Machine Learning For Beginners Guide Algorithms

Supervised & Unsupervsied Learning. Decision Tree & Random Forest Introduction

William Sullivan

• Publisher: PublishDrive
• ISBN:
  9781975632328
• Topic: Computer Science
• Subtopic: Programming Algorithms
• Index: Computer Science

Machines can LEARN?!?!
Machine learning occurs primarily through the use of " algorithms" and other elaborate procedures Whether you're a novice, intermediate or expert this book will teach you all the ins, outs and everything you need to know about machine learning Note: Bonus chapters included inside! Instead of spending hundreds or even thousands of dollars on courses/materials why not read this book instead? Its a worthwhile read and the most valuable investment you can make for yourself Other books easily retail for $50-$100+ and have far less quality content. This book is by far superior and exceeds any other book available for beginners.
What You'll Learn

• Supervised Learning

• Unsupervised Learning

• Reinforced Learning

• Algorithms

• Decision Tree

• Random Forest

• Neural Networks

• Python

• Deep Learning

• And much, much more!

This is the most comprehensive and easy to read step by step guide in machine learning that exists. Learn from one of the most reliable programmers alive and expert in the field You do not want to miss out on this incredible offer!

Machine Learning For Beginners Guide Algorithms:

Supervised & Unsupervised Learning Decision Tree & Random Forest Introduction

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Introduction:

Chapter 1: About Machine Learning

What is Machine Learning?

History:

Chapter 2: Machine Learning Basics

Differences between Traditional Programming and Machine Learning

Elements of Machine Learning

Types and Kinds of Machine Learning

Machine Learning in Practice

Learning models

Sample applications of machine learning

Chapter 3: Machine Learning: Algorithms

Ensemble Learning Method

Supervised Learning

Unsupervised Learning

Semi-Supervised Learning

Algorithms Grouped By Similarity

Chapter 4: Decision Tree and Random Forests: Part One

What is a Decision Tree? How exactly does it work?

Decision Tree, Algorithms

Types of Decision Trees

Terminology and Jargon related to Decision Trees

Advantages

Disadvantages

Regression Trees vs. Classification Trees

Where does the tree get split?

Gini Index

Chi-Square

Information Gain, Decision Tree

Reduction in Variance

Chapter 5: Decision Trees: Part 2

Tree Pruning

Linear models or tree based models?

Ensemble methods:

What is Bagging? How does it work?

Chapter 6: Decision Trees: Part Three (Random Forests)

Workings of Random Forest:

Advantages of Random Forest

Disadvantages of Random Forest

What is Boosting? How does it work?

By utilizing average or weighted average

How do we choose a different distribution for each round?

GBM or XGBoost: Which is more powerful?

How to work with GBM in R and Python?

Chapter 7: Deep Learning

The difference between Machine Learning, Deep Learning, and AI:

Chapter 8: Digital Neural Network and Computer Science

Applications of ANN

Advantages of ANN

Risks associated with ANN

Types of Artificial Neural Networks

Conclusion

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Please note the information contained within this document is for educational purposes only. 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. Please consult a licensed professional before attempting any techniques outlined in this book.

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About The Series

"Machine Learning For Beginners Guide Algorithms" is the first installment in this book series, meticulously developed by me and my passionate software loving engineering team.

This series will provide you in depth insights and a full introduction to the world of machine learning.

Whenever we cover a new concept, topic or formula I ensure that a full in depth explanation is provided and push your understanding of the modern technology to a whole new level. Diagrams are provided to help maximize and visualize concepts, enhancing the learning process.

Please understand that this series will challenge your way of thinking. Especially, in later books we will dive into extremely technical topics that will inspire you.

I just need three things from you before we can begin. Please stay committed, focused and passionate throughout the duration of all subject matter.

I have worked tirelessly structuring all this content together in the most practical, easy to read and step by step guide. I try to keep "high tech jargon" to a minimal and keep the flow of reading seamless and uninterrupted.

This is the first publication officially released to the public - stay tuned for the newest releases by following my author page or simply find the author page directly under the book on Amazon.com

Feel free to comment and give feedback on potential new topics you'd like to learn about. I gather all input given by readers and take it into serious consideration when writing a new book.

Whenever you are ready, let's dive into the world of machine learning together! Turn the page. :)

Introduction

I want to thank you for choosing this book, ‘Machine learning for beginners - Algorithms, Decision Tree & Random Forest Introduction.’

By choosing this book, you have made the right decision, as you will learn many new, innovative and exciting things about the world of technology and computers. It will help you learn the basics of AI and machine learning in a simple, entertaining and informative way.

Currently one of the most talked about topics in the world of technology, machine learning is a promising concept. But along with the promises and benefits, it is also often associated with controversies and debates. People who are not aware of the nature and advantages of machine learning or have received their information from untrustworthy sources often look down on machine learning and are scared of it as well. However, all the strange and bizarre things that you have heard about machine learning are probably just myths and false apprehensions.

This book will try to do away with such apprehensions by showing how machine learning is perhaps the best thing that could happen to the world of technology right now. You will get answers to all your questions about machine learning and more. So, rather than making assumptions, you will learn and understand what machine learning is all about and make your own decisions.

So let’s read on.

Chapter 1: About Machine Learning

One of the best features of today’s era of technology is its flexibility and adaptableness. A new scientific innovation comes out almost every day. This ever-changing nature of scientific and technological world changes the trajectory of the world every day. Things that were considered dreams and fiction once are now rapidly turning into reality. Human beings are slowly but steadily trying to defeat nature at its own game. However, one field remains to be conquered. We still have not managed to conquer the world of machine learning or AI. However, it has become a buzzword now, and the whole of the world is talking about it. Not everyone is excited about it though. Most people are worried or scared of it. However, there is no need to be afraid of machine learning or AI, as it will help humanity to achieve things that we cannot even currently imagine.

What is Machine Learning?

If you check the search results for the most popular keywords of 2016, you will find that machine learning and AI are leading the figures by a large margin. This steady rise in the fame of machine learning is because of its rising use in our daily lives. It is nowadays being used in various devices and machines as well as gadgets. However, the general population is still are wary of it. So, to do away with such myths, let us have a look at the brief history of machine learning.

As per the 1959 definition of Arthur Samuel, machine learning can be defined as a process of inputting data to the computer systems in a way that the computer will learn the ability to process and perform the activity in the future without being explicitly programmed or being fed with similar or extra data. What this means in simple words is that it will allow computers to develop a ‘mind’ of their own and allow them to “think.” Sounds scary but it isn’t.

If computers are provided with the ability to think, they become smarter and thus easier to use. Their functionality will increase by a large margin, and they become an integral asset for humanity. Machine learning can be used in almost all the fields of epistemology. Right now, it is being used in areas such as cheminformatics, computational anatomy, gaming, adaptive websites, natural language processing, robot movement and locomotion, medical diagnosis, sequence mining, behavior analysis, linguistics, translation, fraud detection, etc. The list goes on.

History:

The history of machine learning can be traced to the birth of another related field- AI or artificial intelligence. It is safe to say that both of these fields were born at the same time and then got separated over time. Many scientists studying AI in the beginning slowly shifted to...

Table of contents

• About The Series
• Table of Contents
• Introduction
• BONUS - Business Intelligence