Ratings and Reviews for Applied Machine Learning in Python

Rather then writing code while explaining like the intro and plotting in python, the instructor shows it like slides, its hard to follow which chunk of jupyter notebook he is explaining, and requires lot of back and forth to read the code. Very bad way of explaining the codes.

Great overview

Great course for implementing machine learning using python.

Excellent!!

Great course, got to learn a lot. Very helpful discussion forum.

Pros:The course provided me with a very good introduction about Machine Learning(in Application level), for example, the relative terms that be using, differences in classification and regression models, the validation metrics and methods, the related tools using in Python. It fulfills the application goal as the Professor said in the week1. I can utilize a lot from the course into my current work. Cons: The auto-grader could be improved better which can save learners lot of time debugging it.....

For applied machine learning, outstanding. It could be improved with bit more theory, which gives more insight to the concept.

Decent material and I appreciate the amount of hard work that went into building the course. However, the course should really be titled "Evaluating Classification Methods", as that is pretty much the focus of the entire class. The lectures (especially in Week 2) were SOOOOOO long and very hard to absorb, that even double-speed didn't help. In education, less is more. I would compare this course to the reading of a textbook. There was very little focus on making sense of the code and solving real-world problems and far too much emphasis on shotgunning (what felt like) every single classification technique known to man and trivializing pros and cons of each method. To make matters even more strange, PCA and other useful methods were pushed into "optional". This course should really be a two-part course, especially since the claim is that the course requires 18 hours of time. Sure, type in the code just as the professor does and you get the right answer, but meaning is lost of if you are to adhere to the timeline. If I didn't know more about machine learning and this class had been the first one I had taken, I couldn't run fast enough from the pursuit of a career in this field. Data analysis is intriguing and the methods are varied and fascinating. For me personally, this class was a let-down. Again, I recognize the course was hard work; I am merely stating my personal sentiments.

This course provides a brief introduction to many of the vast and dense ML concepts, like Regression, Classification, Clustering, Neural Networks and many more.I took a course by Prof. Andrew ng on Coursera before taking this course. And due to this reason, i was somewhat familiar with the concepts that are being taught in this video.If you are a beginner, i personally recommend you to take Prof. Ng's course on Machine Learning, and then switch to this part of specialisation, by completing the 1st specialisation (2nd is optional but if you are sort of artistic person, and have a habit of visualising things then opt this too). It is best for those who just want a quick recap of some topic.

C'est le meilleure cours en pratique que j'ai rencontré dans toute ma vie.je vous remercie énormément pour m'offrir cette cours et je remercié mon professeur pour la simplicité et la méthode avec laquelle a fait ce cours.