Hello everyone,
I have a background in Python and algebra and calculus and statistics and probabilities, and I am familiar with pandas and numpy
I just finished course 2 of machine learning algorithms and did all the labs and assignment with attention in order not to miss important part and I guess I got 90% of the idea of those courses.
here is my question, how to master in them? how to implement different models to get fluent in the topics? where to start?
or is it better to also finish course 3 before getting master in course1 and 2?
Hi @Ali_Jandaghi , welcome to the community.
To really master the concepts, you’ll need plenty of hands-on practice. Start by working with real datasets from platforms like Kaggle or the UCI Machine Learning Repository. Build small projects using different algorithms, compare their performance, and try to understand why one model performs better than another.
It also helps to read and reproduce projects from GitHub. Reimplementing other people’s code, making changes, and experimenting with different approaches will deepen your understanding far more than just reading about the algorithms.
Most importantly, keep building. The more problems you solve, the more fluent you’ll become with choosing, implementing, and tuning machine learning models. You can do all these alongside proceeding to the next course in the Specialization.