# Book Machine Learning

Hi, Khabrozhiteli to us from the printing house finally came a novelty from Henrik Brink, Joseph Richards and Mark Feverowolf.

This book will allow programmers, data analysts, statisticians, data processing specialists and everyone else to apply machine learning to solve real problems, or even just understand what it is. Readers, without resorting to a deep theoretical study of specific algorithms, will gain practical experience in processing real data, modeling, optimizing and deploying machine learning systems. For those who are interested in theory, we discuss the mathematical basis of machine learning, explain some algorithms and provide links to materials for additional reading. The main emphasis is on practical results in solving tasks.

The book is intended for those who want to apply machine learning to solve various problems. It describes and explains the processes, algorithms and tools related to the basic principles of machine learning. Attention is focused not on the methods of writing popular algorithms, but on their practical application. Each stage of building and using machine learning models is illustrated by examples, the complexity of which varies from simple to intermediate.

Part I “The sequence of actions in machine learning” introduces the five stages of the main sequence of machine learning:

• Chapter 1, “What is machine learning?” Describes what machine learning is and what it is for.

• Chapter 2, “Real Data,” discusses in detail the characteristic stages of data preparation for machine learning models.

• Chapter 3, “Modeling and Forecasting,” teaches using common algorithms and libraries how to create simple ML models and generate forecasts.

• In Chapter 4, “Evaluating and Optimizing a Model,” ML models are discussed in detail to evaluate and optimize their performance.

• Chapter 5, “Fundamentals of Character Design,” describes how to increase the amount of raw data using information from the task before us.

Part II “Practical Application” introduces models scaling techniques, as well as techniques for extracting features from text, images, and time series, which increase the efficiency of solving many modern problems with machine learning. This part contains three chapters with practical examples.

• Chapter 6, “Example: Tipping for Taxi Drivers,” is the first to fully examine the example. We will try to predict the taxi driver’s chances of getting a tip.

• Chapter 7, Advanced Feature Design, introduces more advanced feature design techniques to extract values from texts, images, and time series.

• In Chapter 8, An Example of Natural Language Processing, advanced feature design techniques are used to predict the tonality of movie reviews.

• Chapter 9, “Scaling up the machine learning process,” introduces technicians who enable ML systems to work with large amounts of data, providing faster forecasting speed and reducing their wait times.

• In chapter 10, “Digital Advertising Example,” a large amount of data is used to build a model that predicts the likelihood of a transition through an advertising banner.

Those who do not yet have experience in the field of machine learning, chapters 1 through 5 will introduce the processes of preparing and researching data, designing features, modeling and evaluating models. The Python code examples use such popular libraries as pandas and scikit-learn. Chapters 6 through 10 include three practical examples of machine learning along with advanced topics such as feature design and optimization. Since the main computational complexity is encapsulated in libraries, the given code fragments are easy to adapt to your own ML-applications.

»More information on the book can be found on the publisher’s website

» Table of Contents

» Excerpt

For Khabrozhiteley 25% off coupon -

This book will allow programmers, data analysts, statisticians, data processing specialists and everyone else to apply machine learning to solve real problems, or even just understand what it is. Readers, without resorting to a deep theoretical study of specific algorithms, will gain practical experience in processing real data, modeling, optimizing and deploying machine learning systems. For those who are interested in theory, we discuss the mathematical basis of machine learning, explain some algorithms and provide links to materials for additional reading. The main emphasis is on practical results in solving tasks.

The book is intended for those who want to apply machine learning to solve various problems. It describes and explains the processes, algorithms and tools related to the basic principles of machine learning. Attention is focused not on the methods of writing popular algorithms, but on their practical application. Each stage of building and using machine learning models is illustrated by examples, the complexity of which varies from simple to intermediate.

### Book structure

Part I “The sequence of actions in machine learning” introduces the five stages of the main sequence of machine learning:

• Chapter 1, “What is machine learning?” Describes what machine learning is and what it is for.

• Chapter 2, “Real Data,” discusses in detail the characteristic stages of data preparation for machine learning models.

• Chapter 3, “Modeling and Forecasting,” teaches using common algorithms and libraries how to create simple ML models and generate forecasts.

• In Chapter 4, “Evaluating and Optimizing a Model,” ML models are discussed in detail to evaluate and optimize their performance.

• Chapter 5, “Fundamentals of Character Design,” describes how to increase the amount of raw data using information from the task before us.

Part II “Practical Application” introduces models scaling techniques, as well as techniques for extracting features from text, images, and time series, which increase the efficiency of solving many modern problems with machine learning. This part contains three chapters with practical examples.

• Chapter 6, “Example: Tipping for Taxi Drivers,” is the first to fully examine the example. We will try to predict the taxi driver’s chances of getting a tip.

• Chapter 7, Advanced Feature Design, introduces more advanced feature design techniques to extract values from texts, images, and time series.

• In Chapter 8, An Example of Natural Language Processing, advanced feature design techniques are used to predict the tonality of movie reviews.

• Chapter 9, “Scaling up the machine learning process,” introduces technicians who enable ML systems to work with large amounts of data, providing faster forecasting speed and reducing their wait times.

• In chapter 10, “Digital Advertising Example,” a large amount of data is used to build a model that predicts the likelihood of a transition through an advertising banner.

### How to read this book

Those who do not yet have experience in the field of machine learning, chapters 1 through 5 will introduce the processes of preparing and researching data, designing features, modeling and evaluating models. The Python code examples use such popular libraries as pandas and scikit-learn. Chapters 6 through 10 include three practical examples of machine learning along with advanced topics such as feature design and optimization. Since the main computational complexity is encapsulated in libraries, the given code fragments are easy to adapt to your own ML-applications.

### About Authors

**Henrik Brink**is a data processing and analysis specialist and software developer with vast practical experience in machine learning in both manufacturing and research.**Joseph Richards**is a senior fellow in applied statistics and predictive analytics. Henrik and Joseph co-founded Wise.io, a company that develops machine learning solutions for the industry.**Mark Feverolf**- Founder and president of Numinary Data Science, a company specializing in data management and predictive analytics. He worked as a statistician and developer of analytical databases in the fields of social sciences, chemical engineering, information system performance, volume planning, cable television and online advertising applications.»More information on the book can be found on the publisher’s website

» Table of Contents

» Excerpt

For Khabrozhiteley 25% off coupon -

**Machine Learning**