5 Free Machine Learning Courses from Top Universities

 If you’re reading this article, I assume you already know what machine learning is. But just for a quick refresher, it’s simply making computers smart enough to do jobs that humans used to do, for example, taking attendance using facial recognition. Anyway, moving on to our main discussion, I know there are a lot of resources available regarding ML, but the problem is finding the right and high-quality resources. I think we can all agree that university-level courses will indeed be better in terms of comprehensiveness and quality. Therefore, in this article, I will be sharing my personal favorite machine learning courses from top universities.

1. CS229: Machine Learning by Stanford

Link To Access Material: CS229: Machine Learning Material

Youtube Link: CS229: Machine Learning Videos

This course has various versions and is taught by different instructors, but the one I pointed out is taught by Andrew Ng – hands down, one of the best machine learning instructors. This course provides a broad introduction to machine learning and statistical pattern recognition. It covers various topics like supervised learning, unsupervised learning, learning theory, reinforcement learning, and control. You can access all the material online.

Course Outline

• Linear Regression and Logistic Regression
• Perceptron & Generalized Linear Model
• Naive Bayes, Support Vector Machines and Kernels
• Data Splits, Models & Cross-Validation
• Decision Trees and Ensemble Methods
• Neural Networks, Backpropagation, and Improving Neural Networks
• Debugging ML Models and Error Analysis
• Expectation-Maximization Algorithms, EM Algorithm & Factor Analysis
• Independent Component Analysis & Reinforcement Learning
• Continuous State MDP, Reward Model, and RL Debugging

2. Machine Learning with Python: From Linear Models to Deep Learning by MIT

Link: MIT Machine Learning Course 

This course has a 4.1 rating on edX and is a fantastic introductory course that covers a wide range of machine learning topics from deep learning and reinforcement learning, through hands-on Python projects. If you are new to machine learning, going in-depth on a specific topic might be frustrating, and it’s better to go through courses with a wider breadth to see what part of ML excites you. Now, ML itself is considered a very broad category, and learning everything in-depth will exhaust you.

Course Outline

• Understanding the principles behind machine learning problems such as classification, regression, clustering, and reinforcement learning
• Implementing and analyzing models such as linear models, kernel machines, neural networks, and graphical models
• Learning how to choose suitable models for different applications
• Implementing and organizing machine learning projects, from training, validation, and parameter tuning, to feature engineering.

3. Data Science: Machine Learning by Harvard

Link: Data Science: Machine Learning Course

This course is actually a part of the Professional Certificate Program in Data Science (Certification is paid, but this part is available for free). This project walks you through all the skills that are fundamental to machine learning by building a movie recommendation system. So, if you like to learn by practically implementing things, then my suggestion would be to go for this course.

Course Outline

• Basics of machine learning
• How to perform cross-validation to avoid overtraining
• Popular machine learning algorithms
• How to build a recommendation system
• What is regularization and why it is useful?

4. Machine Learning by Carnegie Mellon University

Link: CMU Machine Learning Course

If you try to explore some of the famous ML books, then I can guarantee that you will find Machine Learning, Tom Mitchell, McGraw Hill, 1997. on this list. What could be more exciting than learning a course from this book’s instructor? This course not only covers theory but also provides a practical touch to machine learning. From machine learning algorithms, the math behind them, right tools and technologies, it has got you covered.

Course Outline

• Probability and Estimation
• Decision trees, Naive Bayes, linear and logistic regression
• Learning Theory and Graphical Models
• Boosting, kernels, SVM, Margins
• Semi-supervised and Active Learning
• Neural Networks and Deep Learning
• Reinforcement Learning and Discussion on the Future of ML

5. Mathematics for Machine Learning Specialization by Imperial College London

Official Link: Mathematics for Machine Learning – Coursera

Youtube Link: Mathematics for Machine Learning – (3 Courses in 1 Video)

Last but not least, I would like to recommend a course that is crucial for anyone looking to make a career in ML. Mathematics plays a huge role in machine learning and having a good grasp of the mathematical principles involved is key, to interpreting the outcomes produced by ML algorithms. Out of all the ML maths-related courses, the “Mathematics for Machine Learning” specialization on Coursera is my top pick. The specialization consists of three courses: Linear Algebra, Multivariate Calculus, and Principal Component Analysis. Each course spans 4-6 weeks and covers the foundational mathematical concepts required to understand machine learning algorithms.

Course Outline

• Linear Algebra: Vectors, Matrices, Dot Product, Basis Vectors, Changing the Basis Vectors, Eigenvalues, and Eigenvectors
• Multivariate Calculus: Single Variable Differentiation, Multivariable Differentiation, Chain Rule, Jacobians, Hessians, Lagrange Multipliers, and Taylor Series
• Principal Component Analysis: Statistics, Dot and Orthogonality between Vectors, Projection in Matrices, Multivariate Calculus Concepts, and Applications of PCA

Wrapping Up

So there you have it – a collection of some of the best machine-learning courses from top universities around the world. If you’re interested in exploring some YouTube channels to learn machine learning, you can check out this article of mine:

Top 15 YouTube Channels to Level Up Your Machine Learning Skills