This course is good.

Nice course to study

Quite challenging.

very good learning

HARD BUT GOOD

nice course

Excellent!!

Awesome !!!

hard a bit

Excellent

Very Good

very good

perfect

Good

good

Good

GOOD

....

Good

Good

ok

.

Here's my review for this course - The good aspects - - This course served as a good refresher for traditional ML concepts like Regression, Classification, and Model Evaluation, along with hands-on exercises in Python. - Assignments need effort, have good exercises & force you to think. You cannot simply watch the lectures & complete them straight away. - I especially liked the module about Data Leakages and how it impacts our model's performance. Scope for Improvement - - Some concepts like Classification models are explained pretty well whereas others such as Regression, and Unsupervised learning (Clustering, Anomaly Detection) are quite rushed. - There are some obvious errors in the assignments and auto-grader, missing files, some clearly vague questions. The discussion forum is riddled with similar questions for these errors - they could have fixed it years ago but chose not to. - Not much depth in the topics - beginners will have difficulty understanding pitfalls of certain models, how real-world data mining works, and how to select features and models.

If you're a beginner - it will give you a good overview of traditional ML models and implementation in Python. Good to try, but you need to spend a lot of time for self-learning the concepts, specially the mathematics behind these algorithms.

This is an OK introduction to Machine Learning. It covers a range of relevant topics. The gap between the lecture content and the assignments is the typical chasm for this U.Michigan "speciality", and frankly you end up basing assignment answers more on internet research rather than lecture content.

I'd sum it up as a substantial missed opportunity. The last assignment is really good in terms of doing a realistic Machine Learning project, but the preceding course content doesn't give you the tools or frameworks to do that project in a logical, industry standard workflow. It gives you an idea of what the tools are, but not how to really apply them all together in an efficient and logical series of steps.

It's as if those who designed the course decided that learners needed a tough-love approach, like a trainer lying down on the grass and showing learners swimming strokes, and then just throwing those learners into a pool and expecting them to keep afloat, and combine what they remember with what they see other more experienced swimmers in the pool doing. It shows a fundamental misundestanding of the Coursera learners usually being very time poor and expecting much more from the instructors.

This course provided a good structure and order to learn introductory machine learning concepts in Python. However, I thought the lectures in particular were needlessly more abstract than the previous data science courses in this specialization.

In my experience, learning a new programming concept comes from practically writing code then observing what happened. The earlier data science courses were great because you could test code with the lecturer as the video progressed and learn from it.

The lecture content here structured to discuss broader machine learning concepts, rather than setup to follow along in the notebook. I found this was okay for introducing the idea of different machine learning concepts, though without the practical application and observation it became difficult to remember these concepts or test what I was hearing. I found most of my learning happened in the assignments or by following more practical online resources. The course could be improved by tying the notebook modules more closely to the video content, making it easier for learners to follow along.