Machine Learning by Columbia University and edX

There are not many courses online that provide such in-depth learning experience in Machine Learning. This course goes into some details and mathematics of the algorithms being used. It demands a good amount of time every week to understand and apply all that is being taught but that is what makes it good.

Amazing in-depth knowledge. Almost all algorithms out there are being explained.But:Terrible execution, lecturer is just reading the slides and explaining a bit. Not enough real life examples or any code examples. No interactivity.

This course requires a solid foundation on probabilities, calculus, linear algebra and programming. Provided these prerequisites are available (anyone who is serious about the field should possess these skills anyway), the course will become an incredibly useful resource to break into Machine Learning.

Beautiful course which covers advanced concepts of machine learning. Professor Paisley covers a whole range of topics and breaks down hard concepts clearly. This course is very theoretical. There are four programming assignments which give an opportunity implement some of the algorithms learned.

This is my second course about Machine Learning (I'm a senior developer with a mathematical background) and I am delighted. This course is probably the best online ML course you can find. Professor John Paisley is an amazing and brilliant teacher ; he doesn't only have the knowledge, he has the talent of teaching, of transmitting knowledge with detail and precision, of explaining, reexplaining, illustrating sometimes complex concepts to make them simpler to understand and digest. I would like to thank him warmly, because my linear algebra and math background were pretty rusted, but it was a so-interesting 3 months, challenging for some parts but at the end of the day, it open perspectives, it provides extra tools of knowledge, it gives you keys of understanding. What a pleasure.

Buckle up -- this deep, mathematically rigorous dive into the major areas of machine learning is fast-paced and challenging. In fact, most of the course is less about machine learning than the math behind it -- problem-solving and applications take a back seat to the underlying mathematical techniques. You won't see very many implementation examples or, in the programming projects, watch a machine get smarter. For that context, you either need prior ML training or to have taken the first course in the AI MicroMaster series, Artificial Intelligence with Ansaf Salleb-Aouissi, which treats most of the topics in this course at a higher, more introductory level. Also, considerable fluency in probability and statistics is assumed -- at the level of MITx 6.041, for example. Although no textbook is suggested, I found "The Elements of Statistical Learning" by Hastie et al. to be quite useful. The topics covered are numerous, too many to list without putting you to sleep, but they span all of the common machine learning techniques except neural networks, which is a subject in itself (and is nicely covered at a high level in the AI course).

This is the most insightful course I cam across. Although I am a trained practitioner of ML concepts, but there were some topics like Gaussian Processes, Collaborative Filtering, LDA etc, for which I need more satisfactory explanations.

The materials are mathematically rigorous and really provide insight on how to analyse, design and evaluate learning algorithms. Prof Paisley lectures are dense, though unfortunately he's not the greatest lecturer and spends most of the time reading from the slides.

The course covers a wide range of machine learning techniques. The teacher is nice and his teaching is clear and clever. The programming assignments are full of fun and I have learned a lot from them. But the setting for assignments are somehow poor because most questions only accept one attempt and none answer is shown once you failed.

Course was a great overview of the theory behind a wide range of models. The theory gives a good intuition about what the models are doing and how best to make use of them. My effectiveness has improved, even using models I thought I was very familiar with. The lecturer was excellent and did a good job of communicating difficult concepts well. The course isn't easy. You need a strong mathematical background and a decent amount of time to get through it properly.