ABSTRACT
Machine Learning (ML), a subfield of computer science and a subset of Artificial Intelligence (AI), has application in many different areas including online shopping, medicine, video surveillance, email spam, etc. The main idea is to develop algorithms that can learn from data and make predictions based on this learning. There are many types of ML algorithms applied in the various domains. They are divided in three main categories, i.e., supervised, unsupervised, and reinforcement. Python has an arsenal of libraries that support the implementation of these algorithms, including NumPy, SciPy, Scikit-learn, TensorFlow. This chapter introduces some of the most important as well as popular ML libraries through the implementation of a series of example cases.
