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Introduction to Machine Learning

Machine learning is everywhere. When you book a flight ticket, an algorithm decides the price you are going to pay for it. When you apply for a loan, machine learning may decide whether you are going to get it or not. When you scroll through your Facebook timeline, it picks which advertisements to show to you. Machine learning also plays a big role in your Google search results. It organizes your email's inbox and filters out spam, it goes through your resumé before recruiters when you apply for a job, and, more recently, it has also started to play the role of your personal assistant in the form of Siri and other virtual assistants.

In this book, we will learn about the theory and practice of machine learning. We will understand when and how to apply it. To get started, we will look at a high-level introduction to how machine learning works. You will then be able to differentiate...

Description

Machine learning is applied everywhere, from business to research and academia, while scikit-learn is a versatile library that is popular among machine learning practitioners. This book serves as a practical guide for anyone looking to provide hands-on machine learning solutions with scikit-learn and Python toolkits. The book begins with an explanation of machine learning concepts and fundamentals, and strikes a balance between theoretical concepts and their applications. Each chapter covers a different set of algorithms, and shows you how to use them to solve real-life problems. You’ll also learn about various key supervised and unsupervised machine learning algorithms using practical examples. Whether it is an instance-based learning algorithm, Bayesian estimation, a deep neural network, a tree-based ensemble, or a recommendation system, you’ll gain a thorough understanding of its theory and learn when to apply it. As you advance, you’ll learn how to deal with unlabeled data and when to use different clustering and anomaly detection algorithms. By the end of this machine learning book, you’ll have learned how to take a data-driven approach to provide end-to-end machine learning solutions. You’ll also have discovered how to formulate the problem at hand, prepare required data, and evaluate and deploy models in production.

What you will learn

Understand when to use supervised, unsupervised, or reinforcement learning algorithms Find out how to collect and prepare your data for machine learning tasks Tackle imbalanced data and optimize your algorithm for a bias or variance tradeoff Apply supervised and unsupervised algorithms to overcome various machine learning challenges Employ best practices for tuning your algorithm’s hyper parameters Discover how to use neural networks for classification and regression Build, evaluate, and deploy your machine learning solutions to production

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