Here are 10 free machine learning courses. I spent a lot of time researching the best free ML courses. Most of the free online ML courses suggested on the web are the list of courses from various providers or Universities. But I listed the courses according to their level from beginner to advanced.
Here are the courses:
Beginner
# 1. Intro to Machine Learning (Kaggle)
# 2. AI for Everyone (Stanford University)
# 3. Machine Learning (Stanford University)
Intermediate
# 4. Machine Learning with Python (IBM)
# 5. Machine Learning (Columbia University)
# 6. Introduction to Machine Learning for Coders (University of San Francisco)
# 7. Introduction to Machine Learning (MIT)
# 8. Introduction to Machine Learning (Duke University)
Advanced
# 9. Advanced Machine Learning Specialization (HSE University)
# 10. Machine Learning with Python: From Linear Models to Deep Learning (MIT)
Beginner
# 1. Intro to Machine Learning (Kaggle)
- Provider: Kaggle
- Instructor: Dan Becker
- Founder at decision.ai
- Formerly data scientist at Google
- Consulting for 6 companies in the Fortune 100
- Covered Topics
- How models work
- Basic data exploration
- Your first machine-learning model
- Model validation
- Underfitting and overfitting
- Random forests
- Machine learning competitions
- Intro to AutoML
- Best review
“It was helpful to practice with pandas by creating and using data structures for analysis. I have already requested this library to use at work. You can load a table into a DataFrame using pandas and it retains its column, row structure.” -Kristen Smith - You can start now! Visit Here
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# 2. AI for Everyone (Stanford University)
- Provider: Stanford University (Coursera)
- Instructor: Andrew Ng
- Adjunct professor at Stanford University
- Founder of DeepLearning.AI
- General partner at AI Fund
- Co-founder of Coursera
- Covered Topics
- The meaning behind common AI terminology, including neural networks, machine learning, deep learning, and data science
- What AI realistically can–and cannot–do
- How to spot opportunities to apply AI to problems in your organization
- What it feels like to build machine learning and data science projects
- How to work with an AI team and build an AI strategy in your company
- How to navigate ethical and societal discussions surrounding AI
- Best review
“Brilliantly delivered, contains all the most important stuff to help practitioners (not AI experts) orient themselves. I watch a video or two every day, and I’m learning. Enjoyable and useful.” -Marcus B. - You can start now! Visit Here
If you don’t know how to take a course for free in COURSERA, check out our guide on Guide for the Coursera Free Course!
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# 3. Machine Learning (Stanford University)
- Provider: Stanford University (Coursera)
- Instructor: Andrew Ng
- Adjunct professor at Stanford University
- Founder of DeepLearning.AI
- General partner at AI Fund
- Co-founder of Coursera
- Covered Topics
- Supervised learning (parametric/non-parametric algorithms, support vector machines, kernels, neural networks)
- Unsupervised learning (clustering, dimensionality reduction, recommender systems, deep learning)
- Best practices in machine learning (bias/variance theory; innovation process in machine learning and AI)
- Best review
“I’ve never expected much from an online course, but this one is just Great! Even if you feel like you have gaps in your calculus/linear algebra training don’t be afraid to take it, because you’ll be able to fill most of those right from the course material or at least figure out where to look. This course gives a grand picture of how ML stuff works without focusing much on the specific components like programming language/libraries/environment which most of ML courses/articles suffer from.” -Vasily. - You can start now! Visit Here!
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Intermediate
# 4. Machine Learning with Python (IBM)
- Provider: IBM (Coursera)
- Instructor: Saeed Aghabozorgi and Joseph Santarcangelo
- Saeed Aghabozorgi
- Ph.D. and Sr. Data Scientist in IBM
- Expert in developing advanced analytic methods of machine learning
- Joseph Santarcangelo
- Ph.D. in Electrical Engineering
- Expert in machine learning and computer vision
- Saeed Aghabozorgi
- Covered Topics
- General overview of Machine Learning topics such as supervised vs unsupervised learning, the usage of each algorithm, and the advantage of using Python libraries for implementing Machine Learning models
- Linear, Non-linear, Simple, and Multiple regression, and their applications
- Practice with different classification algorithms, such as KNN, Decision Trees, Logistic Regression, and SVM
- How to use clustering for customer segmentation, grouping same vehicles, and also clustering of weather stations
- Best review
“This was my favorite course in the specialization and hence the only one that gets my 5* rating! Everything was well explained and thorough meaning I did not get lost. The quizzes were challenging but fair. The final project was spot on and related perfectly with what has been learned (unlike many other final projects in this specialization). Overall a very good experience.” -Karim C N - You can start now! Visit Here
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# 5. Machine Learning (Columbia University)
* This course currently may be unavailable!
- Provider: Columbia University (EdX)
- Instructor: John W. Paisley
- Ph.D. in Electrical and Computer Engineering from Duke University
- Assistant Professor in the Department of Electrical Engineering at Columbia University
- Expert in statistical machine learning
- Covered Topics
- Supervised learning techniques for regression and classification
- Unsupervised learning techniques for data modeling and analysis
- Probabilistic versus non-probabilistic viewpoints
- Optimization and inference algorithms for model learning
- Best review
“There are not many courses online that provide such in-depth learning experience in Machine Learning. This course goes into some details and mathematics of the algorithms being used. It demands a good amount of time every week to understand and apply all that is being taught but that is what makes it good. It is not like many other courses that you can take and pass with minimal effort but at the end of it, it is worth spending time taking this course.” -Kush Kulshrestha - You can start now! Visit Here
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# 6. Introduction to Machine Learning for Coders (University of San Francisco)
- Provider: University of San Francisco (fast.ai)
- Instructor: Jeremy Howard
- Distinguished research scientist at the University of San Francisco
- A faculty member at Singularity University, and a Young Global Leader with the World Economic Forum
- Founding researcher at fast.ai
- Covered Topics
- Introduction to random forests
- Random forest deep dive
- Performance, Validation, and Model interpretation
- Extrapolation and RF from scratch
- Data products and live coding
- RF from scratch and gradient descent
- Gradient descent and logistic regression
- Regularization, Learning rates, and NLP regularization
- More NLP and columnar data
- Embeddings
- Complete Rossmann, Ethical issue
- Best review
“This is a fantastic hands-on learning experience. Like many data professionals outside of academia, I found deep learning to be intimidating and opaque. This class changed that and empowered me to make deep learning part of the toolkit I use at Udemy. While there are a lot of resources available online about the theoretical underpinnings of deep learning this is the only course I have found that guides students through the implementation of fundamental deep learning frameworks.” -Sara Hooker, Data Scientist, Udemy - You can start now! Visit Here
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# 7. Introduction to Machine Learning (MIT)
- Provider: MIT Open Learning
- Instructor: Leslie Kaelbling, Tomas Lozano-Perez, Issac Chuang, Duane Boning
- Ph. D. in Computer Science from Stanford University
- Professor of Computer Science and Engineering at MIT
- Covered Topics
- Introduces principles, algorithms, and applications of machine learning from the point of view of modeling and prediction
- Formulation of learning problems and concepts of representation, over-fitting, and generalization
- Exercise in supervised learning and reinforcement learning, with applications to images and temporal sequences
- You can start now! Visit Here!
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# 8. Introduction to Machine Learning (Duke University)
- Provider: Duke University (Coursera)
- Instructor: Lawrence Carin, David Carlson, Timothy Dunn, Kevin Liang
- Distinguished Professor of Electrical and Computer Engineering at Duke
- Vice Provost for Research at Duke
- One of the most prolific authors in the world in the fields of machine learning and artificial intelligence
- Covered Topics
- Simple Introduction to Machine Learning
- Basics of Model Learning
- Image Analysis with Convolutional Neural Networks
- Recurrent Neural Networks for Natural Language Processing
- Best review
“Excellent course. Concepts such as gradient descent and convolutions as they pertain to neural networks are explained without going into the mathematical details but, in my opinion, are explained more intuitively and better, as compared to most other courses. The course does include some ungraded Jupyter notebooks exemplifying key elements of deep learning networks. Highly recommended to ‘cement’ understanding of neural networks.” -Michael B - You can start now! Visit Here!
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Advanced
# 9. Advanced Machine Learning Specialization (HSE University)
- Provider: HSE University (Coursera)
- Instructor: Evgeny Sokolov +20 more instructors
- Deputy head of the Big Data and Information Retrieval department at the Higher School of Economics – one of Russia’s top universities,
- Covered Topics
- Introduction to Deep Learning
- How to Win a Data Science Competition: Learn from Top Kagglers
- Bayesian Methods for Machine Learning
- Practical Reinforcement Learning
- Best review
“one of the best courses I have attended. clear explanation, clear examples, amazing quizzes & Programming Assignment this course is an advanced level, don’t enroll it if you are a new starter.” -Anas K - You can start now! Visit Here!
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# 10. Machine Learning with Python: From Linear Models to Deep Learning (MIT)
- Provider: MIT (EdX)
- Instructor: Regina Barzilay, Tommi Jaakkola, Karen Chu
- Delta Electronics Professor in the Department of Electrical Engineering Computer Science
- Member of the Computer Science and Artificial Intelligence Laboratory at the MIT
- Covered Topics
- Linear classifiers, separability, perceptron algorithm
- Maximum margin hyperplane, loss, regularization
- Stochastic gradient descent, over-fitting, generalization
- Linear regression
- Recommender problems, collaborative filtering
- Non-linear classification, kernels
- Learning features, Neural networks
- Deep learning, backpropagation
- Recurrent neural networks
- Recurrent neural networks
- Generalization, complexity, VC-dimension
- Unsupervised learning: clustering
- Generative models, mixtures
- Mixtures and the EM algorithm
- Learning to control: Reinforcement learning
- Reinforcement learning continued
- Applications: Natural Language Processing
- Projects
- Automatic Review Analyzer
- Digit Recognition with Neural Networks
- Reinforcement Learning
- Best review
“Great course. Very well structured. The course is divided into 5 units each with its exercises, homework and a project to be coded in Python. The exercises can be solved pretty straightforwardly from the content of the lectures and some googling. (Effort: 5 to 10 hours a week) Homeworks are a bit more challenging and require more research (Effort: 8 to 10 hours a week) Projects are great, very challenging but extremely rewarding. You will need a basic course in Python. (Effort: 14 hours a week on average) In general, you will need a solid background in calculus and linear algebra. There is no textbook but most of the concepts are available online and there is a lot of information.” -Fabian Pachano - You can start now! Visit Here!
Conclusion
The listed courses above would help you to get started in the fastest-growing industry for your development. All these courses are free but the only problem is your commitment to pursue them. Invest your time and energy for your future, then you will get a better position to see more.
