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
Making Decisions with Linear Equations

The method of least squares regression analysis dates back to the time of Carl Friedrich Gauss in the 18th century. For over two centuries, many algorithms have been built on top of it or have been inspired by it in some form. These linear models are possibly the most commonly used algorithms today for both regression and classification. We will start this chapter by looking at the basic least squares algorithm, then we will move on to more advanced algorithms as the chapter progresses.

Here is a list of the topics covered in this chapter:

  • Understanding linear models
  • Predicting house prices in Boston
  • Regularizing the regressor
  • Finding regression intervals
  • Additional linear regressors
  • Using logistic regression for classification
  • Additional linear classifiers