Sources to learn more about scikit-learn regression/classification

I learned a lot in this first course and just stared the next one that jumps straight to advanced learning algorithms. Do you have any recommendations on how I can deepend my skills of the first course? For my work, models like random forest and grandient boosting are of particular importance, which I think are not the scope of this spezialisation.

Please deep into the next course. In the second course, you will learn about many useful methods to speed up learning, solve the bias and variance, reduce overfitting, and also optimization algorithms such as GD with momentum, RMSProp, Adam and so on.

First course seems brief introduction of this specialization and now you are at good position to start deeper knowledge and gain practical experience with Deep Learning.

I am also keep learning in this specialization, and Now I almost done second course.
Hope you are success in this specialization.
Best Regards.

Hi @manuel.y.schar , the second course of the MLS covers random forest and xgboost, and there is no better way than to practice to improve the skills of using them, and this usually begins with finding an interesting dataset, and as one of our mentors suggested, the UCI Machine Learning Repository, and Kaggle are good places to start with for finding a dataset.