Introduction to machine learning with python

Introduction to machine learning with python

Aug 18, 2021

Author: Andreas C. Müller & Sarah Guido Published: 2017 Pages: 379 Language: English File Size: 31.5 MB

Machine learning is an integral part of many commercial applications and research projects today, in areas ranging from medical diagnosis and treatment to finding your friends on social networks. Many people think that machine learning can only be applied by large companies with extensive research teams. In this book, we want to show you how easy it can be to build machine learning solutions yourself, and how to best go about it. With the knowledge in this book, you can build your own system for finding out how people feel on Twitter, or making predictions about global warming. The applications of machine learning are endless and, with the amount of data avail‐able today, mostly limited by your imagination.

Who Should Read This Book
This book is for current and aspiring machine learning practitioners looking to implement solutions to real-world machine learning problems. This is an introduc‐
tory book requiring no previous knowledge of machine learning or artificial intelli‐ gence (AI). We focus on using Python and the scikit-learn library, and work
through all the steps to create a successful machine learning application. The meth‐ods we introduce will be helpful for scientists and researchers, as well as data scientists working on commercial applications. You will get the most out of the book if you are somewhat familiar with Python and the NumPy and matplotlib libraries. We made a conscious effort not to focus too much on the math, but rather on the practical aspects of using machine learning algorithms. As mathematics (probability theory, in particular) is the foundation upon which machine learning is built, we won’t go into the analysis of the algorithms in great detail. If you are interested in the mathematics of machine learning algorithms, we recommend the book The Elements of Statistical Learning (Springer) by Trevor Hastie, Robert Tibshirani, and Jerome Friedman, which is available for free at the authors’ website. We will also not describe how to write machine learning algorithms from scratch, and will instead focus on viihow to use the large array of models already implemented in scikit-learn and other libraries.

Why We Wrote This Book
There are many books on machine learning and AI. However, all of them are meant for graduate students or PhD students in computer science, and they’re full of
advanced mathematics. This is in stark contrast with how machine learning is being used, as a commodity tool in research and commercial applications. Today, applying machine learning does not require a PhD. However, there are few resources out there that fully cover all the important aspects of implementing machine learning in practice, without requiring you to take advanced math courses. We hope this book will help people who want to apply machine learning without reading up on years’ worth of calculus, linear algebra, and probability theory.

Navigating This Book
This book is organized roughly as follows:
• Chapter 1 introduces the fundamental concepts of machine learning and its applications, and describes the setup we will be using throughout the book.
• Chapters 2 and 3 describe the actual machine learning algorithms that are most widely used in practice, and discuss their advantages and shortcomings.
• Chapter 4 discusses the importance of how we represent data that is processed by machine learning, and what aspects of the data to pay attention to.
• Chapter 5 covers advanced methods for model evaluation and parameter tuning, with a particular focus on cross-validation and grid search.
• Chapter 6 explains the concept of pipelines for chaining models and encapsulating your workflow.
• Chapter 7 shows how to apply the methods described in earlier chapters to text data, and introduces some text-specific processing techniques.
• Chapter 8 offers a high-level overview, and includes references to more advanced topics.


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