In 2011, Stanford professor Andrew Ng launched his first MOOC, teaching just over 100,000 students about machine learning. Ng has said that the course, which turned out to be very popular, led to the start of Coursera. Today, Coursera reaches 7.5 millions of users around the world, and Ng’s machine learning course continues to receive wildly positive reviews from seasoned and newly initiated programmers alike.

Programmers are using machine learning to build really, really smart algorithms — and thanks to educators like Andrew Ng, people are starting to take notice. If you want to begin your education in machine learning and explore some of its applications, check out our round-up of some of the best online machine learning courses today.

Beginner Machine Learning:

1. Machine Learning (Stanford): This highly-rated Stanford course is perhaps the best introduction to machine learning. Users praise Professor Andrew Ng for his ability to expertly explain the mathematical concepts involved in different areas of machine learning.

Level: Beginner | Duration: 10 weeks

2. Learning From Data: This introductory course from CalTech dives into machine learning as if telling a story: Can machines learn? How exactly? Students will gain an understanding of the theory behind machine learning, and gain experience with different algorithms and models.

Level: Beginner | Duration: 10 weeks

3. Principles of Autonomy and Decision Making: Gain an overview of the different ways systems make decisions, from logic to heuristics to model-based reasoning.

Level: Beginner | Duration: Self-paced


Intermediate Machine Learning:

4. Machine Learning (University of Washington): This interactive course goes beyond basic concepts to explore neural networks, learning theory and vector machines — among other things. It is taught through “supervised learning” — meaning that the correct answer is usually given to the student during class.

Level: Intermediate | Duration: N/A

5. Practical Machine Learning: Part of John Hopkins’ specialization in data science, this course focuses on learning the components and applications of prediction functions.

Level: Intermediate | Duration: 4 weeks 

6. Introduction to Convex Optimization: This high-level course will help students recognize and tackle convex optimization problems. The course will also go over applications in areas like finance, computational geometry, mechanical engineering and more.

Level: Intermediate | Duration: Self-paced


Advanced Machine Learning:

7. Machine Learning (MIT): This graduate level course from MIT approaches machine learning through the lens of statistical inference.

Level: Advanced | Duration: Self-paced

8. Prediction: Machine Learning and Statistics: Another graduate level course from MIT, but this one explores machine learning through predictive models and “the study of generalization” from data.

Level: Advanced | Duration: Self-paced 

9. Topics in Statistics: Statistical Learning Theory: This course provides an in-depth analysis of the theories behind statistical learning, and covers empirical process theory, Vapnik-Chervonenkis Theory and more.

Level: Advanced | Duration: Self-paced


Introduction to Neural Networks:

10. Introduction to Neural Networks:  This course delves deeper into the topic of neural computing and learning, and covers models of perception, motor control, memory and more.

Level: Advanced | Duration: Self-paced 

11. Neural Networks and Deep Learning: This course teaches the foundations of deep learning so students can build, train, and apply fully connected deep neural networks

Level: Intermediate | Duration: 8 weeks


Introduction to Genetic Programming:

12. Bioinformatic Algorithms: In this University of California at San Diego course, students will deal with real genetic data to learn more about the computational methods used in modern biology and genome sequencing.

Level: Intermediate | Duration: 10 weeks


Additional Resources (updated December 2018):

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