Chapter 1, Machine Learning Fundamentals, introduces the CRISP-DM model, which presents an industry standard framework/workflow for any data science, ML, or deep learning project. We will also touch upon various important concepts covering the fundamentals in the ML landscape such as exploratory data analysis, feature extraction and engineering, evaluation metrics, and so on.
Chapter 2, Deep Learning Essentials, provides a whirlwind tour of deep learning essentials, providing an overview of the basic building blocks of neural networks and also how deep neural networks are trained. Starting from the basics of how a single neural unit works, important concepts like activation functions, loss functions, optimizers and neural ...
