Oliver R. Simpson - PYTHON: 4 BOOKS IN 1: Learn How To Develop Programs And Apps In 7 Days With Python Programming And Start Deep Hands-on Learning For Beginners of Data Science And Machine Learning.

Python

This Book Includes:

Learn How To Develop Programs And

Apps In 7 Days With Python Programming And Start Deep Hands-on Learning For Beginners

of Data Science And Machine Learning.

By

Oliver R. Simpson

Code Developer Academy

Copyright 2020 - All rights reserved.

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Table of Contents

PYTHON MACHINE LEARNING

PYTHON FOR DATA ANALYSIS

PYTHON DATA SCIENCE FOR BEGINNERS

PYTHON PROGRAMMING LANGUAGE FOR BEGINNERS

Python Machine Learning

The Ultimate Basic Guide For Beginners To Learn How To Design Types Of Automatic Production With Classification Algorithms And Create A Data Pipelines With Unsupervised Learning

By

Oliver R. Simpson

Code Developer Academy

Table of Contents

Introduction

C ongratulations on downloading Python Machine Learning: The Ultimate Basic Guide for Beginners to Learn How to Design Types of Automatic Production with Classification Algorithms and Create A Data Pipelines with Unsupervised Learning and thank you for doing so.

The following chapters will discuss the fundamentals of machine learning technology to help you understand the process of creating a data pipeline for a machine learning model. You will start this book by developing a solid understanding of the basics of artificial intelligence technology and the fourth industrial revolution upon us. It is important to master the concepts of artificial intelligence technology and learn how researchers are breaking the boundaries of data science to mimic human intelligence in machines. The power of artificial intelligence has already started to manifest in our environment and our everyday objects. This chapter will also discuss how artificial intelligence technology is being applied in some of the most important industrial domains including finance and banking, journalism, travel, and the transportation industry.

The chapter 2 of this book titled Fundamentals of Machine Learning will introduce you to the basic concepts of machine learning, namely, representation, optimization and evaluation. You will learn how the definition of machine learning has been customized across the academic and business worlds. An overview of different types of machine learning algorithms along with the relationship between machine learning and Artificial Intelligence technology. A thorough understanding of the concept of Statistical Learning has been provided, which is a descriptive statistics-based machine learning framework that can be categorized as supervised or unsupervised.

In chapter 3 titled Machine Learning Algorithms, a variety of supervised and unsupervised learning algorithms are explained in exquisite detail. Select algorithms such as classification, logistic regression and a "Nave Bayes classifier" algorithms have been explained with required mathematical equations for application in real life. You will learn the basics of some supervised algorithms including "regression", Support Vector Machine, Decision trees and k-nearest neighbors as well as unsupervised algorithms including K-Means clustering. You will also learn that the "Artificial Neural Networks" or (ANN) have been developed with inspiration from the structure of the human brain and utilize numerous processing units like human neurons or nodes working as a single unit. In this chapter, you will be given a stage by stage walkthrough of how you can create a data pipeline to build your own machine learning model. The 9 stages of this process are explained in detail with a focus on "offline" and "online" mode of execution of the solution.

The final chapter of this book, titled "Python and Machine Learning Libraries" will introduce you to the Python programming language and some of the key features that render it as the language of choice for coding beginners and advanced software programmers alike. A number of Python coding tips and tricks have also been provided that will help you sharpen up your Python programming skillset or get familiar with the coding if you are new to Python coding. This chapter also includes a brief overview of various renowned machine learning libraries such as "Scikit-Learn", "NumPy", "SciPy", "Matplotlib", "IPython", "SymPy and Pandas among others. This book is filled with real-life examples to help you understand the nitty-gritty of the concepts and names and descriptions of multiple tools that you can further explore and selectively implement to make sound choices for the development of a desired machine learning model.

There are plenty of books on this subject on the market, thanks again for choosing this one! Every effort was made to ensure it is full of as much useful information as possible, please enjoy!

Chapter 1: Introduction to Artificial Intelligence

H umans or Homo Sapiens often claim to be the most superior species to have ever inhabited the planet Earth, mainly attributing to their "intelligence." Even the most complicated animal behavior is never regarded as smart, but intelligence is attributed to the simplest of human behavior. For example, when a female digger wasp returns with food to her burrow, she deposits the food on the threshold and checks for intruders prior to carrying her food inside. Sounds like wasps are quite smart, right? However, an experiment performed on these wasps, where the scientist moved the food a few inches away from the burrow's entrance while the wasp was inside the burrow, reported that the wasps continued to repeat the entire ordeal as often as the food was moved. This experiment revealed that the wasps fail to adapt to evolving conditions and therefore, "intelligence" in the wasps is noticeably absent. Then how do we define "human intelligence?" Psychologists characterize human intelligence as a composite of a variety of skills such as learning from experiences and adapting consequently, understanding abstract ideas, reasoning, problem-solving, linguistic use, and perception.

Artificial Intelligence (AI) can be defined as the science of developing human-controlled and operated machines, such as digital computers or robots, that are capable of imitating human intelligence, adapting to new inputs, and performing human-like activities. Thanks to Pop culture, upon hearing the words Artificial Intelligence, most people tend to think of robots coming to life to wreak havoc on human beings. But that is far from reality. The core principle of Artificial Intelligence is the capacity of Artificial Intelligence powered machines to rationalize (think like humans) and take action (mimic human actions) to achieve the targeted objective. To put it simply, Artificial Intelligence is designing a machine that will think and behave like human beings. Artificial Intelligence has three primary objectives, which are learning, reasoning, and perception.