10 Free Machine Learning Courses from Top Universities

Learn the basics of machine learning, including classification, SVM, decision tree learning, neural networks, convolutional, neural networks, boosting, and K nearest neighbors.



Machine learning is a rapidly growing field that is revolutionizing many industries, including healthcare, finance, and technology. With its ability to analyze large amounts of data and make predictions and decisions, machine learning is an essential skill for anyone interested in a career in data science or artificial intelligence.

If you’re looking to learn more about machine learning, you’re in luck! There are many high-quality courses available online, offered by some of the top universities in the world. In this article, we’ll introduce you to 10 free machine learning courses from top universities. These courses cover various topics, from the basics of machine learning to more advanced techniques, and are suitable for learners at all levels. Whether you’re a beginner looking to get started in machine learning or an experienced data scientist looking to deepen your knowledge, you’re sure to find something of interest in this list. So, let’s get started!

 

10 Free Machine Learning Courses from Top Universities
Photo by Datingscout on Unsplash

 

1. Introduction to Machine Learning - UC Berkeley

 

Course Link: https://lnkd.in/dChzX6dZ

The first course is the introduction to machine learning course by UC Berkeley. This course is a very good introduction to the field of machine learning, especially for beginners. It covers the most important machine learning algorithms for each machine learning task such as:

  • Classification: Support vector machines (SVMs), Gaussian discriminant analysis ( linear discriminant analysis, LDA, and quadratic discriminant analysis, QDA), logistic regression, decision trees, neural networks, convolutional neural networks, boosting, and K nearest neighbor.
  • Regression: least-squares linear regression, logistic regression, polynomial regression, ridge regression, Lasso.
  • clustering: k-means clustering, hierarchical clustering, spectral graph clustering.

If you are a beginner and would like to build a solid foundation in the basics of machine learning concepts. This course will be a perfect choice. 

Estimated duration: 30 hours

Lecturer: Jonathan Shewchuk

Difficulty level: Beginner

Course material:

 

2. Introduction to Machine Learning - Carnegie Mellon University

 

Course Link: https://lnkd.in/dH8ktatw

The second course is also an introductory machine learning course by Carnegie Mellon University. This course covers more machine learning algorithms in both theoretical and practical ways. The course covers the most important machine learning algorithms such as Bayesian networks, decision tree learning, SVM, statistical learning methods, unsupervised learning algorithms, introduction to deep learning, and reinforcement learning. 

In addition to that the course also covers important concepts such as the PAC learning framework, Bayesian learning methods, margin-based learning, and Occam’s Razor. 

This course is designed to give you a thorough grounding in the methodologies, technologies, mathematics, and algorithms that are currently needed by people who do research or work in machine learning.

Estimated duration: 50 hours

Lecturer: Tom Mitchell & Maria-Florina Balcan

Difficulty level: Beginner

Course material:

 

3. Machine Learning - Stanford University

 

Course Link: https://lnkd.in/d4FzSKpJ

The third course is the famous Andrew NG’s Machine Learning course taught at Stanford. This course focuses both on theoretical and practical machine learning techniques. You will not only understand the most important machine learning algorithms but you will also learn how to build and implement them from scratch. Finally, you’ll learn about some of the industry's best practices in innovation as it pertains to machine learning and AI.

NOTE: There is a new version of this course that is available on Coursera taught also by Andrew NG. You can find it here.

Estimated duration: 60 hours

Lecturer: Andrew Ng

Difficulty level: Beginner

Course material:

 

4. Machine Learning & Data Mining - Caltech

 

Course Link: https://lnkd.in/dUhbEyBx

The fourth course is the Machine Learning & Data Mining course from Caltech. This course covers the most popular methods in machine learning and data mining with more focus on developing a solid understanding of how to apply these methods in practice. In addition to that, it also covers some of the recent research developments such as deep generative models.

Estimated duration: 30 hours

Lecturer: Yisong Yue

Difficulty level:

Course material:

 

5. Learning from Data - Caltech

 

Course Link: https://lnkd.in/d4zZZJ5h

The fifth course on this list is the Learning from Data course by Caltech. This course focuses more on learning theory in a story-like fashion and covers topics such as what is learning and can a machine learn and how. It also balances theory and practice and also covers the important mathematical foundations for machine learning. 

Estimated duration: 30 hours

Lecturer: Professor Yasser Abu-Mostafa

Difficulty level: Beginner

Course material:

 

6. Machine Learning for Intelligent Systems - Cornell University

 

Course Link: https://lnkd.in/dtSjQ22i

The sixth course on this list is the Machine Learning for Intelligent Systems course from Cornell University. This course will provide a broad introduction to the field of machine learning and will introduce you to the most important machine learning algorithms and concepts to start your machine learning journey. 

Estimated duration: 30 hours

Lecturer: Kilian Weinberger

Difficulty level: Beginner

Course material:

 

7. Large Scale Machine Learning - University of Toronto

 

Course Link: https://lnkd.in/dv8-7EFE

The seventh course on our list is the Large Scale Machine Learning Course by the University of Toronto. This course is more advanced and is designed for graduate students who have a reasonable degree in mathematical maturity. The course starts with basic machine learning methods such as linear methods for regression and classification and then it dives more into statistical machine learning methods such as Bayesian networks, Markov random fields, and more advanced methods.

Estimated duration: 20 hours

Lecturer: Russ Salakhutdinov

Difficulty level: Advanced 

Course material:

 

8. Machine Learning with Large Datasets - Carnegie Mellon University

 

Course Link: https://www.youtube.com/@user-yd6im1cq5k/about

The eighth course on this list is the Machine Learning with Large Datasets course from Carnegie Mellon University. This course approaches a similar problem to the previous course but in a more profound way. It focuses on how to build machine learning systems that can handle large datasets. Working with large datasets is difficult for several reasons such as: 

  • They are computationally expensive to process and train models on them 
  • It is difficult to visualize and understand it
  • Large datasets display different behaviors in terms of which learning methods produce the most accurate predictions. 

Based on this dealing with large datasets require different scalable learning techniques which include:

  • Streaming learning techniques 
  • parallel infrastructure such as map-reduce
  • Feature hashing and Bloom filters for reducing memory requirements for learning methods. 

Estimated duration: 40 hours

Lecturer: William Cohen

Difficulty level: Advanced 

Course material:

 

9. Foundations of Machine Learning and Statistical Inference - Caltech

 

Course Link: http://tensorlab.cms.caltech.edu/users/anima/cms165-2020.html#

The ninth course is the Foundations of Machine Learning and Statistical Inference offered by Caltech.  This course covers the core concepts of machine learning and statistical inference. The covered machine learning concepts are:

  • Spectral methods 
  • Non-convex optimization 
  • Probabilistic models 
  • Representation theory 

The covered statistical inference topics include:

  • Detection & estimation
  • Sufficient statistics
  • Cramer-Rao bounds
  • Rao-Blackwell theory 
  • Variational inference

The course assumes you are comfortable with analysis, probability, statistics, and basic programming. 

Estimated duration: 30 hours

Difficulty level: Beginner

Course material:

 

10. Algorithmic Aspects of Machine Learning - MIT

 

Course Link: https://ocw.mit.edu/courses/18-409-algorithmic-aspects-of-machine-learning-spring-2015/

The tenth and final course on this list is the Algorithmic Aspects of Machine Learning course by MIT. This course is structured around algorithmic issues that arise in machine learning. Modern machine learning systems are always built on top of algorithms that do not have provable guarantees, and it is the subject of debate when and why they work. In this class, the focus will be on designing algorithms whose performance we can rigorously analyze for fundamental machine-learning problems.

Lecturer: Prof. Ankur Moitra

Estimated duration: 50 hours

Difficulty level: Beginner

Course material:

In conclusion, there are many free machine learning courses available online, offered by some of the top universities in the world. These courses cover various topics, from the basics of machine learning to more advanced techniques, and are suitable for learners at all levels. Whether you’re a beginner looking to get started in machine learning or an experienced data scientist looking to deepen your knowledge, you’re sure to find something of interest in this list of 10 free machine learning courses. By taking advantage of these resources, you can learn valuable skills and knowledge that will help you succeed in the rapidly growing field of machine learning.
 
 
Youssef Rafaat is a computer vision researcher & data scientist. His research focuses on developing real-time computer vision algorithms for healthcare applications. He also worked as a data scientist for more than 3 years in the marketing, finance, and healthcare domain.