Good

nice

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good

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cool

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Really good conceptual teaching of ANNs and decision trees, but it's a little lacking in the Python implementation. It teaches you how to program an ANN with any number of layers/neurons, but there is no mention of finding the "optimal" number of each. The last week on decision trees and ensemble models feels rushed as there is only one lab and required assignment, so it completely misses the Python implementation of XGBoost. However, it teaches the essential functions in each library, so one can easily continue his or her learning with Kaggle competitions and Stack Overflow. In the end, it's meant to introduce working professionals to the most common ML models in the world today, and it does that very well, but not much more.

This course is brilliance personified especially the intuition (which is the primary focus). The reason for a 4-star rating is, that it ended quickly, and it does not cover the codes in detail, but rather the logic on 'why we do what we do'. Andrew Ng drips knowledge and passion . I only wish he formulates a course named "AI-Scientist" with a one year completion time, that covers topics right from basics of Python, basic math, advanced math, ML, DL, NLP, MLOPs through and through. I am excited to jump into my next course :) Thank you Andrew Ng :)

I am missing handouts from the course and also access to the labs upon completion of the course. It was great that practice labs were offering a lot of help for the student, but I am afraid that too much material was given and the assignment was only about typing the given equation. It leaves me with the feeling that it would be difficult to apply the knowledge from the course to the real word problem, especially that I do not have any code available after the course is finished...

Excellent explanation of the concepts by Andrew Ng. However, like other reviewers, I find the last week a little bit rushed and, as compared to the first course of the specialization, this course feels a little...lacking, not in the sense of the information being taught but how the information is being presented (eg. the effort put into making quizzes and labs is subpar ). note: subpar of best is still good so I'd still recommend this one to anyone.

It was a very good and interesting course. I learned a lot about machine learning algorithms.

Compared to the first course "Supervised Machine Learning: Regression and Classification" there were a few things missing:

1- Practical exercises

2- Quizzes during the videos

Although you can learn the theoretical content very well in this course, in my opinion there is a lack of opportunities to practically apply and practice the knowledge you have learned.