We have long been planning to collaborate and write a book on machine learning. This field has grown and expanded immensely since we started learning these topics almost a decade ago. We realized that, as lifelong learners ourselves, the initial few steps in any field require a much clearer source that shows a path clearly. This also requires a crisp set of explanation and occasional ideas to expand the learning experience by reading, learning, and utilizing what you have learned. We have used Python for a long duration in our academic life and professional careers in software development, data science, and machine learning. Through this book, we have made a very humble attempt to write a step-by-step guide on the topic of machine learning for absolute beginners. Every chapter of the book has the explanation of the concepts used, code examples, explanation of the code examples, and screenshots of the outputs.

The first chapter covers the setup of the Python environment on different platforms. The second chapter covers NumPy and Ndarrays. The third chapter explores visualization with Matplotlib. The fourth chapter introduces us to the Pandas data science library. All these initial chapters build the programming and basic data crunching foundations that are one of the prerequisites for learning machine learning.

The next section discusses traditional machine learning approaches. In Chapter 5, we start with a bird’s-eye view of the field of machine learning followed by the installation of Scikit-learn and a short and quick example of a machine learning solution with Scikit-learn. Chapter 6 elaborates methods to help you understand and transform structural, textual, and image data into the format that’s acceptable by machine learning libraries. In Chapter 7, we introduce supervised learning methods, starting with linear regression for regression problems and logistic regression and decision trees for classification problems. In each of the experiments, we also show how to plot visualizations that the algorithm has learned with the use of decision boundary plots. The eighth chapter ponders over further fine-tuning of machine learning models. We explain some ideas for measuring the performance of the models, issues of overfitting and underfitting, and approaches for handling such issues and improving the model performance. The ninth chapter continues the discussion of supervised learning methods especially focusing on naive Bayes and Support Vector Machines. The tenth chapter explains ensemble learning methods, which are the solutions that combine multiple simpler models to produce a performance better than what they might offer individually. In the eleventh chapter, we discuss unsupervised learning methods, specifically focusing on dimensionality reduction, clustering, and frequent pattern mining methods. Each part contains a complete example of implementing the discussed methods using Scikit-learn.

The last section begins with introducing the basic ideas of neural network and deep learning in the twelfth chapter. We introduce a highly popular open source machine learning framework, PyTorch, that will be used in the examples in the subsequent chapters. The thirteenth chapter begins with the explanation of artificial neural networks and thoroughly discusses the theoretical foundations of feedforward and backpropagation, followed by a short discussion on loss functions and an example of a simple neural network. In the second half, we explain how to create a multilayer neural network that is capable of identifying handwritten digits. In the fourteenth chapter, we discuss convolutional neural networks and work through an example for image classification. The fifteenth chapter discusses recurrent neural networks and walks you through a sequence modeling problem. In the final, sixteenth chapter, we discuss strategies for planning, managing, and engineering machine learning and data science projects. We also discuss a short end-to-end example of sentiment analysis using deep learning.

If you are new to the subject, we highly encourage you to follow the chapters sequentially as the ideas build upon each other. Follow through all the code sections, and feel free to modify and tweak the code structure, datasets, and hyperparameters. If you already know some of the topics, feel free to skip to the topics of your interest and examine the relevant sections thoroughly. We wish you the best for your learning experience.