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Top 10 Free Machine Learning Courses To Study Online

“Machine learning is a field of study that gives computers the ability to learn without being explicitly programmed” Arthur Samuel, 1959.

Machine learning and artificial intelligence have been a rising field of research in both the corporate and the academic world. Machine learning proves to be incredibly powerful when it comes to making predictions or calculated suggestions that are based on large amounts of data.

If an individual wants to master machine learning, how do you start and from where? In order to learn about Machine Learning, one not only needs a keen interest in it but also have the right resources.

So, we have compiled and listed the Top 10 Free Machine Learning Online Courses and Tutorials by leading researchers in the field to help you while starting from scratch or even improve if you are already familiar with it.

1. Introduction to Neural Networks and Machine Learning

This course, by Geofffrey Hinton, from the University of Toronto,  makes the use of the “inverted classroom” model. Simply, it means that instead of being introduced to the related material in a large lecture hall, one can first watch the lecture as a set of about 3 short videos at home before the commencement of the class and then in class, a discussion about the videos takes place.

Link: http://www.cs.toronto.edu/~tijmen/csc321/

2. Introduction to Machine Learning

This course was taught at the University of Toronto in 2015 by Russlan Salakhutdinov, Director of AI research at Apple. The course covers some parts of the theory as well as the methodology of the statistical aspects of machine learning. Some of the major topics among the many topics that are covered include:

  • Linear methods for regression
  • Linear models for classification
  • Regularization methods
  • Neural Networks

Link: http://www.cs.toronto.edu/~rsalakhu/CSC411/4

3. Machine Learning and Pattern Recognition

This course is by Yann LeCun, who was the director of AI Research in Facebook 2010. The prerequisites are Linear Algebra, vector calculus, elementary statistics, and probability theory.

The course not only provides an individual with a wide variety of topics related to pattern recognition, machine learning, statistical modelling but it also covers the mathematical methods and theoretical features, still mainly focusing essentially on practical and algorithmic topics.

Link: https://cs.nyu.edu/~yann/2010f-G22-2565-001/index.html

4. Machine learning

This course is by Kilian Weinberger. The crucial goal of the course is to provide the individual with a basic outline into the field of machine learning. It also trains the students in the basic skills to code up their own learning algorithm and to choose appropriate learning algorithms for different type of problems, followed by debugging.

Link: https://courses.cis.cornell.edu/cs4780/2017sp/

5. Machine Learning and Adaptive Intelligence

This course was taught at the University of Sheffield by Neil Lawrence, director of Machine learning at Amazon.  The prerequisites required are linear algebra, probability, and calculus.

This course aims to provide an individual with an understanding of the necessary technologies within which the modern artificial intelligence and machine learning lies. To be precise, it will act as an aid in providing an initial understanding of probability and statistical modelling, supervised learning for classification and regression, as well as unsupervised learning for data exploration.

Link: http://inverseprobability.com/mlai2015/

6. Introduction to Neural Networks and Machine Learning

This course was taught at University of Toronto by Roger Grosee in 2017. The prerequisites required are calculus, probability, and linear algebra.

This course focuses primarily on giving an individual an overview of both neural networks and machine learning which includes the foundation as well recent advances in the field. It acts as an aid in providing details about concepts like probability and algorithms.

Link: http://www.cs.toronto.edu/~rgrosse/courses/csc321_2017/

7. Machine Learning (Standford University)

This course was taught at Standford University via coursera by Andrew.

In this course, an individual learns about the various machine learning techniques that are known to be most operational, and implement it to gain practice. Throughout this course, one will not only get to learn about the theoretical parts of learning but also gain practical knowledge which is essential and as well as powerfully apply these techniques to new problems.

Additionally, one will also learn about some of the best practices of the Silicon Valley when it comes to the invention in machine learning and AI. Thus, this course very efficiently provides a wide introduction to data mining, statistical pattern recognition, and machine learning.

Link: https://www.coursera.org/learn/machine-learning

8. Machine Learning-Tom Mitchell and Maria-Florina Balcan

This course was taught at Carnegie Mellon University by Tom Mitchell and Maria-Florina Balcan in 2015. The prerequisites required for this course are probability, linear algebra, statistics, and algorithms.

This course has been mainly designed for a student who is of graduate-level in order to provide them with a thorough training in the technologies, mathematics and algorithms, methodologies that are currently needed by people who are involved with research in machine learning.

Link: http://www.cs.cmu.edu/~ninamf/courses/601sp15/

9. Machine Learning-Michael Littman, Charles Isbell and Pushkar Kolhe

This course was taught at Georgia Institute of technology by Michael Littman, Charles Isbell and Pushkar Kolhe in 2017. It requires a strong familiarity with probability theory, Linear algebra, and statistics.

The course is divided into two parts. The initial part of the is aimed to cover all aspects about Supervised Learning – a machine learning task that makes it possible for phones today to recognize your voice, your email to filter spam, and a lot more.

In part two, you will learn about Unsupervised Learning.

Link: https://in.udacity.com/course/machine-learning–ud262

10. Introduction to Machine Learning-Sargir Srihari

This course was taught at University of Buffalo by Sargir Srihari in 2017.

This course covers the essential theory, principles as well as algorithms related to machine learning. The methods are based on statistics and probability which have now become vital when it comes to designing systems that display artificial intelligence.

Link: http://www.cedar.buffalo.edu/~srihari/CSE574/

Final words

To conclude, Machine learning is one of the hottest topics in the field of Information Technology.  A lot of companies in today’s world want to implement machine learning on a bigger scale and they are likely to look for employees who have mastered it. Taking one of these free courses would surely be a prominent addition to your resume if you want to pursue this field.

About the author

Bradley Wood

Bradley Wood is a freelance writer who lives in Pomona, Los Angeles. He is pursuing graduation from the University of California (UC). Bradley frequently contributes his high-quality articles in Academics and Education to our site to help students in their day-to-day life.

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