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This book is intended to be an initiation to learn machine learning with Python programming for absolute beginners that have no background in programming. It provides the basics of both machine learning and Python programming. It also provides a guide to use Python libraries to build machine learning models. This book has mainly four chapters that will help you understand models of machine learning and to how to carry them out in a Python environment. Chapters 1 and 2 are dedicated to machine learning principles, the third chapter for Python programming and its basic syntax. Finally, the four chapters provide applications of machine learning with Python. The first chapter discusses the fundamentals and concepts of machine learning. You will learn the different learning machine paradigms, namely supervised, unsupervised, and reinforcement paradigms. Basically, this chapter helps enhance understanding the different paradigms of machine learning, the cases when each paradigm is applied. This chapter also explains some widely used algorithms in machine learning and the process to create a model of machine learning. In the second chapter, we will discuss the artificial neural networks, a tool widely used in machine learning. Because, the artificial neural network is a machine learning branch by itself, it is best covered in a separate chapter. You will learn the principle of neural networks, types of neural networks, and how to train these neural networks. This chapter explains in detail the different components of an artificial neural network. In the third chapter, you will tackle Python programming. You will learn why Python is useful to develop machine learning models. You will also learn how to get started with Python, running Python programs, and basic syntax of Python programming. You will also explore some useful platforms that use Python if you prefer using a graphical user interface instead of a command line. In chapter four, you learn how to apply machine learning with Python. This chapter contains some machine learning applications that were discussed in the previous chapters. You will get through detailed examples in order to run machine learning models using Python tools you learned in chapter 3. The examples cover the paradigms of machine learning and steps to develop a multilayer neural network without relying on the pre-coded functions in Python as well as using the built-in function of Python libraries. Through these examples, you will apply Python skills to process and analyze and visualize the dataset. Several books discussing this particular subject exist on the market. Thanks for deciding to read this book. To ensure the book covers the most relevant information for a beginner, a lot of effort was taken. Enjoy reading and learning!