finelybook
9781719528405
Machine Learning: Step-by-Step Guide To Implement Machine Learning Algorithms with Python
by 作者: Rudolph Russell
ISBN-10 书号: 1719528403
ISBN-13 书号: 9781719528405
Publisher Finelybook 出版日期: 2018-05-22
Pages: 106
8
This book is for anyone who would like to learn how to develop machine-learning systems. We will cover the most important concepts about machine learning algorithms,in both a theoretical and a practical way,and we’ll implement many machine-learning algorithms using the Scikit-learn library in the Python programming language. In the first chapter,you’ll learn the most important concepts of machine learning,and,in the next chapter,you’ll work mainly with the classification. In the last chapter you’ll learn how to train your model. I assume that you’ve knowledge of the basics of programming
This book contains illustrations and step-by-step explanations with bullet points and exercises for easy and enjoyable learning.
Benefits of reading this book that you’re not going to find anywhere else:
Introduction to Machine Learning
Classification
How to train a Model
Different Models Combinations
CHAPTER1
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
Reinforcement Learning
Batch Learning
Online Learning
Instance based learning
Model-based learning
Bad and Insufficient Quantity of Training Data
Poor-Quality Data
Irrelevant Features
Testing
Overfitting the Data
Underfitting the Data
EXERCISES
SUMMARY
REFERENCES
CHAPTER2
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
CHAPTER3
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
EXERCISES
SUMMARY
REFERENCES
Chapter4
Different models combinations
