109 Best Resources to Learn Machine Learning Online in 2024

Best Resources to Learn Machine Learning Online in 2021

Machine Learning is very powerful and popular. Many people are shifting their careers into the Machine learning field. But when it comes to learning machine learning, most of us are stuck and don’t know where to learn. That’s why I thought to collect and combine all the best resources to learn machine learning online.

So give your few minutes and find out the best resources to learn machine learning. You can bookmark this article so that you can refer to this article later.

Now without further ado, let’s get started-

Best Resources to Learn Machine Learning Online

Before discussing the resources, I would like to tell you what topics or skills you need to learn for Machine Learning-

Skills Required for Machine Learning

1. Programming Language

Knowledge of Programming language is compulsory for machine learning. And the most popular programming languages are Python, R, Java, and C++. But as a beginner, you can start with Python.

2. Mathematics Skill

Knowledge of Mathematics is very important to understand how machine learning and its algorithms work. In math, the most important topics are-

  • Probability and Statistics
  • Linear Algebra
  • Calculus

Now, let’s have a detailed look at all of them-

a). Probability and Statistics

Probability and statistics are used in Bayes’ Theorem, Probability Distribution, Sampling, and Hypothesis Testing.

b). Linear Algebra

Linear Algebra has two important terms- Matrices and Vectors. They are both used widely in Machine Learning. Matrices are used in Image Recognition.

c). Calculus

In Calculus, you have Differential Calculus and Integral Calculus. These terms help you to determine the probability of events. For example, finding the posterior probability in the Naive Bayes model.

3. Machine Learning Algorithms

You should know Machine Learning Algorithms like-

4. Machine Learning Frameworks

Machine Learning Frameworks make the life of developers and machine learning engineers a whole lot easier. ML Frameworks remove the complex part of machine learning and make it available for everyone who wants to use it.

These are some widely used Machine Learning Frameworks-

  • TensorFlow.
  • Theano.
  • scikit learn.
  • PyTorch.
  • Keras.
  • DL4J.
  • Caffe.
  • Microsoft Cognitive Toolkit.

5. Data Engineering Skills

For building a machine learning model, you need data for training and testing. That’s why knowledge of data engineering is important. Data Engineering contains 3 basic steps-

  • Data pre-processing- Data pre-processing step is performed before you process the data. Data pre-processing steps are cleaning, parsing, correcting, and consolidating the data.
  • ETL (Extract, Transform, and Load)- In this step, you need to perform extraction of data from the internet or local server, then transform the data into a suitable format, and after that load the data into your program. That’s why you should have knowledge of ETL so that you can perform these steps easily.
  • Knowledge of Database- You should be familiar with DBMS like SQL, Oracle Database, and No SQL.

6. Deep Learning Algorithms

Deep learning is the subpart of machine learning. And it is much more powerful than machine learning. Deep learning is getting more interest nowadays. That’s why you should be familiar with Deep Learning Algorithms.

The most used Deep Learning Algorithms are-

  1. Feedforward Neural Network.
  2. Backpropagation.
  3. Convolutional Neural Network.
  4. Recurrent Neural Network.
  5. Generative Adversarial Networks (GAN).

So, these are some must-have skills for Machine Learning, now let’s move to the best resources to learn machine learning online.

Resources to Learn ML-

For your convenience, I have created separate tables for each resource. So let’s start with online courses-

Online Courses

TopicsOnline Courses
1. Programming Language (Python & R)1. Introduction to Python Programming– Udacity
2. Python for Everybody– University of Michigan
3. Introduction To Python Programming– Udemy
4. Python Core and Advanced– Udemy
5. Crash Course on Python– Google
6. Python for Absolute Beginners!– Udemy
7. Python 3 Programming Specialization– University of Michigan
8. R Programming – Johns Hopkins University
9. Programming for Data Science with R– Udacity
10. R Programming A-Z™– Udemy
2. Mathematics1. Mathematics for Machine Learning SpecializationImperial College London
2. Mathematics for Data Science SpecializationCoursera
3. Data Science Math Skills– Duke University
4. Intro to Statistics– Udacity
5. Probability – The Science of Uncertainty and Data– MITx
6. Basic Statistics– University of Amsterdam
7. Probabilistic Graphical Models Specialization– Stanford University

8. Introduction to Calculus– The University of Sydney
9. Probability and Statistics– University of London
3. Machine Learning Algorithms1. Become a Machine Learning Engineer (Udacity)
2. Machine LearningStanford University
3. Machine Learning with PythonIBM
4. Intro to Machine Learning with TensorFlow (Udacity)
5. Machine Learning A-Z™: Hands-On Python & R In Data Science -Udemy
6. Python for Data Science and Machine Learning Bootcamp– Udemy
7. Advanced Machine Learning SpecializationCoursera
4. TensorFlow1. TensorFlow in Practice Specialization– deeplearning.ai
2. Intro to Machine Learning with TensorFlow– Udacity
3. Tensorflow 2.0: Deep Learning and Artificial Intelligence– Udemy
4. TensorFlow: Data and Deployment Specialization– deeplearning.ai
5. Machine Learning with TensorFlow on Google Cloud Platform Specialization– Google Cloud
5. Data Preprocessing1. Applied Data Science with Python Specialization by the University of Michigan
2. Exploratory Data Analysis With Python and Pandas (Guided Project)
3. NumPy Tutorial by freeCodeCamp
6. Deep Learning1. Deep Learning (Udacity)
2. Deep Learning Specialization (deeplearning.ai)
3. AI & Deep Learning with TensorFlow (Edureka)
4. Deep Learning A-Z™: Hands-On Artificial Neural Networks Udemy

Text Books

TopicsText Books
Programming Language (Python & R)1. Python Crash Course by Eric Matthes
Buy on Amazon or download PDF from here.

2. Head First Python: A Brain-Friendly Guide by Paul Barry
Buy on Amazon or download PDF from here.

3. Learn Python the Hard Way by Zed A. Shaw 
Buy on Amazon or download PDF from here.

4. Automate the Boring Stuff with Python by Al Sweigart 
Buy on Amazon or download PDF from here.

5. R for Data Science by Hadley Wickham
Buy on Amazon or download PDF from here.

6. Machine Learning with R by Brett Lantz 
Buy on Amazon

7. The Book of R: A First Course in Programming and Statistics by Tilman M. Davies
Buy on Amazon or download the PDF from here.
Mathematics1. An Introduction to Statistical Learning by Gareth James, Daniela Witten, Trevor Hastie, and Robert Tibshirani
Buy this book on Amazon-An Introduction to Statistical Learning
You can download the pdf version of this book from here.

2. Practical Statistics for Data Scientists by Peter Bruce
Buy this book on Amazon-Practical Statistics for Data Scientists
You can download the pdf version of this book from here.

3. Probability and Statistics for Data Science by Norman Matloff
Buy this book on Amazon-Probability and Statistics for Data Science.

4. Introduction to Probability by Joseph K. Blitzstein, Jessica Hwang
Buy this book on Amazon- Introduction to Probability.
You can download the pdf version of this book from here.

5. Mathematics for Machine Learning by Marc Peter Deisenroth
Buy on Amazon or download PDF from here.

6. Linear Algebra and Optimization for Machine Learning by
Charu C. Aggarwal
Buy on Amazon or check the table of content from here.
3. Machine Learning Algorithms1. Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow by Aurélien Géron
Buy on Amazon or download from here.

2. The Hundred-Page Machine Learning Book by Andriy Burkov 
Buy on Amazon or download from here.

3. Machine Learning For Absolute Beginners by  Oliver Theobald
Buy on Amazon or download from here.

4. Machine Learning: An Applied Mathematics Introduction by Paul Wilmott
Buy on Amazon
4. TensorFlow1. TinyML: Machine Learning with TensorFlow Lite on Arduino and Ultra-Low-Power Microcontrollers by Pete Warden
Buy on Amazon

2. Adopting TensorFlow for Real-World AI by Mr. Naresh R. Jasotani
Buy on Amazon

3. Advanced Deep Learning with TensorFlow 2 and Keras by Rowel Atienza
Buy on Amazon
5. Deep Learning1. Deep Learning (Adaptive Computation and Machine Learning series) by  Ian Goodfellow
Buy on Amazon or download from here.

2. Deep Learning with Python by Francois Chollet
Buy on Amazon or download from here.

3. Neural Networks and Deep Learning by Charu C. Aggarwal 
Buy on Amazon or download from here. 

4. Deep Learning: A Practitioner’s Approach by Adam Gibson and Josh Patterson’s
Buy on Amazon or download from here. 


1. Programming Language (Python & R)1. The Python Tutorial (PYTHON.ORG)
2. Python 3 Tutorial (SOLOLEARN)
3. Python Tutorial- MLTUT
5. Google’s Python Class
7. Python Tutorial- Tutorials Point
8. Python Tutorial W3Schools
9. R Tutorial- Tutorials Point
10. R Tutorial- Statmethods
2. Mathematics1. Statistics and probability– Khan Academy
2. Probability on Khan Academy
3. Statistics – Probability (TutorialsPoint)
4. Probability Tutorial (Stat Trek)
5. Probability and Statistics (MathisFun)
6. Probability theory (Wikipedia)
3. Machine Learning Algorithms1. Machine Learning with Python Tutorial- Tutorials Point
2. Machine Learning Basics– MLTUT
3. Machine Learning Tutorial- Javatpoint
4. Machine LearningGeeksforGeeks
4. TensorFlow1. TensorFlow CoreTensorFlow org
2. TensorFlow Tutorial- Tutorials Point
3. Introduction to Deep Learning with TensorFlowPythonProgramming
5. Deep Learning1. Deep Learning Basics– MLTUT
2. Python Deep Learning Tutorial- Tutorials Point
3. Deep Learning TutorialJavatpoint

YouTube Videos

TopicsYouTube Videos
1. Programming Languages (Python & R)1. CS DOJO
2. Programming with Mosh
3. Telusko
4. Clever Programmer
5. Corey Schafer
6. R Programming TutorialfreeCodeCamp.org
7. R Programming Full CourseSimplilearn
2. Mathematics1. Statistics for Data ScienceGreat Learning
2. Mathematics for Machine Learning [Full Course]Edureka
3. Mathematics For Machine Learning- Simplilearn
4. Mathematics for Machine LearningMy CS
3. Machine Learning Algorithms1. Machine Learning with PythonGreat Learning
2. Machine Learning Tutorial Pythoncodebasics
3. Python Machine Learning Tutorial- Programming with Mosh
4. Machine Learning by Krish Naik
4. TensorFlow1. TensorFlow 2.0 Complete CoursefreeCodeCamp.org
2. TensorFlow Tutorial- Aladdin Persson
3. Coding TensorFlowTensorFlow
5. Deep Learning1. Complete Deep LearningKrish Naik
2. Deep Learning With Tensorflow 2.0, Keras and Python codebasics
3. Deep learning TutorialGreat Learning

And here the list ends. I hope these resources will help you to learn and master machine learning. I would suggest you bookmark this article for future referrals.

What does Machine Learning Engineer do?

Machine Learning work with the following steps-

  1. Data Collection.
  2. Data Preprocessing.
  3. Choose a Machine Learning Algorithm.
  4. Training the Model.
  5. Testing the Model.
  6. Tuning the Model.

So, as a machine learning engineer, you have to perform all these steps.

Machine Learning Engineers create a Machine Learning model that can work properly with the best performance. Machine Learning Engineers have to choose the right algorithms as per model compatibility and requirement.

They have to extract ideas from the data science team, choose appropriate tools and ecosystems, Use machine Learning frameworks, and stay up to date with the latest development.

Now, let’s see the Roles and Responsibilities of Machine Learning Engineers-

Roles and Responsibilities of Machine Learning Engineer

  • Study and convert Data Science Prototypes.
  • Build Machine Learning models.
  • Research and apply appropriate Machine Learning tools and algorithms.
  • Build a Machine Learning application based on industry requirements.
  • Choose correct datasets and data visualization methods.
  • Conduct Machine Learning tests and experiments.
  • Execute Statistical Analysis and fine-tuning with the help of test results. (Statistical Analysis is a small part of ML Engineers whereas it’s a major job part of Data Analyst).
  • Train and Retrain the model based on model accuracy.
  • Stay updated with the latest development in the field.

So, these are the Roles and responsibilities of the Machine Learning Engineer.

Now it’s time to wrap up this article “Best Resources to Learn Machine Learning Online“.


In this article, I tried to cover all the Best Resources to Learn Machine Learning Online from online courses to YouTube videos. If you have any doubts or questions, feel free to ask me in the comment section.

All the Best!

Enjoy Learning!

Thank YOU!

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Though of the Day…

Anyone who stops learning is old, whether at twenty or eighty. Anyone who keeps learning stays young.

– Henry Ford
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Written By Aqsa Zafar

Founder of MLTUT, Machine Learning Ph.D. scholar at Dayananda Sagar University. Research on social media depression detection. Create tutorials on ML and data science for diverse applications. Passionate about sharing knowledge through website and social media.

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