Machine Learning For Beginners Guide Algorithms: Supervised & Unsupervsied Learning. Decision Tree & Random Forest Introduction

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 towards studying machine learning academically.  They started using probabilistic reasoning etc. Around the ‘90s the two fields, AI and machine learning, were officially separated, and now both of them are studied individually.  In the following chapters, you will learn the basics of machine learning and how it can be used in day-to-day life. You will also learn about the careers that are available in this field as well as certain advanced topics for the experts.

Chapter 2: Machine Learning Basics

What is it that has made machine learning a buzzword in today’s era? The simplest answer to the above question would be its unique, feature-rich nature that can change the future of humanity forever. In the words of Bill Gates:

"A breakthrough in machine learning would be worth ten Microsofts."

What the above statement roughly means is that scientists and computer experts all over are desperately looking for a breakthrough in machine learning and are looking for a way to make it more accessible, useful and trustworthy. However, such programs are still going, and we still haven’t found a way to devise a machine that could think.

In machine learning, computers learn to program themselves. If programming is considered to be automation and an automatic process, then machine learning is the automation of this automatic process, thus making a double automatic process.

Machine learning can make programming more scalable and can help us to produce better results in shorter durations. To prove this, let us see the following comparison:

Differences between Traditional Programming and Machine Learning

Traditional Programming:

The data is fed to the computer, and a program is run. This program then, using the supplied data, presents output.

Machine Learning:

Pre-solved data and the resulting output are fed to the computer. These two inputs are used to create a program. This program then can do the job of traditional programming.

Thus, machine learning can be explained by using the metaphors of agriculture. Algorithms are, in a way seeds while data is nutrients. You are the farmer while the program that grows out of the data is your crop.

Elements of Machine Learning

As machine learning is a complicated and convoluted field, it's hard to understand its basics. It is also an ever-growing field. Hence it is possible to see new development in the area almost every day. For instance, it is believed that every year more than a few hundred, new algorithms are developed all over the world. This brings the number of overall machine learning algorithms to a sum that is larger than ten thousand. Even though a lot of variety is seen in the algorithms of machine learning, all of them are based on three basic concepts that are as follows.

Representation:

This concept deals with the representation of knowledge. It deals with how the knowledge can be represented, what is necessary to represent the knowledge etc.  Some examples of representation include sets of rules, including decision trees, support vector machines, instances, neural networks, graphical models, model ensembles, etc. Some of these will be discussed in the book later.

Evaluation:

This is the second most important concept of algorithms. It is the way used to evaluate the hypotheses, also known as the candidate program.  Some examples are accuracy, prediction and recall, squared error, likelihood, posterior probability, cost, margin, entropy k-L divergence and others.

Optimization:

This is the third and last concept of algorithms. It is the method in which the hypothesis or the candidate program is created. It is also known as the search process. Examples include combinatorial optimization, convex optimization and constrained optimization.

Making various combinations of the above components creates all machine-learning algorithms, and thus they are the basis of machine learning.

Types and Kinds of Machine Learning

As said earlier, machine learning is complex and vast field hence it can be divided into many sections and classes. However, on a superficial level, it can be split into four parts, and they are as follows:

Supervised Learning

Unsupervised Learning

Semi-supervised Learning

Reinforcement Learning

Supervised learning:

Supervised Learning is also known as inductive learning in the technological circles.  It is considered to be the most advanced and mature of all the forms of learning. This is why it is the most studied as well as most used learning as well. It is easy to used Learning type as it is much easier to learn under supervision than without supervision.