There's no better time to learn machine learning using Python. However, machine learning isn't for the faint of heart—it needs a good foundation in statistics and coding knowledge. This book helps coders of all levels master one of the most in-demand programming skillsets.

Python Machine Learning

Preface

Welcome to Python Machine Learning ! The fact that you are reading this book is a clear indication of your interest in this very interesting and exciting topic.

Introduction to Machine Learning

Extending Python Using NumPy

Manipulating Tabular Data Using Pandas

Data Visualization Using matplotlib

Getting Started with Scikit‐learn for Machine Learning

Supervised Learning&mdash;Linear Regression

Supervised Learning&mdash;Classification Using Logistic Regression

Supervised Learning&mdash;Classification Using Support Vector Machines

Supervised Learning&mdash;Classification Using K‐Nearest Neighbors (KNN)

Unsupervised Learning&mdash;Clustering Using K‐Means

What Is Microsoft Azure Machine Learning Studio?

Using Azure Machine Learning Studio

Deploying Machine Learning Models

A accuracy, computing of, 168–171 algorithms categories of in ML, 5 comparing ML algorithms, 258–260 evaluating ML algorithms, 260–261, 277–279 supervised learning algorithms, 5 Two‐Class Decision Jungle algorithm, 258, 259, 260 Two‐Class Logistic Regression algorithm, 258, 259, 260 Two‐Class Support Vector Machine algorithm, 258 unsupervised learning algorithms, 5, 7 Anaconda, 88–1 apply() function, 57, 58, 59 area under the curve (AUC), 174 argsort() function, 33 arrange() function, 20 array assignment, 34–38 array indexing, 22–26 array math, 27–34 arrays copying by reference, 34–35 copying by value (deep copy), 37 copying by view (shallow copy), 36–37 creating NumPy arrays, 20–21 reshaping of, 26–27 slicing of, 23–25 asmatrix() function, 30 auc() function, 174 Auto MPG Data Set, 98 Azure Machine Learning Studio (MAML) comparing against other algorithms, 258–260 creating experiment, 248–252 evaluating machine learning algorithms, 260–261 example using Titanic experiment, 244–246 filtering data and making fields categorical, 252–254 introduction, 243 programmatically accessing web service, 263–266 publishing experiment, 261–263 publishing learning model as web service, 261–262 removing missing data, 254 splitting data for training and testing, 254–256 testing web service, 263 training a model, 256–258 uploading dataset, 247–248 use of, 246–266 

Index

Python® Machine Learning

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