Hackernoon logoTop 20 ML Stories For Data Science by@divyanshi

Top 20 ML Stories For Data Science

Data Science is undoubtedly one of the main fields that every AI, ML, or data enthusiast crosses paths with. Here are my top 20 picks for you if you are into Data Science and extending your first step towards Machine Learning: The best image classification datasets vary in scope and magnitude and can suit a variety of use cases. The datasets have been divided into the following categories: medical imaging, agriculture & scene recognition, and others. The datasets come from various locations around the world, and most of the data covers large time periods.
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Data Science is undoubtedly one of the main fields that every AI, ML, or data enthusiast crosses paths with. Here are my top 20 picks for you if you are into Data Science and extending your first step towards Machine Learning:

10 Best Image Classification Datasets for ML Projects

By @limarc

To help you build object recognition models, scene recognition models, and more, we’ve compiled a list of the best image classification datasets. These datasets vary in scope and magnitude and can suit a variety of use cases. Furthermore, the datasets have been divided into the following categories: medical imaging, agriculture & scene recognition, and others. 

An Introduction to Machine Learning Model Scoring

By  @modzy

There is a plethora of metrics that can be used to evaluate machine learning models, and identifying the right metric to use is a crucial step for accurately assessing a model’s performance and whether its predictions are trustworthy.

One of the major challenges is that a model could simply memorize the data it is being trained with, and consequently perform poorly on new, unseen samples.

In the case of classification, a model could also favor one class over another because the training dataset used contained an imbalanced number of samples from each class.

Machine Learning in Cybersecurity: 5 Real-Life Examples

By @ngorovitch

From real-time cybercrime mapping to penetration testing, machine learning has become a crucial part of cybersecurity. Fortunately, machine learning can help solve the most common tasks, including pattern detection, prediction, regression, and classification.

In an era of large amounts of data and a shortage of network security talents, machine learning seems to be an alternative to solve many problems. Indeed, through machine learning, when applied to computer security, we can sort through millions of files to discover threats. Microsoft Windows Defender, for example, employs multiple layers of machine learning to block potential threats. 

A Quick Introduction to Machine Learning with Dagster

By @stephanbv

This article is a rapid introduction to Dagster using a small ML project. It is beginner-friendly but might also suit more advanced programmers if they don't know Dagster.

17 Open Crime Datasets for Data Science and Machine Learning Projects

By @limarc

For those looking to analyze crime rates or trends over a specific area or time period, we have compiled a list of the 16 best crime datasets made available for public use.

The datasets come from various locations around the world, and most of the data covers large time periods. 

What is Data Imbalance in Machine Learning?

By @modzy

Data imbalance, or imbalanced classes, is a common problem in machine learning classification where the training dataset contains a disproportionate ratio of samples in each class. Examples of real-world scenarios that suffer from class imbalance include threat detection, medical diagnosis, and spam filtering.

GPU Computing for Machine Learning

By @modzy

GPU Computing (general-purpose computing on graphics processing units) enables many modern machine learning algorithms that were previously impractical due to slow runtime. By taking advantage of the parallel computing capabilities of GPUs, a significant decrease in computational time can be achieved relative to traditional CPU computing.

Machine Learning Frameworks for PHP Developers

By @atulhjindall

Today Machine learning is Buzz, and many social media groups on software design and engineering are filled with ML, AI, Python, Tensor Flow, NumPy related posts. We all wonder, PHP has been in the market for more than 2 decades, and there has been no machine learning with PHP. In this article, I will cover some available frameworks for building Machine Learning applications using PHP. Let's start with a basic understanding of AI.

Machine Learning in Cybersecurity: 5 Real-Life Examples

By @ngorovitch

From real-time cybercrime mapping to penetration testing, machine learning has become a crucial part of cybersecurity. Fortunately, machine learning can help solve the most common tasks, including pattern detection, prediction, regression, and classification.

In an era of large amounts of data and a shortage of network security talents, machine learning seems to be an alternative to solve many problems. Indeed, through machine learning, when applied to computer security, we can sort through millions of files to discover threats. Microsoft Windows Defender, for example, employs multiple layers of machine learning to block potential threats. 

How To Get Started With Machine Learning: A Tutorial For Beginners By A Beginner

By @torbet

Here's how a beginner can get started with machine learning by a REAL beginner. This will cover everything, from what libraries and frameworks to use to how to save and load your trained model. By the end of this project, you should have a rough idea as to how it all works, which will send you on your merry way to more complex projects.

What is Data Imbalance in Machine Learning?

By @modzy

Data imbalance, or imbalanced classes, is a common problem in machine learning classification where the training dataset contains a disproportionate ratio of samples in each class. Examples of real-world scenarios that suffer from class imbalance include threat detection, medical diagnosis, and spam filtering.

 How Machine Learning Can Help You Detect Influencers?

 By @kyle-jones

In the age of social media more than ever, marketing teams are turning to influencers to advertise their new, innovative products or services. The number of followers, impressions, and engagements all impact traffic, which in turn helps drive one very important metric - sales. This makes the ability to identify the next up-and-coming influencer all the more important and valuable, with 61% of marketers agreeing that it’s difficult to find the right influencers for a campaign.

Federated Learning: A Decentralized Form of Machine Learning

By @shaanray

Most major consumer tech companies that are focused on AI and machine learning now use federated learning – a form of machine learning that trains algorithms on devices distributed across a network, without the need for data to leave each device. Given the increasing awareness of privacy issues, federated learning could become the preferred method of machine learning for use cases that use sensitive data (such as location, financial, or health data).

Machine-Learning Neural Spatiotemporal Signal Processing with PyTorch Geometric Temporal

By @benitorosenberg

PyTorch Geometric Temporal is a deep learning library for neural spatiotemporal signal processing. This library is an open-source project. It consists of various dynamic and temporal geometric deep learning, embedding, and Spatiotemporal regression methods from a variety of published research papers.

What’s the Difference Between AI And Machine Learning?

By @udacity

It’s nearly impossible to have a conversation about technology without mentioning artificial intelligence (AI) or machine learning (ML). 

These terms are cropping up everywhere in conversations about how tech is changing the world and streamlining our lives. However, AI and ML are often used interchangeably, making the difference between the two even less clear. 

In this post that explores AI vs. machine learning, we’ll take a step back and examine what sets these disciplines apart and exactly how they’re shaping the future of tech.

Extract Prominent Colors from an Image Using Machine Learning

By @kamalkishor1991 

Ok, I did this back in 2014 in java, but since I reimplemented the app and the algorithm in javascript this time. I thought of writing about how I found a nice and simple algorithm to extract prominent colors out of an image.

Top 5 Machine Learning Programming Languages in 2021

By @zedism

If you are interested in the fields of artificial intelligence and machine learning, you’re probably planning your path forward in the exciting and dynamic world of programming. But which languages should you study if you see AI and machine learning in your future?

7 Challenges in Marketing AI & Machine Learning Solutions

By @vishal-mistry

AI technology definitely sounds business-friendly, hands down, and accepted. On the flip side, it is also imperative for technology companies to know about the problems while marketing AI-driven products or solutions.

This article will help our readers to identify and understand the challenges faced by the AI development companies to market the AI & ML products followed by the needs to overcome them.

Use plaidML to do Machine Learning on macOS with an AMD GPU

By @Alex_wulff

Want to train machine learning models on your Mac’s integrated AMD GPU or an external graphics card? Look no further than PlaidML.

Anyone who has tried to train a neural network with TensorFlow on macOS knows that the process kind of sucks. TensorFlow can only leverage the CPU on Macs, as GPU-accelerated training requires an Nvidia chipset. Most large models take orders of magnitude more time to train on a CPU than on even a simple GPU.

How To Improve Data Quality When With Unsupervised Machine Learning

By @mobidev

When preparing data, I often go through many different approaches to reach a level of quality of data that can provide accurate results. In this article, I describe how unsupervised ML can help in data preparation for machine learning projects and how it helps to get more accurate business insights.

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