Search Ebook here:


Machine Learning: 2 Books in 1: An Introduction Math Guide for Beginners



Machine Learning: 2 Books in 1: An Introduction Math Guide for Beginners PDF

Author: Samuel Hack

Publisher: Independently published

Genres:

Publish Date: May 11, 2020

ISBN-10: B088LD4PV4

Pages: 219

File Type: PDF

Language: English

read download

Book Preface

Congratulations on purchasing Machine Learning for Beginners and thank you for doing so.
There are many opportunities opening up in the field of machine learning. It’s being adopted as a tool by almost every major industry. Whether you are interested in health care, business and finance, agriculture, clean energy, and many others, there is someone utilizing the power of machine learning to make their job easier.

Unfortunately for these industries, but fortunate for you is that there is a major shortage of talent in the field of data science and artificial intelligence. While entry-level data science jobs remain competitive, there is a major shortage of experienced data professionals who can fill the high-level roles. It’s a newer field in computer science, with a younger group of individuals who make up for much of the field.

It can be very financially rewarding if you manage to land a job in data science. In 2016 the average data scientist made about $111,000, with predicted growth over the next five years. About half of data scientists working in the field have a Ph.D. It’s not a requirement, but it’s something to consider if you are looking into starting a real career as a data scientist.

If you are looking to add machine learning to your wheelhouse, so that you can have a better understanding of it and implement it in your own business or projects down the road, then a Ph.D. may not be necessary. But for those looking to enter the field, higher education is recommended as it will help you stand out amongst the field.

Indeed.com called machine learning the best career in 2019, and it’s easy to see why. With a huge demand for talented data scientists and a lucrative payout, it’s worth a look. And big data doesn’t seem to be going away anytime soon with an increase in connectivity and higher than ever internet usage by both consumers and companies alike. Data is a part of our modern world, and as the complexity and size of data increases, it will take even more specialized knowledge and skills to be able to complete the task at hand.

To supplement the knowledge in this book, I highly recommend seeking further knowledge in statistics and programming. A good base of statistical knowledge is required to perform any work in machine learning because statistical mathematics provides the structure and justification for all the models and algorithms that data scientists use for machine learning.

The Purpose of This Book

This book is not meant to be a comprehensive textbook on machine learning. Instead, it will give you a base of knowledge to continue with your study of machine learning and artificial intelligence. In order to continue your studies and master the subject, there is a large degree of studying that must be done. Will discuss the general structure and organization of machine learning models, the common terms, and the basic statistical concepts necessary to use and understand machine learning.
It’s necessary to have a solid understanding of statistics and quantitative analysis to be a data scientist. After all, artificial intelligence and machine learning are rooted in statistics. This provides the anchor and foundation for the kind of mathematics needed.
While coding is not required to understand this book, it is a major component of machine learning. In order to handle large volumes of data, data scientists need to have a working knowledge of computer programming to ‘tell’ the data what they want it to do. This book will not offer much in the way of coding information, but it will present resources and avenues to get you started in studying coding on your own. By the end of the book, I will at least assist you in setting up Python with the necessary libraries and toolkits to help you start learning to code.

versatile language that is relatively easy to learn and freely available. There are Python packages designed for data analysis to make your coding go faster. C++ is also quite common but more difficult to master. A third option is R, which is quite popular because it is free and open source. Students often use it because of its availability and simplicity. The drawback of using R is that it can’t handle massive datasets often used in machine learning and artificial intelligence, which is somewhat limiting.
Computers in machine learning distinguish themselves by being able to not only memorize new information but apply that information to new situations in the future. There is a difference between memorizing and learning. There is an important distinction between giving a computer a line of code and creating a machine learning model.
The basic characteristic of machine learning is the use of artificial inductive reasoning. Artificial inductive reasoning means that a specific event gives you cause to generalize a characteristic. This apple is green; therefore, all apples must be green. But here you can see why inductive reasoning on its own is not always perfect, and why it’s difficult to train computers to have the same thought process. One given piece of data is not necessarily representative of thousands of other possible pieces of data. Therefore, when we are using statistics and machine learning, we must be using enough data to be able to reason with confidence, without making the wrong inference based on data that is misinterpreted and becomes misleading.
There are things we do every day as humans that we think of as ‘common sense.’ These types of intuitive decisions cannot be explicitly programmed in a computer, because the variables that help us make our decisions are too difficult to measure. We probably don’t need to see a thousand different combinations of chess pieces on a chessboard to think ahead and plan when we are given a situation we haven’t seen before. We, as humans, require much fewer data to be able to infer and learn.
This is where machine learning comes in. These situations where the variables are complex, and directions can’t be explicitly stated have to be learned instead. Back to the example of a computer that can play checkers. It would take way too long to teach someone to play checkers by giving them every possible move and every possible countermove. Instead, you teach someone the basics, and by playing the person learns what helps them win and what doesn’t.

In the same way, it would be impossible to tell your computer every possible situation in a checkers game, and then tell the computer what it needs to do in each situation. There are far too many possibilities. Instead, you must give the computer enough data so that even when it is met with a new situation, it can respond accordingly.
Another example we will talk about later in this book uses artificial neural networks to sort whether a photo is a picture of a cat or a dog. As humans, this type of classification would be too easy. We know what a dog looks like because we have seen dogs, and we know what a cat looks like because we have seen cats.
But there is no way to explicitly tell a computer how it can tell the difference between a cat and a dog. Instead, you give the computer a set of training data with pictures of cats and dogs, and you tell the computer which pictures are of cats, and which pictures are of dogs. Eventually, the model should be able to tell you whether new, unseen pictures have a feline or a canine in them.
The problem with explicitly programmed instructions is their inability to change. If I tell a computer exactly what to do, using a programming language with explicit instructions, then the program will do a very good job of performing that task, but only that task. It won’t change when it gets new information, and it won’t alter its method if it performs incorrectly
Over time, a machine learning model will be able to change itself as the data changes so that it continues to adapt and remain accurate in a changing environment without guidance. This offers a huge advantage because it makes our models more adaptable to change, which is constantly around us. Without machine learning and artificial intelligence, our computers wouldn’t have a way to keep up.


Download EbookRead NowFile TypeUpload Date
downloadreadPDFJanuary 23, 2022

Do you like this book? Please share with your friends, let's read it !! :)

How to Read and Open File Type for PC ?