The Top 5 Machine Learning Libraries in Python (Udemy.com)

This is awesome! The concepts related to various libraries that are majorly used in machine learning are explained in very easy way! Though the course is a high level one, it covers the basic aspects which one must know for getting into ML. Overall, liked the course and it encouraged to explore the ML libraries in more depth!

Thanks Mike - I like the structure of this course. It's hard to put all of these in such short course as well as to make sure student can pick up efficiently and get covered in main libraries in machine learning. And the code as samples are clean and clear, straight-forward. Love this course. Will recommend it to my friends. Thanks again.

It got me to understand Python machine learning libraries better. The exercises helped me alot to navigate through Jupyter Notebook, to save my work and to retrieve them for later use. In the future I will take more courses from this instructor, he is very knowledgeable with the teaching material.

If you are starting to learn machine learning and dont known where to start this is a good course .You can select whichever lib u want to use and go ahead. As this is just an introduction course you won't find it very much interesting .The tutor is great.We get to learn some simple easy function of each library here.Go as this course is free try it no harm in that. P.S-To the course tutor ,Sir please give some details on where to use this in industrial level work.Add some projects too.Thank you.

I'm coming from an extensive SQL Analytics background and have been working for approx 6 months on adding Python as a skillset for Data Science. I was already familiar with Pandas, Numpy, & Matplotlib. Mr. West provided a valuable high level overview of Scikit-learn and NLTK and how those packages complement the other packages in the Python Open Source Machine Learning Framework. This course provided a good frame of reference for going deeper into Machine Learning.

All over the course is excellent. Helped me lot in understanding the basic things about python libraries used for ML. Self-assignment on some real life problem could help us to have a clear understanding in learning how to approach towards a problem in ML way. Deeper explanation of the codes (like whats happening inside) written for Iris Data set was missing.

If you are a complete beginner to machine learning and python, this course is a very good start. The course did provide a brief knowledge about these libraries and it also introduces you to the work machine learning engineer's do which might prove beneficial to get you started with data science career.

This was a create course for learning machine learning libraries in Python. I didn't have much experience with python or machine learning. I found the explanations to be very simple and easy to follow while getting the important information cross. I liked how the lectures were brief and to the point. The instructor was helpful with getting me setup on Anaconda and the exercises reinforced the material.

I would have liked a bit more information/clarity in real time. For example, when typing a method, tell me that is what it is instead of having to learn that later. Otherwise, I found this course informative even though I have no idea of how or if I'd apply any of it. After all I'm a beginner taking a beginners course.

Very clear explanations. Very good mix of audio and visual learning tools. Very well paced. I loved the user experience and Mike;s relaxed yet thorough way of explaining things. It was even good to see him make the same mistakes we all make when typing code. It was brilliant Mike, can't wait to do your other courses.