I now feel comfortable setting up logistic regression in Python. Some advanced topics like regularization weren’t covered in much depth.

The course builds a strong foundation by explaining what supervised learning is and how models learn from labeled data.

Hyperparameter tuning and feature engineering may feel too shallow in beginner courses.

Code examples make it easier to understand how supervised learning models work.

Coding examples help connect the theory to practical implementation.

Decent coverage of theory with practical Python examples.

However, some users feel the coverage is a bit surface-level, meaning it teaches the basics very clearly but doesn’t go much deeper into model tuning, regularization, or advanced supervised learning workflows. (inferred from similar course feedback)

Some explanations feel a little quick, especially when moving from theory to implementation. A few more practical examples or visual breakdowns would have made the transitions smoother.

Many beginners report that learning how to transform, encode, and prepare features made their models significantly better and was one of the most actionable skills gained.

After taking this, I was confident enough to try logistic regression on my own datasets. I even started exploring feature engineering on my own.

I appreciated the balance between theory and practical implementation, which helps in understanding how models work in real scenarios.

Overall, it’s a solid course for building foundational skills in logistic regression and supervised machine learning using Python.

This course helped me understand the basics of supervised learning — especially how logistic regression works in practice.

The confusion matrix and ROC discussions made key concepts clearer. I wished there were more real-world case studies.

The course introduces logistic regression and supervised learning concepts in a simple and beginner-friendly way.

Independent mini-courses (like ImpoDays) give concise, clear introductions without overwhelming length.

Working through each step of the ML process made the whole pipeline feel logical, not intimidating.