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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...

Key benefits

Delve into machine learning with this comprehensive guide to scikit-learn and scientific Python

Master the art of data-driven problem-solving with hands-on examples

Foster your theoretical and practical knowledge of supervised and unsupervised machine learning algorithms

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.

Who is this book for?

This book is for data scientists, machine learning practitioners, and anyone who wants to learn how machine learning algorithms work and to build different machine learning models using the Python ecosystem. The book will help you take your knowledge of machine learning to the next level by grasping its ins and outs and tailoring it to your needs. Working knowledge of Python and a basic understanding of underlying mathematical and statistical concepts is required.

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

What do you get with eBook?

Instant access to your Digital eBook purchase

Download this book in EPUB and PDF formats

Access this title in our online reader with advanced features

DRM FREE - Read whenever, wherever and however you want

AI Assistant (beta) to help accelerate your learning

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Adam Powell Sep 21, 2020

The perfect read for an analyst that wants to transition into machine learning. It broadly covers all the key algorithms with an insightful practitioner's perspective. Highly recommended!

Amazon Verified review

Sara Oct 25, 2020

This book is an excellent introductory book for data scientists learning machine learning and any curious people wanting to know what it takes to develop and build machine learning algorithms.The book covers the basic concepts, the terminology, and goes into detail about implementing these algorithms using Python and one of the most well-known machine learning libraries Scikit-learn.The book introduces these complex concepts is straightforward and easy to understand, even if the reader doesn't have any maths background.It contains many real-life applications of machine learning with code that the reader can follow and use to build more significant applications.I would recommend this book to anyone getting started with machine learning. This book will give you the concrete knowledge you need to start building your own machine learning applications.

Przemyslaw Chojecki Sep 02, 2020

If you've already did a couple of data science projects, had a basic understanding of Python, did some visualisation and want to go deeper into some details of what it means to analyse data, then this book is for you.This is a practical guide to both supervised and unsupervised learning with plenty of examples in code.The main focus is on imperfect data and how to make sense of these imperfections through various machine learning algorithms.The author discusses standard data science algorithms using scikit-learn library which gives a coherent overview of the subjest. You will learn decision trees, KNN classification, Naive Bayes and much more; applied to classical datasets like Iris dataset, Boston housing prices or Fashion-MNIST.Recommended for beginning data scientists!

Matthew Emerick Aug 10, 2020

About This BookThis is a guide for newcomers to machine learning who want to start learning one of the core Python ML libraries. This book focuses on scikit-learn, though discusses other related libraries as needed. We see several algorithms developed from the ground up, which aids in understanding what is going on and why things work the way they do.Who is This For?The author makes the following claim, taken directly from the preface: "This book is for machine learning data scientists who want to master the theoretical and practical sides of machine learning algorithms and understand how to use them to solve real-life problems." This a big claim for a 384 page introductory book.Why Was This Written?scikit-learn is a powerful library that is essential for machine learning in Python. Every ML developer needs to learn the basics, and this can be a good book for that. Several machine learning algorithms, both supervised and unsupervised, are covered in a fair amount of detail. As was mentioned, the book intends the reader to master machine learning and does indeed introduce the reader to important terms, algorithms, and some of the theory.OrganizationThe book starts out with a very brief introduction to machine learning, finishing up the chapter with instructions on how to set up your computer to run the included code. The book then surges forward by discussing its first algorithm. I did find it odd that a chapter on preparing your data is found after the first couple algorithms. The book focuses on supervised machine learning with only the last three chapters discussing unsupervised learning.Did This Book Succeed?I think that the computer setup would have been better left in the preface while the ML introduction could have been expanded. After reading this book, the reader will have an understanding of a selection of algorithms, enough to get a junior software developer role on a team, but will need to dig deeper to a better understanding. If you simply want to learn a bit about the subject, then this is a good book.Rating and Final ThoughtsThis is a useful book, but I feel it misses the mark by trying to do too many things. If one were to truly master machine learning, this book might be an aid early on, but is only one step. Important concepts were discussed, and even bolded, but there was no glossary at the end to make it easy to look up the terms later on; you have to use the index to find where the term was originally used and look there. There are a great many algorithms not discussed and theory left out.I have to give this book a 3.5 out of 5. It's useful, especially to the ML beginner, but it misses its own goal. A cookbook style might have worked better, focusing on either ML or scikit-learn.

Hands-On Machine Learning with scikit-learn and Scientific Python Toolkits: A practical guide to implementing supervised and unsupervised machine learning algorithms in Python

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