1. The basics of Jupyter notebooks and processing data in Python
2. The basic concepts of linear regression
3. Non-linear regression
4. Over-fitting
5. Classification

1. Time series prediction
2. Recommender systems
3. Anomaly detection

The goal of each exercise is to learn the basic concepts behind the topics above. Most of the code will be provided for you so this is not a coding exercise. So if you don’t have a Python background this won’t be an issue. You will only need to implement the essential parts of the code to really understand the concepts. Typically this is just around 5 lines of code per exercise.

At the beginning of each exercise I will introduce you to the task at hand and explain what we are trying to accomplish. During the exercise I will answer all the questions you might have so you don’t need to be stuck on silly coding mistakes. And once the majority of people have finished the exercise I will go over the solutions and I will provide a bit more context to how to apply this in real life tasks.