Section 1, Getting Started with Machine Learning, discusses the general framework for a machine learning project. In this section, we will learn how to systematically approach a machine learning problem. In addition, this section introduces machine learning common terminology such as training and evaluating a model, and splitting data into train and test sets.
Section 2, Setting Up the Environment, introduces Jupyter notebooks, to create and share your code. Jupyter is an open-source web application that allows you to create and share documents that contain live code, visualizations and narrative text. We will use Google Colab environment for this purpose. Google Colab is a free cloud service that supports free GPU. We will introduce the concept of GPUs in later chapters. You can develop deep learning applications using popular libraries such as Keras, TensorFlow without installing Python or any libraries on your local computer. This chapter also entails a sample notebook that contains a simple machine learning model to get familiar with creating models and testing them using Jupyter notebooks on Google Colab environment.

Introduction