Machine Learning For Beginners Guide Algorithms电子书

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