Will cover some mathematical underpinnings and tools used during the course. Will cover various data sources and how to read out data from them and put it back. Also some basic introduction to obtaining data from APIs and scraping.
Will cover how to obtain and inspect data to understand its quality. Will also cover how data can be cleaned up and augmented.
Will cover common training methods and models from the sklearn library used to regression and classification problems. Will cover clustering techniques using the sklearn library.
Also, some general sklearn techniques
Will provide an introduction to deep learning techniques using the keras library