Machine Learning - A Comprehensive, Step-by-Step Guide to Intermediate Concepts and Techniques in Machine Learning: 2

By Peter Bradley

Do you want to impress the processes that you are working on? Do you want to make your machines more intelligent? If your answer to any of those questions is yes, then you have come to the right place.

This book is a sequel to the book titled 'Machine Learning: A Step-by-Step guide.' In the first book, you gathered information on what machine learning is, and the different algorithms that one needs to know. This book is written for those who have a basic understanding of what machine learning is.

In this book, you will gather information on:

Practical examples of machine learning

How to build a machine learning algorithm in Python

An introduction to deep learning and neural networks

How to create a neural network in Python using Keras

And much more

The book breaks the process of building a machine-learning model in Python into simple steps. These steps will help you build your very own machine-learning model from scratch. You should first build the model using the programs and scripts given in the book before you build your model from scratch. If you want to learn more about what you can do with machine learning, then this is the perfect book for you.

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• Language: English
• Publisher: Peter Bradley
• Release date: Jun 24, 2019
• ISBN: 9781393521549

Peter Bradley

Peter Bradley was the Labour MP for The Wrekin between 1997 and 2005. More recently, he co-founded and directed Speakers’ Corner Trust, a charity which promotes freedom of expression, open debate and active citizenship in the UK and developing democracies. He has written, usually on politics, for a wide range of publications, including The Times, The Guardian, The Independent, The New Statesman and The New European.

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© Copyright 2018 Peter Bradley All rights reserved.

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This book is copyright protected. This is only for personal use. You cannot amend, distribute, sell, use, quote or paraphrase any part of the content within this book without the consent of the author.

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Please note the information contained within this document is for educational and entertainment purposes only. Every attempt has been made to provide accurate, up to date and complete, reliable information. No warranties of any kind are expressed or implied. Readers acknowledge that the author is not engaging in the rendering of legal, financial, medical or professional advice. The content of this book has been derived from various sources. Please consult a licensed professional before attempting any techniques outlined in this book.

By reading this document, the reader agrees that under no circumstances is the author responsible for any losses, direct or indirect, which are incurred as a result of the use of information contained within this document, including, but not limited to, —errors, omissions, or inaccuracies.

Table of Contents

Introduction

Chapter One: Practical Examples of Machine Learning

Chapter Two: Advantages and Disadvantages of Machine Learning

Chapter Three: How to Create and Train Machine Learning Models

Chapter Four: An Introduction to Deep Learning

Chapter Five: An Introduction to Neural Networks

Chapter Six: Building Your First Algorithm in Python

Chapter Seven: How To Build An Algorithm in Python

Chapter Eight: How To Evaluate Machine Learning Algorithms in Python?

Chapter Nine: How to Develop a Neural Network in Python using Keras

Conclusion

Sources

Introduction

I want to thank you for choosing this book, ‘Machine Learning - A Comprehensive, Step-by-Step Guide to Intermediate Concepts and Techniques in Machine Learning.’

In the first part of the book, I covered the basics of machine learning. This book is an intermediate level and will help you gather a deeper understanding of what machine learning is. You will also learn about how you can build a machine learning algorithm or model in Python. It can be difficult to build an algorithm from scratch, but this book will help you every step of the way. This book provides a step-by-step process that you need to follow to build the algorithm from scratch.

This book also provides some information about deep learning and artificial neural networks. It also provides information about the different types of neural networks that you can use to train machines. You will also learn how to build an algorithm for a deep learning model.

Thank you for purchasing the book. I hope you gather all the information you were looking for.

Chapter One: Practical Examples of Machine Learning

One can use machine learning and artificial intelligence in many ways, and there are many applications and tools that use the concepts of machine learning to improve our lives. These tools also help businesses make informed decisions and optimize their operations. In the first part of the book, we have covered the different uses of machine learning, and some applications of machine learning. This chapter provides a deeper understanding of how machine learning can be used in different industries.

Consumer Goods

The Hello Barbie doll uses machine learning, advanced analytics and natural language processing to listen and respond to a child. The microphone in the doll’s necklace records what the child says and transmits that information to the servers in ToyTalk. The machine analyzes the recording and determine what the response should be using 8,000 lines of responses. The servers will then transmit the response back to the doll in less than a second to respond to the child. The answers to some questions like what the doll’s favorite food is are stored in the database. These answers are used in later conversations.

Coca-Cola’s extensive product list and global market make it one of the largest beverage companies in the world. The company creates large volumes of data, and it has embraced a lot of new technology that helps it put the data to good use. The company uses the data to develop new products and also uses augmenting reality an artificial learning in the bottling plants.

Heineken, the Dutch company, is a worldwide leader and has been a leader for over 150 years. The company wants to use the large volumes of