I think the methodology is outdated. But the bases are the same. It is remarkable that they teach how the algorithm and formulas work.

Very fascinating course but exercises like final project will be more for exposure to real coding than it will be really more helpful.

Course content and presentation is really good! The only thing i would add is the tuning of hyperparamaters which makes ML what it is.

Great course but there should be videos where there is need of explanation on code as well, codes given are very good and covers basic

I really liked the course content, way of teaching and assignments.

This will definitely help a beginner in data analysis to start with

Very nice course!! You learn a lot more of the theory than the practice part, but the concepts are well explained and I learned a lot

Really great class overall! some of the lecture videos the speaker spoke a little too quickly but I could always rewind and rewatch.

A good course to quickly learn important aspects of ML with Python. The assignments and final exam is also very useful for learning.

quite good at the explanation and un-graded exercises.

But the knowledge could be deeper and more about parameters in Sklearn APIs.

Goodone for anyone who's a beginner in this field. But I personally suggest you to take the Data Analysis with Python course first.

Although not extremely detailed in the model optimisation part of the work, it is a very useful way to get started on applied ML.

Gives a good overview of regression and classification algorithms . It could have been expanded to other ML algorithms as well.

Great course, SO much information and great excercise, In Captone project project guidance need improve,otherwise great course

It was a hard class - the concepts made sense but it is hard to figure out how to use them without more programming examples.

Great Course to get an understanding of Machine Learning in Python with no background knowledge needed. Cheers to Learning!

Very complete course yet full of typos even in the datasets. Lots of information were redundant but an overall great value.

After taking Andrew Ng's ML course, I still learned some new things here, but this course is rather shallow in comparison.

This course was a great refresher from my data mining course in college, but I think some topics need to be expanded upon

A pretty good course to get familiar with supervised learning. Topics on unsupervised learning were moderately explained.

I learned a lot and I understood the different clustering algorithms to organize the data like DBSCAN, K-Means and more.

Good for quick overview of some of the basics of introductory Machine Learning. More focused on theory and definitions.

This course gives an introduction to machine learning by giving brief about algorightms such as KNN, Random forest etc.

I'm not a fan of their notebooks. I prefer the DataCamp model. However, the videos were easy to understand and follow.

it is all good but I cannot seem to create IBM Cloud account for some reasons... you guys have got to fix this problem

Week 6 was a bit chaotically organized. Overall, I really liked this course and would recommend it to other learners.