Machine Learning 101 with Scikit-learn and StatsModels (Udemy.com)

Thanks for Sharing this knowledge . When I was trying to teach myself ML by looking at other code in Kaggle I was always wondering what these codes are doing and why they are even there . Now I know why :-)

So far so good. I could give detailed feedback when I reach difficult subjects as the course progresses. Now only I'm at the beginning of the whole course. However, I have a positive expectation as I've taken other course of the instructor before, which was pretty good.

This course really teach you in an easy way to understand what is the ML doing. Only that I can't access the dropbox site, it is blocked by my company, I have use my own PC to download the resources.

Although my English skill is not good, I could understand well enough because of its plain English. Funny illustration and animation made the some lectures heartwarming. It was easy to understand the Python cording, but it was difficult to look at the cording because the screen capturing was a bit far away. It was a pretty satisfying course. Thanks so much!

Statistical Modelling is the base of Modern Machine Learning Algorithms. The intention behind taking this course is to explore different libraries than "Sklearn" and see the result on a given problem. Compare the result with that produces by Sklearn library and choose the better one from them based on Ease of Interpretation and Accuracy. This course is covering both of the mentioned objectives with multiple examples for concept practice with ease of explanation of each concept.

Successfully gained more knowledge than expected. My main reason to join the course was to understand linear and logistic regression. But K-Means was explained as well which was a bonus and I figured some new tricks and techniques which I was not aware of. Highly recommended for people who want to start machine learning with no knowledge of linear and logistic regression and K-means clustering.

Awesome Tutorial, I really suggest if you are a beginner then start with this course to understand about Regression, Classification and Clustering and Practice the examples. I really enjoyed the course, Thanks a lot for the awesome course.

Good material and very clear pronunciation – but in parts over paced and then to little hints for getting better background. But in general, a very worthwhile curse on the subject gets you fast to the intuition with the test statistics. The python part gives a nice entry point for real projects or actually knowing where to start with sklearn, very cool. Nice would have been if the 2 feature sets just also been plotted 3d, it is still possible to visualize I think this would have been nice.

One of the best course I have ever taken on regression. I have really enjoyed the course. The exercises are pretty awesome and well structured. Though I believe the course is complete but I have one request to the author that to include a step wise regression methodology which make this course 110% complete. 100% recommended course.

UNDERRATED This is an excellent course in the world of Machine Learning. - Linear + Multi Regression is excellent. - The explanation of 'standardization' is amazing! - In fact, many explanations with great visuals - lot of work! - In practice, I don't like Clusters, but the material was great. - The math behind Logistic Regression is fantastic. - I'm not convinced about Statsmodels for Logistic Regression and prefer Scikit-Learn. - Tutor seems to math-proficient. Awesome course! Thanks!

Good one

Nice