Machine Learning and Data Science · an Introduction to Statistical Learning Methods With R by Gutierrez, Daniel D. -- Read -- Imperial Library of Trantor

Index

Introduction

How This Book is Organized Intended Audience for This Book What you Will Need R Code and Figures Going Beyond This Book Contacting the Author

Chapter 1 Machine Learning Overview

Types of Machine Learning Use Case Examples of Machine Learning

Acquire Valued Shoppers Challenge Netflix Algorithmic Trading Challenge Heritage Health Prize Marketing Sales Supply Chain Risk Management Customer Support Human Resources Google Flu Trends

Process of Machine Learning Mathematics Behind Machine Learning Becoming a Data Scientist R Project for Statistical Computing RStudio Using R Packages Data Sets Using R in Production Summary

Chapter 2 Data Access

Managing Your Working Directory Types of Data Files Sources of Data Downloading Data Sets From the Web Reading CSV Files Reading Excel Files Using File Connections Reading JSON Files Scraping Data From Websites SQL Databases SQL Equivalents in R Reading Twitter Data Reading Data From Google Analytics Writing Data Summary

Chapter 3 Data Munging

Feature Engineering Data Pipeline Data Sampling Revise Variable Names Create New Variables Discretize Numeric Values Date Handling Binary Categorical Variables Merge Data Sets Ordering Data Sets Reshape Data Sets Data Manipulation Using Dplyr Handle Missing Data Feature Scaling Dimensionality Reduction Summary

Chapter 4 Exploratory Data Analysis

Numeric Summaries Exploratory Visualizations Histograms Boxplots Barplots Density Plots Scatterplots QQ-Plots Heatmaps Missing Value Plots Expository Plots Summary

Chapter 5 Regression

Simple Linear Regression Multiple Linear Regression Polynomial Regression Summary

Chapter 6 Classification

A Simple Example Logistic Regression Classification Trees Naïve Bayes K-Nearest Neighbors Support Vector Machines Neural Networks Ensembles Random Forests Gradient Boosting Machines Summary

Chapter 7 Evaluating Model Performance

Overfitting Bias and Variance Confounders Data Leakage Measuring Regression Performance Measuring Classification Performance Cross Validation Other Machine Learning Diagnostics

Get More Training Observations Feature Reduction Feature Addition Add Polynomial Features Fine Tuning the Regularization Parameter

Summary

Chapter 8 Unsupervised Learning

Clustering Simulating Clusters Hierarchical Clustering K-Means Clustering Principal Component Analysis Summary

Index

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