Machine Learning: Step-by-Step Guide To Implement Machine Learning Algorithms with Python

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Machine Learning
 
Step-by-Step Guide To Implement
Machine Learning Algorithms with Python
Author
Rudolph Russell

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Table of Contents
 
CHAPTER 1
INTRODUCTION TO MACHINE LEARNING
Theory
What is machine learning?
Why machine learning?
When should you use machine learning?
Types of Systems of Machine Learning
Supervised and unsupervised learning
Supervised Learning
The most important supervised algorithms
Unsupervised Learning
The most important unsupervised algorithms
Reinforcement Learning
Batch Learning
Online Learning
Instance based learning
Model-based learning
Bad and Insufficient Quantity of Training Data
Poor-Quality Data
Irrelevant Features
Feature Engineering
Testing
Overfitting the Data
Solutions
Underfitting the Data
Solutions
EXERCISES
SUMMARY
REFERENCES
CHAPTER 2

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CLASSIFICATION
Installation
The MNIST
Measures of Performance
Confusion Matrix
Recall
Recall Tradeoff
ROC
Multi-class Classification
Training a Random Forest Classifier
Error Analysis
Multi-label Classifications
Multi-output Classification
EXERCISES
REFERENCES
CHAPTER 3
HOW TO TRAIN A MODEL
Linear Regression
Computational Complexity
Gradient Descent
Batch Gradient Descent
Stochastic Gradient Descent
Mini-Batch Gradient Descent
Polynomial Regression
Learning Curves
Regularized Linear Models
Ridge Regression
Lasso Regression
EXERCISES
SUMMARY
REFERENCES
Chapter 4
Different models combinations
Implementing a simple majority classifer
Combining different algorithms for classification with majority vote
Questions

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