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

Understanding linear models

To be able to explain linear models well, I would like to start with an example where the solution can be found using a system of linear equations—a technique we all learned in school when we were around 12 years old. We will then see why this technique doesn't always work with real-life problems, and so a linear regression model is needed. Then, we will apply the regression model to a real-life regression problem and learn how to improve our solution along the way.

Linear equations

"Mathematics is the most beautiful and most powerful creation of the human spirit."
– Stefan Banach

In this example, we have five passengers who have taken a taxi trip. Here, we have a record of the distance each taxi covered in kilometers and the fair displayed on its meter at the end of each trip:

We know that taxi meters usually start with a certain amount and then they...