Machine Learning Classification Bootcamp in Python (Udemy.com)

This is a VERY basic class. That is ok, but the fail for me was how outdated some of the code is at this point. I think Udemy or the creators of this should mark ALL classes with a last updated date on every course, both when you buy it, and after you buy it. That has proven to be a differentiator in how much is learning vs struggling to refine code for software updates. I would like to have seen them work a bit harder to tie some of the concepts together - perhaps differentiate the use of the algorthms by some type of scenario.

I think this course was great! I had a lot of fun. What this course did best was break these topics down to digestible - need to know - basics for the sake of executing the exercises. It also shared information and links to resources to dig deeper into the backgrounds and nitty gritty of these classification mechanisms. My only critique of the course is a detail about the projects. Project #1 in each section works through an example to teach the concept, while Project #2 was the "homework" exercise that was detailed in the lecture videos after you complete them. These Project #2s didn't actually include questions to answer, they only included the example output in a Jupyter Notebook. I found it frustrating to know what I was supposed to be doing at a given step, as not all terminal output is given (for instance defining variables sometimes doesn't include an output). I would suggest updating the Project #2 Questions with questions if I had anything to add. This still to me this was 4.5/5 stars, I truly enjoyed this and will absolutely consider classes again from this team!

This course has great structure and just the right amount of repetitiveness. A lot of other "bootcamp" courses are trying to overload you with various python and sklearn tricks, so that little remains in memory after you finish the course. This course, on the other hand, is focusing on just a few structural aspects and classification techniques and really makes sure you remember the python/pandas/sklearn manipulations one has to perform.

Hi, I am a graduate of Aalto University, Finland. Due to my limited Finnish Language skills, I have decided to make a career in machine learning. For that, I took this course and I am glad that I paid for this course. As this is one of the best courses I have taken so far. Videos are of great quality and building projects from scratch helped me a lot in understanding how to implement a specific machine learning algorithm on a data set. Highly recommended course. -Hassan

This was a very helpful course, but I felt that the structure was very repetitive and there were a lot of important topics that were not touched. For instance, I would have liked to see more practice with multiclass classification and discrete (non-numeric) independent variables. I would have also liked to see the following: -How to choose which of these classification methods is right -How to optimize parameters for best performance (e.g., n_estimators for RandomForest) -Determining confidence level for predictions Etc.

Excellent instruction. I tried many instructors to refresh my concepts and learn new things, not that other instructors were bad, some of them were very good too, but this course structured with the Questions and Solutions codes, to the point material with targeted and simple approach appealed to my learning style, instruction pace was excellent. This course motivated me to enroll in other courses by the same instructor. Thanks.

An excellent piece of work: examples pulled from real life and highly applicable. Presentation was amazing done: for the most part, clear and brief. Kept me on my toes: the much curiosity at the close of one session led to great expectation into the next. Perhaps, more consideration could be given to posting timely responses to questions/comments - allowed up to four days lapse to complete course but decided to close out with a number of unanswered questions.

Great course to practice Machine Learning classification models! It is very easy to follow if you already have experience. Looking forward to take other courses! Thanks!

I learned a lot. The teacher gives a very good picture of artificial intelligence at the beginning, allowing students to understands most of the buzz words related to this area, such as machine learning, deep learning, KNN, Naive bayes algorithm, logistic regression, ... Also, what my favorite thing in this course was that the teacher spent a lot of time to explain the intuition behind each algorithm, so that when we dive into code, we actually know what we are doing, at least at an abstract level (we don't go too far into mathematics, but we get the idea in simple terms). So no need for a strong mathematical background to follow this course, that was much appreciated.

This is one of the best ML course . Even Andrew's ML course is not enough to develop model, but Andrew's course provides best understanding of basic concepts and Maths . This course gives full practical knowledge with real examples. These are the two best combination, one should take these two courses to understand ML and develop models . Thank you

well explained in a very simplified manner. great course

not at all

a lot of repetition in many lectures