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64 Stories To Learn About Machine Learning Tutorials

by Learn RepoJanuary 31st, 2024

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Learn everything you need to know about Machine Learning Tutorials via these 64 free HackerNoon stories.

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Let's learn about Machine Learning Tutorials via these 64 free stories. They are ordered by most time reading created on HackerNoon. Visit the /Learn Repo to find the most read stories about any technology.

1. Top 9 Free Beginner Tutorials for Machine Learning (ML)

This post includes a round-up of some of the best free beginner tutorials for Machine Learning.

2. Golang in Machine Learning

Can Golang be used in Machine Learning? In the article you will learn advantages and disadvantages of using Go lang in Machine learning

3. How to Build an Image Search Engine to Find Similar Images

After reading this article, you will be able to create a search engine for similar images for your objective from scratch

4. Beat The Heat with Machine Learning Cheat Sheet

If you are a beginner and just started machine learning or even an intermediate level programmer, you might have been stuck on how do you solve this problem. Where do you start? and where do you go from here?

5. How to Leverage Machine Learning to Improve AdWords Efficiency

Recent issues surrounding racial inequality in the United States have led to direct action in the digital marketing world as well. More and more companies are pausing their Facebook ad campaigns because of the social network’s inaction on discrimination and hate speech.

6. Automatic Feature Selection in Python: An Essential Guide

Feature Selection in python is the process where you automatically or manually select the features in the dataset that contribute most to your prediction.

7. How To Compare Documents Similarity using Python and NLP Techniques

In this post we are going to build a web application which will compare the similarity between two documents. We will learn the very basics of natural language processing (NLP) which is a branch of artificial intelligence that deals with the interaction between computers and humans using the natural language.

8. An Essential Python Text-to-Speech Tutorial Using the pyttsx3 Library

Basically, what we want to do is to give some piece of text to our program and it will convert that text into the speech and will read that to us.

9. The Four Types of Machine Learning | Part 2

In the previous post, we saw the first two types of machine learning. In this post, we will discuss the other two types of machine learning. These are — Semi-su

10. Pycaret: A Faster Way to Build Machine Learning Models

Pycaret is an open-source, low code library in python that aims to automate the development of machine learning models.

11. Evaluating Regression Models in Machine Learning

Model evaluation is very important since we need to understand how well our model is performing.

12. Image Style Transfer And Video Transformation In EbSynth

Using EbSynth and Image Style Transfer machine learning models to create a custom AI painted video/GIF.

13. Build a GUI for Your Machine Learning Models

How to build a cool GUI for your Machine Learning models with Gradio so that you can visualise your models easily and effectively for people to understand.

14. Kinetics Dataset - Training and Evaluating Models for Video Classification

A guide to using the open-source tool FiftyOne to download the Kinetics dataset and evaluate video understanding models

15. How I Approached Machine Learning Interviews at FAANGs as an ML Engineer

Cracking a Machine learning interview at companies like Facebook, Google, Netflix, Snap etc. really comes down to nailing few patterns that FAANGs look for.

16. How to Train Computer Vision Models Efficiently

The starting point of building a successful computer vision application is the model. Computer vision model training can be time-consuming and challenging if one doesn’t have a background in data science. Nonetheless, it is a requirement for customized applications.

17. The Hunt for Data: Creating a Computer Vision Dataset for Road Safety

In this article, I would like to share my own experience of developing a smart camera for cyclists with an advanced computer vision algorithm

18. Why and How do We Split the Dataset

Dataset is one important part of the machine learning project. Without data, machine learning is just the machine, and learning is stripped from the title. Whic

19. The Four Types Of Machine Learning

There are three types of machine learning. Initially, there were three, but later type added one more type to the ranks of machine learning types. Thus in total

20. What are Decision Trees in Machine Learning?

Learn to measure the performance of your Regression Models - Tutorial by Berk Hakbilen

21. Artificial Intelligence (AI) VS Machine Learning (ML) - A Beginner's Guide

If you are new to the AI and ML world, this guide is for you to clear the doubts between both domains.

22. Deploy Computer Vision Models with Triton Inference Server

There are a lot of Machine Learning courses, and we are pretty good at modeling and improving our accuracy or other metrics.

23. 70-Page Report on the COCO Dataset and Object Detection [Part 1]

Quickly find common resources and/or assets for a given dataset and a specific task, in this case dataset=COCO, task=object detection

24. How I Transfer an Artistic Style to Any Image

Machine learning and artificial intelligence have been on my radar for years now, but more as a concept and “thing I should know about.” I didn’t feel that I had the free time or skills to dig into it. However, my attitude about machine learning has changed in the past few months. I have seen new and easier tools become accessible to the public. In this post I will walk you through how to transfer an art style to any image using some of these tools.

25. Top 8 Machine Learning Content Creators on YouTube

Here are the top Machine Learning content creators on YouTube to follow for tutorials, deep learning, and more.

26. NLP Tutorial: Creating Question Answering System using BERT + SQuAD on Colab TPU

Open sourced by Google Research team, pre-trained models of BERT achieved wide popularity amongst NLP enthusiasts for all the right reasons! It is one of the best Natural Language Processing pre-trained models with superior NLP capabilities. It can be used for language classification, question & answering, next word prediction, tokenization, etc.

27. Machine Learning Model with FLASK REST API

In this tutorial we will see how you can make your first REST API for Machine Learning Model using FLASK. We will start by creating machine learning model. Then we will see step-by-step procedure to create API using Flask and test it using Postman.

28. 6 Essential Tips to Solve Data Science Projects

Data science projects are focusing on solving social or business problems by using data. Solving data science projects can be a very challenging task for beginners in this field. You will need to have a different skills set depending on the type of data problem you want to solve.

29. Building a Monte Carlo Markov Chain Pipeline Using Luigi

A few months ago I was accepted into a data science bootcamp - Springboard, for their data science career track. As part of this bootcamp I had to work on Capstone projects that would help build my portfolio, show my ability to extract, clean up data, build models and extract insights from said models. For my first project I opted to build a Monte Carlo Markov Chain pipeline initially with the objective of building a multi-touch attribution model that would help me understand conversion rates from different states in the signup process and use that to understand which channels appeared to deliver the greatest conversion rates for users coming through a given landing page and transitioning through the different signup states defined in my dataset.

30. From TF to TFLite: Deploying ML Models on Mobile [Part 1]

tl;dr - Link to code: TensorFlow GAN model.

So the other day I was talking to my rubber ducky about how G-Board predicts my next word, even when those words are entirely made up by me, in that how it actually learns on-device. How amazingly Netflix, Amazon, Google Maps make use of machine learning in their apps. How does machine learning on apps even work? Does the model learn even after being deployed? Can I deploy a GAN model on mobile?

31. NLP Tutorial: Topic Modeling in Python with BerTopic

Topic modeling is an unsupervised machine learning technique that can automatically identify different topics present in a document (textual data). Data has become a key asset/tool to run many businesses around the world. With topic modeling, you can collect unstructured datasets, analyzing the documents, and obtain the relevant and desired information that can assist you in making a better decision.

32. 9 Reasons Why You Should Keep Learning Machine Learning

Machine Learning is an application of Artificial Intelligence. It allows software applications to become accurate in predicting outcomes. Machine Learning focuses on the development of computer programs, and the primary aim is to allow computers to learn automatically without human intervention.

33. Top 5 Machine Learning Platforms to Watch in 2022

Machine Learning Operations (MLOps) is a form of DevOps in a growing area. In this article, we'll discuss the top 5 Machine Learning Platforms to watch in 2022.

34. Logistic Regression: Train Model In Python And Use It on Angular Front End

Demo for this article can be found here.

35. Machine Learning Explained in 5 Minutes

Google uses it to provide millions of search results every hour. It helps Facebook guess your next love interest. Even Elon Musk’s Tesla uses it to make self-dr

36. Adversarial Examples In Machine Learning Explained

There are easy ways to build adversarial examples that can fool any deep learning model and create security issues no matter how complex the model is.

37. Why Data Science Competitions are Important & How to Get Started

To become a Data Scientist, you have to learn, gain the required skills and practice a lot to get more experience. Participating in data science competitions has been one of the best approaches to help beginners in data science get more experience and finally apply for job opportunities.

38. Scikit Learn 1.0: New Features in Python Machine Learning Library

Scikit-learn is the most popular open-source and free python machine learning library for Data scientists and Machine learning practitioners. The scikit-learn library contains a lot of efficient tools for machine learning and statistical modeling including classification, regression, clustering, and dimensionality reduction.

39. How to Build a Sentiment Analysis App Using Gradio and Hugging Face

In this article, we are going to create an end-to-end AI Sentiment Analysis web application using Gradio and hugging face transformers.

40. Software Engineering Best Practices Collection for Machine Learning

An ever-increasing number of organizations are developing applications that involve machine learning components. The complexity and diversity of these applications calls for software engineering techniques to ensure that they are built in a robust and future-proof manner.

41. Fine-Tuning Machine Learning Models with DVC Experiments for Transfer Learning

You can work with pretrained models and fine-tune them with DVC experiments.

42. 70-Page Report on the COCO Dataset and Object Detection [Part 2]

This blog is part 1 of (and contains a link to) a 70+ page report was created to quickly find data resources and/or assets for a given dataset and a specific ta

43. How Machines Learn Emotions: Sentiment Analysis of Amazon Product Reviews

How do you train machines to identify emotions? This is a tutorial for sentiment analysis of Amazon product reviews using machine learning algorithms.

44. A Data Scientist's Guide to Semi-Supervised Learning

Semi-supervised learning is the type of machine learning that is not commonly talked about by data science and machine learning practitioners but still has a very important role to play.

45. Improve Machine Learning Model Performance by Combining Categorical Features

Learn how to combine categorical features in your dataset to improve your machine learning model performance.

46. 10 Best + Free Machine Learning Courses Collection

Here's a compilation of some of the best + free machine learning courses available online.

47. How To Use Microsoft Excel To Classify Your Data

An accessible introduction to ML - no programming or math required. By the end of this tutorial, you’ll have implemented your first algorithm without touching a single line of code. You’ll use Machine Learning techniques to classify real data using basic functions in Excel. You don’t have to be a genius or a programmer to understand machine learning. Despite the popularized applications of self-driving cars, killer robots, and facial recognition, the foundations of machine learning (ML) are quite simple. This is a chance to get your feet wet and understand the power of these new techniques.

48. Top 12 Javascript Libraries for Machine Learning

Rapidly evolving technologies like Machine Learning, Artificial Intelligence, and Data Science were undoubtedly among the most booming technologies of this decade. The s specifically focusses on Machine Learning which, in general, helped improve productivity across several sectors of the industry by more than 40%. It is a no-brainer that Machine Learning jobs are among the most sought-after jobs in the industry.

49. Encoding Categorical Data for ML Algorithms

Encoding is a technique used to convert categorical data to numerical representations to be able to use the data in machine learning algorithms.

50. Scikit-Learn 0.24: Top 5 New Features

For any data scientists & machine-learning engineers use scikit-learn for different machine learning projects here are 5 best new features in scikit-learn 0.24

51. The Hundred-Page Machine Learning Book [Review]

I first ordered The Hundred-Page Machine Learning book back in May and am only just now finishing it up. In COVID-time, that was about 10 years ago. As you might have inferred, this book is NOT a quick read. What it lacks in easy reading, it makes up for in efficiency. This book swallows up the heavyweight mathematics textbooks and spits out a slim product no thicker than the width of my smartphone. From page one all the way to page 136, Andriy Burkov, the author, does not waste a single word in distilling the most practical concepts in machine learning. You read that right. It is MORE than 100 pages! Sounds like the book has some bias. Get it? Now get ready for my hundred-page book review. Just kidding.

52. Build A Commission-Free Algo Trading Bot By Machine Learning Quarterly Earnings Reports [Full Guide]

Introduction

53. Top 20 ML Stories For Data Science

Data Science is undoubtedly one of the main fields that every AI, ML, or data science enthusiast crosses paths with. Now with the advancement of data science, it is not just restricted to refine the data and then put it on the board. It is combined with Machine Learning that makes your machines smart by using the data that you just optimized to feed the machine.

54. How to Deploy Machine Learning Models to the Cloud Quickly and Easily

Machine learning models are usually developed in a training environment (online or offline). And you can then deploy them and use them with live data.

55. Credit Card Fraud Detection via Machine Learning: A Case Study

A machine learning guide on how to identify fraudulent credit card transactions by using the PyOD toolkit.

56. [Hacking Tinder] Train an AI to Auto-Swipe for You 🖖

Auto-tinder was created to train an AI using Tensorflow and Python3 that learns your interests in the other sex and automatically plays the tinder swiping-game for you.

57. Building Machine Learning Models With TensorFlow

In this article, I will share with you some useful tips and guidelines that you can use to better build better deep learning models.

58. 70-Page Report on the COCO Dataset and Object Detection [Part 3]

59. [Explained] Machine Learning Fundamentals: Optimization Problems and How to Solve Them

If you start to look into machine learning and the math behind it, you will quickly notice that everything comes down to an optimization problem. Even the training of neural networks is basically just finding the optimal parameter configuration for a really high dimensional function.

60. The Full Story behind Convolutional Neural Networks and the Math Behind it

Convolutional Neural Networks became really popular after 2010 because they outperformed any other network architecture on visual data, but the concept behind CNN is not new. In fact, it is very much inspired by the human visual system. In this article, I aim to explain in very details how researchers came up with the idea of CNN, how they are structured, how the math behind them works and what techniques are applied to improve their performance.