Markov Models Supervised and Unsupervised Machine Learning: Mastering Data Science And Python

By William Sullivan

Markov Models Supervised and Unsupervised Machine Learning: Mastering Data Science & Python

o you want to MASTER Data science?

Understand Markov Models and learn the real world application to accurately predict future events.

Extend your knowledge of machine learning, python programming & algorithms.

What you'll Learn

·  Mathematics Behind Markov Algorithms

·  3 Main Problems Of Markov Models And How To Overcome Them

·  Uses And Applications For Machine Learning

·  Python Programming

·  Speech Recognition

·  Weather Reporting

·  The Markov Rule And Markov's Model

·  Fundamental Axioms Of Statistics And Probability

·  Solutions

·  Theories

·  Artificial Intelligence

·  Bayesian Inference

·  Important Tools Used With HMM

·  And Much, Much, More!

The objective of this book is to teach you the essentials at the most fundamental level. You will learn the ins and outs of machine learning, and its real world applications. Also, specifically you will discover practical implementations of Markov Models in python programming.

This book offers high value and is the greatest investment in your knowledge base you can make that will benefit you in the long run. Why not take this opportunity to take advantage now and get ahead of everyone else?

Other books can easily retail for $100s- $1000s of dollars!

Get equipped with the knowledge you need to advance yourself today at an affordable price.

What are you waiting for?

Don't miss out on this opportunity!

Grab Your Copy Now!

• Language: English
• Publisher: Publishdrive
• Release date: Apr 29, 2019

William Sullivan

William Sullivan has over 25 + years experience in the field of software/programming. He was born in 1978 in Seattle, Washington. He's worked for many leading USA and international based companies where he's brought on board his talents, highly desirable skill sets, creativity and innovation. From humble beginnings William Sullivan worked his way up the corporate ladder to becoming an influential programmer. He was an only child and had a single parent mom, who always encouraged him to pursue higher education and a better life. They lived pay cheque to pay cheque, she worked over time and erratic shifts. His mother always made sure he had the necessities of life such as food, clothing , and shelter. William was always fascinated with technology building computers from scratch, programming, etc. His mother did everything she could to satisfy his insatiable curiosity by buying him books on software, programming, hardware and almost anything that related to computer technology. He states reading in his leisure time with the resources provided from his mother's very limited income was really the foundational corner stone that brought him the success he has today. He majored in computer science and was granted a full academic scholarship and graduated with honors. He has now since then moved to California and is married with three children. He works various high paying jobs on contract basis, and writes in his free time. He loves to travel, taste different cuisines and experience different cultures. He's gracious for the life changing opportunities he's received and wants to give back through writing books that are affordable for anybody interested in becoming more tech-savvy.

Book preview

Conclusion

© Copyright 2017 by Healthy Pragmatic Solutions Inc.  - All rights reserved.

The contents of this book may not be reproduced, duplicated or transmitted without direct written permission from the author.

Under no circumstances will any legal responsibility or blame be held against the publisher for any reparation, damages, or monetary loss due to the information herein, either directly or indirectly.

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You cannot amend, distribute, sell, use, quote or paraphrase any part or the content within this book without the consent of the author.

Disclaimer Notice:

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.

By reading this document, the reader agrees that under no circumstances are 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

MARKOV MODELS

CHAPTER 1: MARKOV MODELS

Axioms to understand Markov Models

The Markov Property

Markov Models and Observation Probabilities

Hidden Markov Model

CHAPTER 2: DATA MINING

Software

Strategies

MACHINE LEARNING

CHAPTER 3: WHAT IS MACHINE LEARNING?

Subjects involved in machine learning

Varieties of Machine Learning

Uses of Machine Learning

Applications of Machine Learning in the Real World

CHAPTER 4: SUPERVISED MACHINE LEARNING

Overview

Issues to consider in Supervised Learning:

CHAPTER 5: UNSUPERVISED MACHINE LEARNING

ARTIFICIAL INTELLIGENCE AND DATA SCIENCE

CHAPTER 6: ARTIFICIAL INTELLIGENCE 101

Increased Computational Resources

Growth of Data

Deeper Focus

Knowledge Engineering

Alternative Reasoning Models

Exploring AI

CHAPTER 7: BIG DATA AND ARTIFICIAL INTELLIGENCE

What is Big Data?

Assessing Data using AI

CHAPTER 8: HOW DOES AI DRAW CONCLUSIONS FROM DATA?

Deductive Reasoning

Inductive Reasoning

Abductive Reasoning

Case Based Reasoning

Common Sense Reasoning

CHAPTER 9: WHAT IS DATA SCIENCE?

PYTHON

CHAPTER 10: MARKOV’S MODEL IN PYTHON

Getting Started

Programming a Hidden Markov Model

CONCLUSION

Introduction

Thank you for purchasing the book, ‘Markov Models Supervised and Unsupervised Machine Learning: Mastering Data Science & Python.’

In the following book, you will find a great deal of information on the mechanics of the Markov Model and how the model could be used in the world of machine learning though using the powerful yet simple language, Python. This book is for those who have just begun to study and understand Artificial Intelligence and Data Science.

Markov Models was discovered in the year 1916, by Andreevich Markov, a scientist who was studying and analyzing the frequency of different types of words in Pushkin’s poems. Since then, the model created has been expanded to include representations of a number of probabilities like the Hidden Markov Model that is integral to our understanding and towards building artificial intelligence.

The structure of this book will define each section of the topic piece by piece. We will first take a look at Markov Models and the mathematical aspect of those models. We will then take a look at Hidden Markov Models (HMM) and also study the three problems of HMM and also identify the solutions to the same. After thoroughly investigating the Markov Models, we will look at different aspects of Machine Learning and look at Supervised and Unsupervised Machine learning in greater detail.

After understanding the concepts of Markov Models and Machine Learning, we will take a look at Artificial Intelligence and Data Science and understand certain aspects of them. We will then learn about how to apply these concepts in Python. Everything from installing Python to programming a Markov Model in Python will be covered in detail.

At the end of the book, you will have a clear understanding of the mathematical aspects of Markov Models and Hidden Markov Models. You will also be able to build enough confidence to help you program Markov Models using Python.

Thank you for purchasing the book. I hope you find it helpful.

Markov Models

Chapter 1: Markov Models

Axioms to understand Markov Models

There are certain axioms that exist in probability and statistics that will need to be understood before looking at Markov Models.

Fundamental Axioms

Let us take a look at the various axioms that are used in Markov Models to understand the math that supports the model. One of the most fundamental axioms can be expressed as follows: 0

This states that the probability of any event occurring is always going to be greater than zero but less than one, both inclusive. This implies that the probability of the occurrence of any event can never be negative.