Machine Learning and AI: Support Vector Machines in Python (Udemy.com)

The course is great, I regret that I did not start my machine learning study with this instructor. It was possible to avoid many difficulties and not break through the door. I highly recommend everyone to advance their machine learning skills with this course. Everything is explained in it to the smallest detail.

My experience in learning this SVM course was great. I learnt a lot of things. The practice exercises provided was very helpful. The way of teaching the course was very helpful. I enjoyed the entire course, got to learn many new things which will surely help me in improving my efficiency and effectiveness of doing data science.

Best thing about this course is you get to learn how to learn by your own. The tutor does his best on assisting us whenever we are in doubt. Never felt alone following this course as I was supported each time on the Q&A discussions. Thanks for giving us this fantastic course and for the utmost support you have given me.

As always, Lazy Programmer's classes are amazing, so this isn't the opposite. By far, the best teacher on Udemy. I love the way he teaches and, at least for me, encourages me to think about theoretical aspects of ML and take those concepts to code. The knowledge I've gained studying his courses really amazes me. Looking forward to take all of his courses to make more robust my intuitions about ML. Really love this guy and the way he delivers us some of the best material on the internet.

This course is definitely one of the best courses for the topic. I admire the practical approach of the course while not lacking in the technical details. There are some topics that I need to review my prior knowledge to further understand the reasoning, however overall I find myself often coming back to review the fine points. I can see the instructor is an expert, showing both the practical and theoretical sides of the topic.

The flow and structure are helpful for beginners a first. However the course does deep dive into the mathematical foundations and proofs, and to be fair this warning was provided up front. There is some balance achieved, however, between math theory and coding examples so you can get some sense as to how the theories are applied.

The course is well structured and excites you to go further in machine learning. I had learnt the basics of SVM before, and this course gave me a new level of understanding of the concepts. The delivery of the lectures was engaging and well explained. I would suggest anyone who wants to learn about SVM and how to apply it to take this course seriously.

Although I've just finished 35% of course. I felt this is less intuitive, and bit boring, as author mostly uses static presentation slides and monotonous voice. I felt he might just be reading the pre-written script and changing slides. I expected more engagement and creativity through use of animations in order to get more clearer intuitions, as these are video lectures. The course needs clear & intuitive understanding of several Mathematical concepts, and perhaps me revisiting this course after brushing my Math concepts might change my opinion as well. But at this moment, I think author has potential to do lot better than this and utilize the power of Video lectures better.

It is absolutely by far the best organized and explained course on the theoretic aspect of SVM I could find online. As far as my knowledge is concerned it is far more difficult to learn the mathematical in and out about how SVM works from other resources such as online technical documents and papers. I myself as a machine learning research scientist was astonishingly impressed by clarity of logistics from Lazy Programmer. The course is a must for anyone who is interested in underlying math of SVM.

I've attended quite a few courses in Machine Learning from different teachers (including Lazy Programmer) and this is by far the best one. First off, differently from Lazy's other courses, there are a couple of sections devoted to use the SVM API without having to deep dive into the theory with lots of practical advice (this is a really welcome idea for future courses). Secondly, If you want to know (like myself) every details about the algorithm there are a whole bunch of sections devoted only to theory. Though, be warned that you need a solid background in Probabilities and Advanced Linear Algebra (and patience) to get a grasp of SVM. Finally, Lazy uses storytelling to make engaging presentations. The format of this course is really great!

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Great explanations of how kernels are used in SVMs.