Cluster Analysis and Unsupervised Machine Learning in Python (Udemy.com)

This course might seem a bit hard at first, but if you really pay attention to what's going on you are going to understand everything. Make sure you know probability and Python well. Amazing course and thank you Lazy Programmer!

Thank you for your hard work and dedication in creating such a valuable learning resource. Your course has undoubtedly helped me advance my skills and build a solid foundation in machine learning. I look forward to applying what I've learned to my future projects!

This course provided me with all the knowledge that I needed to take my machine learning skills to a new height. This course takes us through all the different kinds of clustering and really helps us develop a solid base for machine learning.

Expectation-maximization is a beast and this course covers a lot of ground. I don't think I'd recommend it as an introductory machine learning course but if you have experience with ML it will be helpful for thinking about the big picture of unsupervised learning and latent variables.

So far I like the theory of how clustering works, I would suggest to add an example with numbers so we can see how the calculations are done using the formulas, and then follow the practical example with the code, that would be good. I am halfway in the course, so most likely there is a chance to give the 5th star :)

More than 37% of the course on installations (mainly installation for other author courses). For the rest of the 4 hours we have a lot of theoretical explanations: yes, the authors know their maths !!! Anyway, this course should not be labeled beginner but for people who need to write their own algorithms instead of using python libraries, I think it would be very useful for them, not yet for me. update: Anyway after Follow the appendice Part I'm not disapointed and will surelly take a lot of his courses .. but mixed them with other beginner/intermediates courses to fully understand his material

Love the maths part in the course! Provide intuitive and technical overview of the models! Its better than other courses which just dumps the code with the intuitive aspect of the model but with no comparison between models and the maths behind it. Highly recommended course!

I was pleased to take this course by the lazy programmer. It's straightforward and teaches all the promised subjects. It goes into the mathematical background more so than I expected. Nice practical examples included tweets analysis and DNA clustering. If you are new to machine learning or advanced and wants to learn more I am sure you can take this course and find lots of benefit.

What I most enjoy about this course was two things. First, it went into the math. I appreciate seeing some formulas, and relating the algorithms to the mathematics behind them. Second, two out of three algorithms were implemented manually. I find this very helpful. Compared to other ML courses this is very short, half of the course is an appendix. The programming lectures skips some steps and I miss some explanation for why the lecturer does what he does. There are no exercises either. You are encouraged to implement algorithms before you see them and work out the math for yourself. But there is no guidance throughout this process, except for going back into the lectures. That being said. I found the course valuable and in particular enjoyed the clear explanation of Gaussian Mixture Models, which is rarely covered in other ML courses. ---------------------------------------------------- After the courses updated with more GMM lectures I had to revisit the review. The new lectures were stellar. They help tie together GMM with Bayes classifier providing a deep insight to both methods. This course goes much more in depth than any other ML course I have done.

Great stuff. Instructor speaks Mathematics like it's his native language. I wish there was more explanation and examples (at a little slower pace) to help me grasp the materials better. I recommend the course for the intermediate/advanced person who wishes to learn unsupervised clustering.

1. The video is well paced and neatly divided.

2. Kmeans and hierarchical clustering is explained very well. infact one of the best I have come across.

3. solution example could definitely be better. A good use case, clustering, and a conclusion is what i was expected when I read about the course.

4. Prereq does not mention NLP, examples are NLP based. There is a DNA example however, I did not make my understanding any better. I might have been stressing more on example than concept, but i love courses that have good examples to back the concepts since I understand the concepts much better that way.

Brief but informative introduction - practical tips on how to study the material.