Book Image

Hands-On Machine Learning with scikit-learn and Scientific Python Toolkits

By : Tarek Amr
Book Image

Hands-On Machine Learning with scikit-learn and Scientific Python Toolkits

By: Tarek Amr

Overview of this book

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.
Table of Contents (18 chapters)
1
Section 1: Supervised Learning
8
Section 2: Advanced Supervised Learning
13
Section 3: Unsupervised Learning and More

The model development life cycle

When asked to solve a problem using machine learning, data scientists achieve this by following a sequence of steps. In this section, we are going to discuss those iterative steps.

Understanding a problem

"All models are wrong, but some are useful."
– George Box

The first thing to do when developing a model is to understand the problem you are trying to solve thoroughly. This not only involves understanding what problem you are solving, but also why you are solving it, what impact are you expecting to have, and what the currently available solution isthat you are comparing your new solution to. My understanding of what Box said when he stated that all models are wrong is that a model is just an approximation of reality by modeling one or more angles of it. By understanding the problem you are trying to solve, you can decide which angles of reality you need to model, and which ones you can tolerate...