclusions and telling a story with data is where your value as a data analyst really comes to life. Questions like, “Why’s this trending up? What’s the reason for this anomaly? What can we do to mitigate a future incident?” will help direct you to valuable conclusions, according to , an online SQL editor for data analysts. Solita Creating a compelling narrative around these questions and answers makes data analysts better equipped to predict future outcomes. A well-constructed analysis also makes it easier to motivate people to get on board with certain business changes that you might suggest. 4. Have a Solid Understanding of Statistics & Math The role of a data analyst can be narrowed down to two main components: statistics and translating those statistics into digestible stories. However, you don’t need to be a master-level statistician or mathematician to excel. Understanding the basics of statistics and probability will help you tell a compelling story with the data. Statistical metrics, probability distributions, and statistical plots will all come in handy in your first 30, 60, and 90 days on the job. “Everything else — algorithms, more programming languages — you can learn,” says . “And since programming languages dedicated to AI change every few years, you’ll need to learn new ones throughout your career. However, the math skills you must have from the beginning, and they are the basis to understand and learn everything else.” Dr. Silipo If an entry-level data analyst role sounds exciting to you, and you’re ready to learn practical statistics, data wrangling, and data visualization, could be the perfect fit. In this program, you’ll refine your ability to work with complex data sets and learn the skills to tell a compelling story with data. Udacity’s Data Analyst Nanodegree program Then, you’ll have the knowledge, experience, and skills to succeed as an entry-level data analyst. When you’re ready to get hired, check out our post about the best ways to . get noticed and hired as a data analyst START LEARNING

4 Tips To Become A Successful Entry-Level Data Analyst

The world around us is changing at a rate that’s often incomprehensible to us. We’re seeing new inventions everywhere we look, and it’s happening every single day. 5G, AI, VR, - these and other inventions are constantly hitting our cognitive system, making it overwhelmed. satellite Wi-Fi This trend is visible in every field of our life, be it work, entertainment, or leisure. And among a vast selection of changing industries, there’s the marketing world that’s also undergoing some serious developments. Long gone are days when a simple spokesman in front of a big crowd would attract some potential customers. People have grown alongside the technology around them and they’ve become more demanding. That’s why marketing is also following that trend and keeping up with modern technological findings. Otherwise, it’s very easy to be left behind in this frenzy race. Leveraging comparative advantage with affiliation One of the most popular forms of marketing today is affiliate marketing. It’s been around for at least 20 solid years, which means it’s had its time to grow and mature. Unlike regular marketing strategies, affiliate marketing uses a partnership between a company and a marketing department to achieve an end-result - make a certain product or a service popular. In this method, the marketing departments take the producer’s service/product, generate online links, and put them in different places of their online content. The great thing about affiliate marketing is that it uses the comparative advantages of both partners to the fullest, making an end-result even more refined. And what’s also great is that affiliate marketing can be used by entertainment providers such as , health institutions like Health Plans of America, movie brands, and even governing bodies and their political websites. Its uses are virtually endless! PlayAmo casino affiliate program So, affiliate marketing is a technological achievement in itself. It uses online resources, websites, and links that generate traffic. But as technology moves forward, so do its “subjects”, including affiliate marketing. Automated affiliation as the future One of the most crucial aspects of affiliate marketing is engaging with customers. Affiliates make the best use of their technological arsenal to collect data about their customers, including what they like, which websites they visit, how they spend their money, etc. In short, cookies enable affiliates to get crucial data and then turn them into usable information. However, the mere data generation won’t cut it in today’s marketing world. Affiliate marketers need to have much deeper connections with their customers, to get more detailed feedback regarding the product/service quality, customer preferences, as well as changes that they’d make in the company. And while all of that can be done by humans, technology can ultimately do it much better and more effectively. There are many affiliate marketers that already use chatbots and AI with greater efficiency. They’re programmed to provide some basic, as well as comprehensive responses in the chatbox and if the customers’ questions become too complicated, there’s always a human service center to take matters into their hands. That’s where the opportunities for complete automation lie in future developments. One of the most popular and already sophisticated chatbots is developed by Facebook. Its and the automated customer service is already used by many providers all around the world. Affiliate marketers, considering how large of a consumer base Facebook has, should definitely leverage this tool to their advantage. Messenger platform AI to sort through the vast database Another important aspect of affiliate marketing is its dependence on user data. If businesses and marketers use the information correctly, they can get some crucial conclusions from their customer preferences that can ultimately improve the quality of their products and services. However, as the internet grows, so does the data generated on it. Almost around the world create online traffic that goes beyond the wildest expectations of the “founding fathers” of the internet. And this directly translates into terabytes of user data, what they search, which websites they visit, and so on. 4.5 billion people Therefore, sorting through this vast kingdom is an unbearable challenge for humans. Our brain might not be limited universally as such, but every individual has their own restrictions as to how much information they can consume. So, making an order from this mess isn’t an option for humans. That’s where AI and computer-powered information sorting mechanisms come in. While your affiliate website gathers all the information from , you can “unleash” an automated software that can easily sort through all this data, categorize them, and finally, provide some useful analyses. And as this technology becomes more sophisticated, the conclusions and suggestions will also become more refined. Google Analytics One of the most popular and already sophisticated chatbots is developed by Facebook. Its and the automated customer service is already used by many providers all around the world. Affiliate marketers, considering how large of a consumer base Facebook has, should definitely leverage this tool to their advantage. Messenger platform Chatbots can also offer affiliate links But the chatbot system can have an additional benefit in this branch of marketing. You see, when a customer chats with the AI, the chatbot is constantly monitoring the words used by them. And depending on which words are used, the pre-programmed responses are generated accordingly. This kind of automated mechanism can be utilized for affiliate marketing. For example, if a user writes words like hair and color, a chatbot can intuitively provide an affiliate link to the hair coloring solution. And this is just one tiny example of how the AI-powered responses can reduce effort and cost spent on finding appropriate places to put the links. As of right now, there are several new automated tools that can easily interpret the data and provide some useful insights. And all of that without human intervention. For example, open-source data analytics platforms such as KNIME or Orange can be a good start for processing a vast collection of data. These types of tools, in general, are already quite sophisticated to the point where data exploitation and analysis happens automatically and in an instant. New challenges in the new decade As we step into the new decade of 2020, the technological possibilities are becoming ever-more exciting, not to mention crucial to our elevated lifestyle and increased demands. Affiliate marketing, being a product of internet interaction, is going to experience that change even more drastically. In this era of information exchange, affiliate marketers should be looking for ways of automating their platforms to better sort through vast databases of information and effectively translate them into usable analytics. And in this task, AI can prove beneficial more than anything.

How Technological Progress Takes Affiliate Marketing to the Next Level

Machine Learning is one of the emerging technologies of the present IT industry. This technology has now become the talk of the town and has seen  an abnormally high growth over the few years. Machine Learning technology, which  wasn’t even heard about just a few years back, has now become one of the mainstream technologies that  many businesses are willing to adapt  to enhance their business. Many Machine Learning tools are now revolutionizing the various sectors of the global economy. But before learning  which  those tools are, let us first define what is Machine Learning. What is Machine Learning? Machine Learning is a technology that allows the systems to learn directly from examples, data, and experience. It is a subset of Artificial Intelligence (AI). It allows the machines to learn automatically, and improve from experience without being explicitly programmed. Machine Learning is considered as a technology of the present and future. It has shown its impact on various sectors of this world and is the reason why so many professionals are willing to make a career in this field of technology. Any professional can learn in-depth of Machine Learning through these . online courses Now let look into various Machine Learning tools: TensorFlow is a JavaScript library that helps in Machine Learning. It is an open-source library that helps to develop Machine Learning models. It has been developed by Google and has a flexible scheme of tools, libraries, and resources that help the researchers and developers in building and deploying Machine Learning applications. 1) TensorFlow : TensorFlow tool is used by some of the famous companies such as eBay, Twitter, and Dropbox, etc. It supports various operating systems such as Linux, Mac OS, and Windows. Scikit-learn is an open-source library for Machine Learning. It is used for Machine Learning development in Python. It supports various operating systems such as Linux, Mac OS, and Windows and helps in Data Mining and Data Analysis. 2) Scikit-learn : Scikit-learn includes several models of Machine Learning such as regression, classification, clustering, and reduction of dimensionality. PyTorch is a Torch based, Python Machine Learning library. It is written using some of the programming languages including Python, C++, CUDA, etc. 3) PyTorch : PyTorch was developed by Facebook’s AI research group in 2016 and  offers Dynamic Computational Graph wherein you can modify the graph on the go with the help of Autograd. This tool has better performance comparatively and according to GitHub repo , PyTorch is faster compared to other frameworks in terms of images processed per second. Benchmark on Deep Learning Frameworks and GPU’s KNIME is a tool used for Data Analytics, reporting and integration platform. It combines different components for Machine Learning and Data Mining using data pipelining concept. 4) KNIME : KNIME helps you to create entire Data Science workflows using a drag and drop interface. This tool can be used for business intelligence, financial data analysis, and also for CRM. Besides offering these applications, it is also very easy to learn and also to deploy and install. Accord.Net is written in C#. This framework consists of multiple libraries encompassing a wide range of scientific computing applications such as Statistical Data Processing, Machine Learning, etc. 5) Accord.Net : Accord.Net provides a large number of probability distributions, hypothesis tests, kernel tests.  It also provides support for most popular measurement techniques. These are some of the prominent tools that we have discussed here. But apart from these tools, there are some more Machine Learning tools available that you can check . These also have some unique features, such as these above-mentioned tools, and can be very useful to organizations. here In this article, we have introduced you to some of the important Machine Learning tools. These are the tools that help the organizations to provide their customers an effective software development solutions as per their requirements. Conclusion: on Machine Learning that provide you an in-depth knowledge of this technique. We hope this guide has been successful in introducing you to some of the Machine Learning tools that can be of great help to you. We also would like to introduce online courses If you think some more tools can be supplemented to the above list, then you can send your suggestions in the comment section below.

Introduction to Different Machine Leaning Tools

Part 9 of The where I interview my heroes. series During the past few interviews, I’ve had the chance of interacting with , , , and and an . Kaggle Grandmasters Technical Leaders Practitioners Two Distinguished Researchers OpenAI Fellow Today, I’m honored to be talking with Mikel Bober-Irizar, (@anokas) or (aka anoukas) If you’ve been living under a rock, Mikel is 17 years old and is The Youngest Kaggle Competition Grandmaster (ranked #31). He’s also a Kernels Master (ranked #13) and Discussions Master (ranked #15). About the Series: I have very recently started making some progress with my . But to be honest, it wouldn’t be possible at all without the amazing community online and the great people that have helped me. Self-Taught Machine Learning Journey In this Series of Blog Posts, I talk with People that have really inspired me and whom I look up to as my role-models. The motivation behind doing this is, you might see some patterns and hopefully you’d be able to learn from the amazing people that I have had the chance of from. learning Hi Mikel! Thank you so much for doing this interview.Congratulations on becoming the Youngest Grandmaster recently! Sanyam Bhutani: Thank you! I’m honored to be interviewed here :) Mikel Bober-Irizar: How does it feel to be the youngest grandmaster? Sanyam Bhutani: Do you get to bunk school? I’m pretty proud of it — but unfortunately it doesn’t mean I get to bunk off school. I still have to find time outside of school for my ML projects; although I have been known to kaggle in class close to competition deadlines! 😅 Mikel Bober-Irizar: You’ve had a very inspiration learning path, you started out when you were 14! Sanyam Bhutani: Could you tell us what got you interested in AI? I actually got into machine learning because of the competitive aspect (On Kaggle and another website which had just launched called numer.ai) — it seemed like a fun challenge in both a fascinating domain and something I knew nothing about at the time. Mikel Bober-Irizar: You were really young when you started kaggle. How has the journey been from a noob to a Grandmaster? Sanyam Bhutani: It’s been a lot of fun. When I started I had no idea about how it all actually worked, but I made an account and started having a go at some of the competitions anyway — initially using simple GUI like KNIME and copying other people’s publicly shared kernels, while doing parameter tuning and other small modifications. It took a few months for me to start writing my own code (and my Python was very rusty to begin with), but learning through trial and error instead of just taking a course helped give me a good intuition for what techniques work and where they work. Mikel Bober-Irizar: tools The highlight of my journey has probably been working in teams — it’s a lot more rewarding to work on a competition with other Kagglers, where you can bounce ideas off each other and learn from each other’s skills. Other than that, it’s just been a case of doing more and more competitions — the leaderboard is a great motivator! What competitions do you seek today? How do you pick which ones to go for? Sanyam Bhutani: The first thing I look for is good data. A lot of competitions have little or badly prepared data, or it may be unclear what the public/private leaderboard split is. These are the competitions where it’s obvious there’s going to be a ton of shakeup — and I try to avoid these. My favourite competitions are ones where there’s a lot of feature engineering to be done, or clever ways to combine multiple data sources, instead of just taking the data and sticking it into a neural network (I’m not an expert at making high performance NNs so I’d lose!). Mikel Bober-Irizar: But it also just depends on how much the specific competition interests me. I try out most competitions, but most of them I’ll lose interest after a day — I tend to only have one competition at a time which I’m working on extensively. Amazing! During our chats, you’ve shared your approach to learning something which is different from just taking an online course. Sanyam Bhutani: How do you approach a new problem? What are your go to techniques when approaching a new problem? Yeah, I tend not to take online courses but instead try and figure things out for myself — which helps me understand it better. If I have a new problem I haven’t seen before, I will do some research first, and then try solving it myself step by step. Whenever I have some issue (very often), a combination of googling and stackoverflow tends to solve it — even if it takes some time to figure out. Mikel Bober-Irizar: You’re about to graduate from High school. Sanyam Bhutani: What are your future plans? I just applied to university, so my hope for next year is to be able to get into a good university! I’m also hoping to get a summer internship next year, but other than it’s all open. I plan to continue with ML in the long term though :) Mikel Bober-Irizar: What would be your best advice to the people just starting out on kaggle? Sanyam Bhutani: One thing I will say is that the competitions have gotten much harder over the last few years, so if you’re just starting out it’s useful to take a look at the playground competitions or previous competitions from the last few years. Kaggle Kernels are probably the best resource for learning, as people share tons of analysis and solutions to all the competitions. The most important thing is just to experiment for yourself and try to improve the score! Mikel Bober-Irizar: Before we conclude, legend says you’re a robot from the future. Sanyam Bhutani: Can you comment on that statement? 4e 6f 20 63 6f 6d 6d 65 6e 74 2c 20 73 6f 72 72 79 2e Mikel Bober-Irizar: You’re an inspiration to everyone starting out on kaggle. Thank you so much for doing this interview. Sanyam Bhutani: Thank you for having me! Mikel Bober-Irizar: If you found this interesting and would like to be a part of My Learning Path , you can find me on Twitter here . If you’re interested in reading about Deep Learning and Computer Vision news, you can checkout my newsletter here .

Interview with The Youngest Kaggle Grandmaster: Mikel Bober-Irizar (anokas)

August 20, 2018 By , I’m referring to introductory content that is intending to teach a concept succinctly. I’ve avoided including chapters of books, which have a greater breadth of coverage, and research papers, which generally don’t do a good job in teaching concepts. Why not just buy a book? Tutorials are helpful when you’re trying to learn a specific niche topic or want to get different perspectives. tutorial I’ve split this post into four sections: , , , and . I’ve included a sampling of topics within each section, but given the vastness of the material, I can’t possibly include every possible topic. Machine Learning NLP Python Math If there are tutorials you are aware of that I’m missing, please let me know! I’m trying to limit each topic to five or six tutorials since much beyond that would be repetitive. Each link should have a different material from the other links or present information in a different way (e.g. code versus slides versus long-form) or from a different perspective. (Source via — Robbie Allen) good Machine Learning (medium.com/@ageitgey) Machine Learning is Fun! Machine Learning Crash Course: , , (Machine Learning at Berkeley) Part I Part II Part III (toptal.com) An Introduction to Machine Learning Theory and Its Applications: A Visual Tutorial with Examples (monkeylearn.com) A Gentle Guide to Machine Learning (sas.com) Which machine learning algorithm should I use? Activation and Loss Functions (neuralnetworksanddeeplearning.com) Sigmoid neurons (quora.com) What is the role of the activation function in a neural network? (stats.stackexchange.com) Comprehensive list of activation functions in neural networks with pros/cons (medium.com) Activation functions and it’s types-Which is better? (exegetic.biz) Making Sense of Logarithmic Loss (Stanford CS231n) Loss Functions (rishy.github.io) L1 vs. L2 Loss function (neuralnetworksanddeeplearning.com) The cross-entropy cost function Bias (stackoverflow.com) Role of Bias in Neural Networks (makeyourownneuralnetwork.blogspot.com) Bias Nodes in Neural Networks (quora.com) What is bias in artificial neural network? Perceptron (neuralnetworksanddeeplearning.com) Perceptrons (natureofcode.com) The Perception (dcu.ie) Single-layer Neural Networks (Perceptrons) (toptal.com) From Perceptrons to Deep Networks Regression (duke.edu) Introduction to linear regression analysis (ufldl.stanford.edu) Linear Regression (readthedocs.io) Linear Regression (readthedocs.io) Logistic Regression (machinelearningmastery.com) Simple Linear Regression Tutorial for Machine Learning (machinelearningmastery.com) Logistic Regression Tutorial for Machine Learning (ufldl.stanford.edu) Softmax Regression Gradient Descent (neuralnetworksanddeeplearning.com) Learning with gradient descent (iamtrask.github.io) Gradient Descent (kdnuggets.com) How to understand Gradient Descent algorithm (sebastianruder.com) An overview of gradient descent optimization algorithms (Stanford CS231n) Optimization: Stochastic Gradient Descent Generative Learning (Stanford CS229) Generative Learning Algorithms (monkeylearn.com) A practical explanation of a Naive Bayes classifier Support Vector Machines (monkeylearn.com) An introduction to Support Vector Machines (SVM) (Stanford CS229) Support Vector Machines (Stanford 231n) Linear classification: Support Vector Machine, Softmax Backpropagation (medium.com/@karpathy) Yes you should understand backprop (github.com/rasbt) Can you give a visual explanation for the back propagation algorithm for neural networks? (neuralnetworksanddeeplearning.com) How the backpropagation algorithm works (wildml.com) Backpropagation Through Time and Vanishing Gradients (machinelearningmastery.com) A Gentle Introduction to Backpropagation Through Time (Stanford CS231n) Backpropagation, Intuitions Deep Learning (nikhilbuduma.com) Deep Learning in a Nutshell (Quoc V. Le) A Tutorial on Deep Learning (machinelearningmastery.com) What is Deep Learning? (nvidia.com) What’s the Difference Between Artificial Intelligence, Machine Learning, and Deep Learning? Optimization and Dimensionality Reduction (knime.org) Seven Techniques for Data Dimensionality Reduction (Stanford CS229) Principal components analysis (Hinton @ NIPS 2012) Dropout: A simple way to improve neural networks (rishy.github.io) How to train your Deep Neural Network Long Short-Term Memory (LSTM) (machinelearningmastery.com) A Gentle Introduction to Long Short-Term Memory Networks by the Experts (colah.github.io) Understanding LSTM Networks (echen.me) Exploring LSTMs (iamtrask.github.io) Anyone Can Learn To Code an LSTM-RNN in Python Convolutional Neural Networks (CNNs) (neuralnetworksanddeeplearning.com) Introducing convolutional networks (medium.com/@ageitgey) Deep Learning and Convolutional Neural Networks (colah.github.io) Conv Nets: A Modular Perspective (colah.github.io) Understanding Convolutions Recurrent Neural Nets (RNNs) (wildml.com) Recurrent Neural Networks Tutorial (distill.pub) Attention and Augmented Recurrent Neural Networks (karpathy.github.io) The Unreasonable Effectiveness of Recurrent Neural Networks (nikhilbuduma.com) A Deep Dive into Recurrent Neural Nets Reinforcement Learning (analyticsvidhya.com) Simple Beginner’s guide to Reinforcement Learning & its implementation (mst.edu) A Tutorial for Reinforcement Learning (wildml.com) Learning Reinforcement Learning (karpathy.github.io) Deep Reinforcement Learning: Pong from Pixels Generative Adversarial Networks (GANs) (nvidia.com) What’s a Generative Adversarial Network? (medium.com/@ageitgey) Abusing Generative Adversarial Networks to Make 8-bit Pixel Art (aylien.com) An introduction to Generative Adversarial Networks (with code in TensorFlow) (oreilly.com) Generative Adversarial Networks for Beginners Multi-task Learning (sebastianruder.com) An Overview of Multi-Task Learning in Deep Neural Networks NLP (Yoav Goldberg) A Primer on Neural Network Models for Natural Language Processing (monkeylearn.com) The Definitive Guide to Natural Language Processing (algorithmia.com) Introduction to Natural Language Processing (vikparuchuri.com) Natural Language Processing Tutorial (arxiv.org) Natural Language Processing (almost) from Scratch Deep Learning and NLP (arxiv.org) Deep Learning applied to NLP (Richard Socher) Deep Learning for NLP (without Magic) (wildml.com) Understanding Convolutional Neural Networks for NLP (colah.github.io) Deep Learning, NLP, and Representations (explosion.ai) Embed, encode, attend, predict: The new deep learning formula for state-of-the-art NLP models (nvidia.com) Understanding Natural Language with Deep Neural Networks Using Torch (pytorich.org) Deep Learning for NLP with Pytorch Word Vectors (kaggle.com) Bag of Words Meets Bags of Popcorn On word embeddings , , (sebastianruder.com) Part I Part II Part III (acolyer.org) The amazing power of word vectors (arxiv.org) word2vec Parameter Learning Explained Word2Vec Tutorial — , (mccormickml.com) The Skip-Gram Model Negative Sampling Encoder-Decoder (wildml.com) Attention and Memory in Deep Learning and NLP (tensorflow.org) Sequence to Sequence Models (NIPS 2014) Sequence to Sequence Learning with Neural Networks (medium.com/@ageitgey) Machine Learning is Fun Part 5: Language Translation with Deep Learning and the Magic of Sequences (machinelearningmastery.com) How to use an Encoder-Decoder LSTM to Echo Sequences of Random Integers (google.github.io) tf-seq2seq Python (kdnuggets.com) 7 Steps to Mastering Machine Learning With Python (nbviewer.jupyter.org) An example machine learning notebook Examples (machinelearningmastery.com) How To Implement The Perceptron Algorithm From Scratch In Python (wildml.com) Implementing a Neural Network from Scratch in Python (iamtrask.github.io) A Neural Network in 11 lines of Python (kdnuggets.com) Implementing Your Own k-Nearest Neighbour Algorithm Using Python (machinelearningmastery.com) Demonstration of Memory with a Long Short-Term Memory Network in Python (machinelearningmastery.com) How to Learn to Echo Random Integers with Long Short-Term Memory Recurrent Neural Networks (machinelearningmastery.com) How to Learn to Add Numbers with seq2seq Recurrent Neural Networks Scipy and numpy (scipy-lectures.org) Scipy Lecture Notes (Stanford CS231n) Python Numpy Tutorial (UCSB CHE210D) An introduction to Numpy and Scipy (nbviewer.jupyter.org) A Crash Course in Python for Scientists scikit-learn (nbviewer.jupyter.org) PyCon scikit-learn Tutorial Index (github.com/mmmayo13) scikit-learn Classification Algorithms (scikit-learn.org) scikit-learn Tutorials (github.com/mmmayo13) Abridged scikit-learn Tutorials Tensorflow (tensorflow.org) Tensorflow Tutorials (medium.com/@erikhallstrm) Introduction to TensorFlow — CPU vs GPU (metaflow.fr) TensorFlow: A primer (wildml.com) RNNs in Tensorflow (wildml.com) Implementing a CNN for Text Classification in TensorFlow (surmenok.com) How to Run Text Summarization with TensorFlow PyTorch (pytorch.org) PyTorch Tutorials (gaurav.im) A Gentle Intro to PyTorch (iamtrask.github.io) Tutorial: Deep Learning in PyTorch (github.com/jcjohnson) PyTorch Examples (github.com/MorvanZhou) PyTorch Tutorial (github.com/yunjey) PyTorch Tutorial for Deep Learning Researchers Math (ucsc.edu) Math for Machine Learning (UMIACS CMSC422) Math for Machine Learning Linear algebra (betterexplained.com) An Intuitive Guide to Linear Algebra (betterexplained.com) A Programmer’s Intuition for Matrix Multiplication (betterexplained.com) Understanding the Cross Product (betterexplained.com) Understanding the Dot Product (U. of Buffalo CSE574) Linear Algebra for Machine Learning (medium.com) Linear algebra cheat sheet for deep learning (Stanford CS229) Linear Algebra Review and Reference Probability (betterexplained.com) Understanding Bayes Theorem With Ratios (Stanford CS229) Review of Probability Theory (Stanford CS229) Probability Theory Review for Machine Learning (U. of Buffalo CSE574) Probability Theory (U. of Toronto CSC411) Probability Theory for Machine Learning Calculus (betterexplained.com) How To Understand Derivatives: The Quotient Rule, Exponents, and Logarithms (betterexplained.com) How To Understand Derivatives: The Product, Power & Chain Rules (betterexplained.com) Vector Calculus: Understanding the Gradient (Stanford CS224n) Differential Calculus (readthedocs.io) Calculus Overview Hope you like this article !! Don’t forget to subscribe and like my blogs. Share some love too! Share: Press This Telegram WhatsApp Mohit Sharma(themenyouwanttobe&Co.) Connect with me Telegram My Latest Blog: THEMENYOUWANTTOBE

Data Science Resources

12 Mistakes that Data Scientists Make and How to Avoid Them

Echo, Siri and Cortana are popular VUIs. However, all these assistants were designed with different goals in mind. Echo was designed as a voice first interface while Siri was designed as just another way of interacting with your iPhone. This is changing. Speech recognition refers to what the recognition system hears. The recognition engine returns a set of responses for every user query. As this technology improves, the challenge of designing a great VUI lies in how the system responds. is how the VUI interprets those responses. It is the way input is handled, rather than the accuracy of correctly transcribing what was said. Natural language understanding(NLU) Handling user responses When you say “Read me this article”, I can interpret this as “Read me this article” or “Reed me this article.” The second interpretation does not really make sense. As an intelligent person, I am expected to understand the context and the logic of the statement to respond to it. Likewise, an intelligent system needs to know what the user meant and respond to it accordingly. Here are the different types of responses that the system needs to handle: Constrained/finite responses For many of the questions that a system asks, there are only a finite set of responses that are logically valid. For example, when the system asks “what is your favorite animal?” or “Do you want to go outside?” Saying “orange” is not a logical response. Providing a mechanism to capture and interpret responses and discard irrelevant ones is especially challenging for a voice based system where the user can choose from a seemingly infinite response set. In the first case, the VUI will have a list of accepted animal names, while in the second one we only need to look at variations of “yes” and “no.” If the user says something outside of the finite set of responses, it can be handled with a null response like “I’m sorry I don’t understand.” Here are some examples that require constrained responses: What is your favorite fruit? : Mango, Apple, Banana, etc. What country are you from? : India, USA, China, etc. What song do you want to hear? : Fix you, Lasya, Roja, etc. Would like to book the tickets? : Yes, No, Naah, Oh yeah, etc. The response sets might be lengthy, however they still are finite. Open Speech One case is when you want the conversation to have a natural flow, but do not explicitly want to handle input. For example, the assistant might say “Hey there! Long time no see!” to which the user might reply “Nothing much” or “Gotta go work now.” In that case, the assistant can give a generic reply like “Hmm.. I see” or “Ok alright.” The user response is not critical for the conversation to continue, the user could say anything and the logic of the response should still be valid. A generic answer is alright. Another strategy for a generic reply would be during confirmations like “Alright! Done!” or “I’ll send this information to our customer service team.” Categorization of input A good strategy to handle user input is to categorize inputs in broadly defined buckets such as positive/negative, happy/sad/excited, good/bad, etc. The VUI simply looks to map to a category rather than give an exact response. For example, the VUI can ask “How was your experience at our restaurant?”: : Good, Amazing, Terrific, Awesome, etc. Good : Depressing, Bad, Irritating, etc. Bad The assistant can then respond accordingly. _Protip:_ Try not announcing what the user is feeling already. For example, when the user says that the experience was bad, do not say “It seems you’ve had a bad experience. Let us know how we can improve.” The user has already indicated the mood, it’s unnatural to repeat it. Instead try saying something reassuring like “I’m sorry to hear that. Want to tell us more?” Logical expressions Looking for specific keywords or phrases is a simpler method, however it is important for a voice based system to allow for more complex queries. For example, the intent for the following queries is the same: “My computer is really slow” “My computer is really really slow” “Computer is slow. What to do?” Booking a cab or ordering food are simple intents, however there might be variations in the way a user asks for information. It would be a huge task to write a condition for each of these variations. Instead, the system could build a recognition for common patterns. Negation Imagine a VUI asking you “How was you experience at the restaurant?” and you say “Not very good.” The VUI designers have not considered this response and pick up the keyword “good” and responds by saying “Awesome! Thanks!” The VUI already sounds stupid and the user might become wary of trusting the assistant. Handling negation is a much more difficult task, but the cost of ignoring it is high. Disambiguation The word disambiguation literally means removing uncertainty and is arguably is one of the most important problems that voice interfaces need to tackle. A simple example would be placing a phone call. If you ask Siri to “call John” and there are multiple Johns in your contact list, it would ask you “which John?” followed by the full names of each of the contacts disambiguated.The system also needs to disambiguate in cases where the user provides insufficient information or excessive information. For example, if a user says “I’d like a large pizza” for which the natural followup question could be “what kind of pizza would you like?” This is a case of insufficient information which the assistant can resolve by asking a leading question. In the case where a the user gives excessive information which the system is not built to handle, the system can ask the user to provide only one piece of information at a time. However, it would be more beneficial to account for multiple pieces of information. Capturing intent For more complex VUIs, you need smarter ways of handling speech input. In many cases like messaging, there are multiple things you can do with a messaging app. You could say, “Send a message to mom” or “Read my last message” or “Have I got any messages?” In each case, the intent is different and handling these queries by searching for the keyword “message” might not be the best strategy. Instead the VUIs NLU model should be trained to handle each of these queries as separate intents. Capturing objects In cases where user utters multiple pieces of information at once, the NLU model should be able to handle the query and capture objects to be used for the intent. For example, if a user says “Order me a large cappuccino from Starbucks at home,” the user has already specified the type of coffee, size, restaurant name and place of delivery. The system should be able to pre-fill this information. You can use tools like Api.ai, Microsoft LUIS, Nuance Mix, Wit.ai, etc. to build and test these models. Wake words Wake words are often used to invoke VUI system. For example, “Alexa” is the wake word for Amazon Echo, while “Hey Google” or “Ok Google” are wake words for Google assistant. Using a wake word is one of the ways to start an interaction with the VUI system without having to touch any device. Following are the things to keep in mind when designing a wake word: It should be easily recognizable. Short words like “Jim” or “Will” are difficult to recognize. It should be easy for users to say it. Use words with multiple syllables. Take note of Alexa or Siri’s wake words, they all have multiple syllables. Don’t choose words that people might say regularly in conversations. Another important thing to note is that wake words should be handled locally. You device should always listen for the wake word and then start recording the user’s voice to send it to the cloud for processing. Always recording and sending data to the cloud is unethical and will lead to serious distrust among users. TTS versus Recorded voice Another important decision you need to make is whether to use Text-to-speech (TTS) or a recorded voice to answer user queries. Although a recorded voice feels more natural, it is expensive and time consuming to record for all answers. TTS on the other hand can work realtime, but sounds robotic. Although it is improving, TTS still has difficulty pronouncing certain words, emotion is difficult to indicate. TTS can be improved by applying , which can help add more natural sounding intonations and pronunciations on the fly. Despite this, there are still words and phrases that the TTS engine might have difficulty with and it might be necessary for you to build a pronunciation dictionary. Speech Synthesis Markup Language (SSML) As a rule of thumb, it is generally a good strategy to use a combination of TTS and recorded voice. Recordings can be used for most common responses like confirmations and greetings. Apart from this, it is also a good strategy to build a voice font of your recording artist, in case you are using a hybrid model. Voice biometric authentication Voice biometric authentication also known as Voice ID or Speaker verification is a mechanism which allows users to authenticate themselves just by their voice. Although the technology is improving, it is generally not advisable to use voice ID alone for authentication (I’m sure i’ll have to eat my words about this in the future). Another use of speaker verification is in speaker identification rather than to authenticate. The VUI can identify who is speaking and respond accordingly. Sentiment analysis Another way of making your VUI smarter would be using sentiment analysis and detecting emotions. Sentiment analysis often refers to identifying and categorizing opinions in a piece of text. Although it sounds complicated, you constant doing sentiment analysis quite easily. First you need to define your categories (for eg. positive, negative or neutral) and compare what the user said to categorize. You can use open source tools like KNIME to get started. Emotion detection This is a fairly new field where companies like Affectiva(now owned by Apple) have begun using facial characteristics to detect emotions of users which can be used by the system to give an appropriate response. Emotion detection can also be done by analyzing voice samples in real time. detects emotions via a voice stream which their Moodies app analyzes to display the primary emotion. Beyond verbal One key thing to remember when using techniques to detect emotions is to err on the side of caution. Getting the emotional state right is good, however getting it wrong might have disastrous consequences. **References:**1. 2. 3. Being Digital — Nicholas Negroponte Designing voice user interfaces — Cathy Pearl Design for Voice Interfaces — Laura Klein If you liked this article, please click the 👏 button (once, twice or more). Share to help others find it!

Designing great voice experiences using natural language cues

Why is everyone flocking to AI and neural networks like it's the hottest concert in town? Well, think of AI as the Swiss Army knife of tech—it's versatile, it's smart, and it's your go-to solution for nearly everything digital. AI: The Go-to Solution Programmers are drawn to it like moths to a flame because it's not just another fad; it's a golden ticket to innovation. Picture this: you're the conductor of an AI orchestra, composing algorithms that predict stock market trends or diagnose diseases faster than a seasoned doctor. It's not just coding; it's like being the architect of tomorrow's world. The job market is ablaze with demand for AI wizards. Companies are practically hosting bake-offs to snag the top AI talent. Why? Because AI isn't just a buzzword; it's a game-changer across industries. Need to optimize supply chains? AI's got your back. Want to revolutionize customer service with chatbots smarter than your average Joe? AI's your wingman. But it's not all serious business in the AI realm. Sometimes, AI can be as unpredictable as a box of chocolates. Remember when your virtual assistant misheard "play some jazz" and thought you said "chase a cat"? Yeah, AI's got its moments—some unintentionally hilarious, some downright quirky. In a nutshell, AI and neural networks aren't just career paths; they're the rocket fuel propelling us into an era of innovation. They're the da Vinci of our digital age, painting a future that's smarter, faster, and more efficient. To all the aspiring programmers out there, buckle up! The AI rollercoaster isn't just a thrilling ride; it's the gateway to shaping a world where imagination meets technology in the most remarkable ways. Ever caught yourself humming to a tune that feels like it was composed just for you? Well, surprise! AI might be the secret DJ behind your favorite playlists. Who knew that behind every catchy melody, there's a sneaky algorithm secretly trying to get stuck in your head? It's like having a musical sidekick you never knew you needed. And let's talk about farming – not your grandma's farming, mind you. Today's farms have upgraded; they've got drones cruising over fields like tiny sci-fi guardians. It's the future of farming: AI-powered crop cuddling, making sure each tomato feels loved and cared for. Who would've thought technology and agriculture could be a match made in harvest heaven? Art authentication is where AI becomes the Sherlock Holmes of the art world. Analyzing brushstrokes, scrutinizing canvas textures—seriously, these algorithms could give a detective a run for their money. Next time you're admiring a masterpiece, just imagine AI whispering, "Elementary, my dear art lover." Fashionistas, brace yourselves! AI isn't just predicting trends; it's designing clothes. Soon, we'll be saying, "Alexa, can you make me a dress that matches my mood today?" And voila! Your AI stylist will have you strutting like a runway model. Who would've thought that our love for good food could get an AI twist? Smart cooking apps are the new kitchen companions, guiding us through recipes and suggesting flavor combinations. Don't be surprised if your dinner whispers, "Hey, thanks to AI, I'm delicious AND nutritious!" And sports? AI's not just analyzing stats; it's the MVP behind the scenes, predicting plays and keeping athletes in top form. It's like having a virtual coach yelling, "Come on, you can do it!"—except it's all in code. In this AI-infiltrated world, these unexpected domains not only surprise us but also give us a chuckle. Who knew that behind every beat, every stroke, and every game-winning move, there's a dash of AI wizardry making it all happen?

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