The ideal course introduces the entire process and provides interactive examples, assignments, and/or quizzes where students can perform each task themselves.

## Do these courses cover deep learning?

First off, let’s define deep learning. Here is a succinct description:

“Deep learning is a subfield of machine learning concerned with algorithms inspired by the structure and function of the brain called artificial neural networks.”

— Jason Brownlee from Machine Learning Mastery

As would be expected, portions of some of the machine learning courses contain deep learning content. I chose not to include deep learning-only courses, however. If you are interested in deep learning specifically, we’ve got you covered with the following article:

My top three recommendations from that list would be:

## Recommended prerequisites

Several courses listed below ask students to have prior programming, calculus, linear algebra, and statistics experience. These prerequisites are understandable given that machine learning is an advanced discipline.

Missing a few subjects? Good news! Some of this experience can be acquired through our recommendations in the first two articles (programming, statistics) of this Data Science Career Guide. Several top-ranked courses below also provide gentle calculus and linear algebra refreshers and highlight the aspects most relevant to machine learning for those less familiar.

## Our pick for the best machine learning course is…

- Machine Learning (Stanford University via Coursera)

Stanford University’s Machine Learning on Coursera is the clear current winner in terms of ratings, reviews, and syllabus fit. Taught by the famous Andrew Ng, Google Brain founder and former chief scientist at Baidu, this was the class that sparked the founding of Coursera. It has a 4.7-star weighted average rating over 422 reviews.

Released in 2011, it covers all aspects of the machine learning workflow. Though it has a smaller scope than the original Stanford class upon which it is based, it still manages to cover a large number of techniques and algorithms. The estimated timeline is eleven weeks, with two weeks dedicated to neural networks and deep learning. Free and paid options are available.

Ng is a dynamic yet gentle instructor with a palpable experience. He inspires confidence, especially when sharing practical implementation tips and warnings about common pitfalls. A linear algebra refresher is provided and Ng highlights the aspects of calculus most relevant to machine learning.

Evaluation is automatic and is done via multiple choice quizzes that follow each lesson and programming assignments. The assignments (there are eight of them) can be completed in MATLAB or Octave, which is an open-source version of MATLAB. Ng explains his language choice:

In the past, I’ve tried to teach machine learning using a large variety of different programming languages including C++, Java, Python, NumPy, and also Octave … And what I’ve seen after having taught machine learning for almost a decade is that you learn much faster if you use Octave as your programming environment.

Though Python and R are likely more compelling choices in 2017 with the increased popularity of those languages, reviewers note that that shouldn’t stop you from taking the course.

A few prominent reviewers noted the following:

Of longstanding renown in the MOOC world, Stanford’s machine learning course really is the definitive introduction to this topic. The course broadly covers all of the major areas of machine learning … Prof. Ng precedes each segment with a motivating discussion and examples.

Andrew Ng is a gifted teacher and able to explain complicated subjects in a very intuitive and clear way, including the math behind all concepts. Highly recommended.

The only problem I see with this course if that it sets the expectation bar very high for other courses.

## A new Ivy League introduction with a brilliant professor

- Machine Learning (Columbia University via edX)

Columbia University’s Machine Learning is a relatively new offering that is part of their Artificial Intelligence MicroMasters on edX. Though it is newer and doesn’t have a large number of reviews, the ones that it does have are exceptionally strong. Professor John Paisley is noted as brilliant, clear, and clever. It has a 4.8-star weighted average rating over 10 reviews.

The course also covers all aspects of the machine learning workflow and more algorithms than the above Stanford offering. Columbia’s is a more advanced introduction, with reviewers noting that students should be comfortable with the recommended prerequisites (calculus, linear algebra, statistics, probability, and coding).

Quizzes (11), programming assignments (4), and a final exam are the modes of evaluation. Students can use either Python, Octave, or MATLAB to complete the assignments. The course’s total estimated timeline is eight to ten hours per week over twelve weeks. It is free with a verified certificate available for purchase.

Below are a few of the aforementioned sparkling reviews:

Over all my years of [being a] student I’ve come across professors who aren’t brilliant, professors who are brilliant but they don’t know how to explain the stuff clearly, and professors who are brilliant and know how explain the stuff clearly. Dr. Paisley belongs to the third group.

This is a great course … The instructor’s language is precise and that is, to my mind, one of the strongest points of the course. The lectures are of high quality and the slides are great too.

Dr. Paisley and his supervisor are … students of Michael Jordan, the father of machine learning. [Dr. Paisley] is the best ML professor at Columbia because of his ability to explain stuff clearly. Up to 240 students have selected his course this semester, the largest number among all professors [teaching] machine learning at Columbia.

## A practical intro in Python & R from industry experts

Machine Learning A-Z™ on Udemy is an impressively detailed offering that provides instruction in *both *Python and R, which is rare and can’t be said for any of the other top courses. It has a 4.5-star weighted average rating over 8,119 reviews, which makes it the most reviewed course of the ones considered.

It covers the entire machine learning workflow and an almost ridiculous (in a good way) number of algorithms through 40.5 hours of on-demand video. The course takes a more applied approach and is lighter math-wise than the above two courses. Each section starts with an “intuition” video from Eremenko that summarizes the underlying theory of the concept being taught. de Ponteves then walks through implementation with separate videos for both Python and R.

As a “bonus,” the course includes Python and R code templates for students to download and use on their own projects. There are quizzes and homework challenges, though these aren’t the strong points of the course.

Eremenko and the SuperDataScience team are revered for their ability to “make the complex simple.” Also, the prerequisites listed are “just some high school mathematics,” so this course might be a better option for those daunted by the Stanford and Columbia offerings.

A few prominent reviewers noted the following:

The course is professionally produced, the sound quality is excellent, and the explanations are clear and concise … It’s an incredible value for your financial and time investment.

It was spectacular to be able to follow the course in two different programming languages simultaneously.

Kirill is one of the absolute best instructors on Udemy (if not the Internet) and I recommend taking any class he teaches. … This course has a ton of content, like a ton!

## The competition

Our #1 pick had a weighted average rating of 4.7 out of 5 stars over 422 reviews. Let’s look at the other alternatives, sorted by descending rating. A reminder that deep learning-only courses are not included in this guide — you can find those here.

The Analytics Edge (Massachusetts Institute of Technology/edX): More focused on analytics in general, though it does cover several machine learning topics. Uses R. Strong narrative that leverages familiar real-world examples. Challenging. Ten to fifteen hours per week over twelve weeks. Free with a verified certificate available for purchase. It has a 4.9-star weighted average rating over 214 reviews.