Machine Learning For Beginners Guide Algorithms

图书信息

作者	William Sullivan
出版社	PublishDrive
ISBN	9781975632328
出版时间	2017-08-19
字数	16.2万
分类	进口书,外文原版书,电脑,网络

读书简介

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!

目录

About The Series

Introduction

Chapter 1: About Machine Learning

What is Machine Learning?

History:

Chapter 2: Machine Learning Basics

Differences between Traditional Programming and Machine Learning

Traditional Programming:

Machine Learning:

Elements of Machine Learning

Representation:

Evaluation:

Optimization:

Types and Kinds of Machine Learning

Supervised learning:

Unsupervised learning:

Semi-supervised learning:

Reinforcement learning:

Machine Learning in Practice

Start Loop

Data integration, selection, cleaning and pre-processing

Learning models

Interpreting results

Consolidating and deploying discovered knowledge.

End Loop

Sample applications of machine learning

Chapter 3: Machine Learning: Algorithms

Ensemble Learning Method

Supervised Learning

Unsupervised Learning

Semi-Supervised Learning

Algorithms Grouped By Similarity

Regression Algorithms

Instance-based Algorithms

Regularization Algorithms

Decision Tree Algorithms

Bayesian Algorithms

Clustering Algorithms

Association Rule Learning Algorithms

Artificial Neural Network Algorithms

Deep Learning Algorithms

Dimensional Reduction Algorithms

Ensemble Algorithms

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

Categorical Variable Decision Tree:

Continuous Variable Decision Tree:

Terminology and Jargon related to Decision Trees

Advantages

Easy to understand:

Useful in Data exploration:

Less data cleaning required:

The data type is not a constraint:

Non-Parametric Method:

Disadvantages

Over fitting:

Not fit for continuous variables:

Regression Trees vs. Classification Trees

Where does the tree get split?

Gini Index

Decision Tree, Algorithm, Gini IndexSplit on Gender:

Similar for Split on Class:

Chi-Square

Information Gain, Decision Tree

How to calculate entropy for a split:

Reduction in Variance

How to calculate Variance:

Maximum Depth of Tree:

Chapter 5: Decision Trees: Part 2

Tree Pruning

Linear models or tree based models?

Ensemble methods:

What is bagging? How does it work?

Create Multiple Data sets:

Build Multiple Classifiers:

Combine Classifiers:

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?

Regularization:

Parallel Processing:

High Flexibility

Tree Pruning:

Built-in Cross-Validation

Continuing the Existing Model

How to work with GBM in R and Python?

Start the outcome.

learning_rate

n_estimators

Subsample

Loss

Init

random state

Verbose

warm_start

Presort

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

Summary:

Conclusion

BONUS - DATA ANALYTICS INTRODUCITON

Table of Contents

Introduction

Description

Chapter 1: Regression Analysis

Chapter 2: Big Data

Chapter 3: Data and Text Mining

Chapter 4: Data Management

Chapter 5: Data Reduction and Clustering

Chapter 6: Web Scraping

Chapter 7: Data Analysis in the Real World

Chapter 8: Social Network Analysis

Chapter 9: Data Analysis Techniques

BONUS - Business Intelligence

Conclusion

Markov Models

Axioms to understand Markov Models

Fundamental Axioms

Additive Property