Great to start with the basics but needed a little more explanation on libraries

This course should be called the basics of how to use JupyterLab rather than ML basics. The instructor goes through some rudimentary data preprocessing, but there is very little theoretical explanation as to why the preprocessing should be done, and for a beginner it would be difficult to understand why the instructor did that. For example, there was no explanation as to why the "stratify" option was used when splitting the training and test data with the train_test_split() function. I was able to figure out the meaning of the option and why it matters by Google it, but I think it should have been explained in the lecture. This is just one example. Overall, there was too little explanation of the theoretical background in this class.

This is a really good guided project to start with Kaggle Competition. I learnt all the basics require to start with Kaggle.

Need to add some more explanation about Kaggle

perfect