Python Machine Learning

Python makes machine learning easy for beginners and experienced developers

With computing power increasing exponentially and costs decreasing at the same time, there is no better time to learn machine learning using Python. Machine learning tasks that once required enormous processing power are now possible on desktop machines. However, machine learning is not for the faint of heart—it requires a good foundation in statistics, as well as programming knowledge. Python Machine Learning will help coders of all levels master one of the most in-demand programming skillsets in use today. 

Readers will get started by following fundamental topics such as an introduction to Machine Learning and Data Science. For each learning algorithm, readers will use a real-life scenario to show how Python is used to solve the problem at hand.

•          Python data science—manipulating data and data visualization

•          Data cleansing

•          Understanding Machine learning algorithms
•          Supervised learning algorithms

•          Unsupervised learning algorithms

•          Deploying machine learning models 

Python Machine Learning is essential reading for students, developers, or anyone with a keen interest in taking their coding skills to the next level. 

Introduction xxiii

Chapter 1 Introduction to Machine Learning 1

What Is Machine Learning? 2

What Problems Will Machine Learning Be Solving in This Book? 3

Classification 4

Regression 4

Clustering 5

Types of Machine Learning Algorithms 5

Supervised Learning 5

Unsupervised Learning 7

Getting the Tools 8

Obtaining Anaconda 8

Installing Anaconda 9

Running Jupyter Notebook for Mac 9

Running Jupyter Notebook for Windows 10

Creating a New Notebook 11

Naming the Notebook 12

Adding and Removing Cells 13

Running a Cell 14

Restarting the Kernel 16

Exporting Your Notebook 16

Getting Help 17

Chapter 2 Extending Python Using NumPy 19

What Is NumPy? 19

Creating NumPy Arrays 20

Array Indexing 22

Boolean Indexing 22

Slicing Arrays 23

NumPy Slice Is a Reference 25

Reshaping Arrays 26

Array Math 27

Dot Product 29

Matrix 30

Cumulative Sum 31

NumPy Sorting 32

Array Assignment 34

Copying by Reference 34

Copying by View (Shallow Copy) 36

Copying by Value (Deep Copy) 37

Chapter 3 Manipulating Tabular Data Using Pandas 39

What Is Pandas? 39

Pandas Series 40

Creating a Series Using a Specified Index 41

Accessing Elements in a Series 41

Specifying a Datetime Range as the Index of a Series 42

Date Ranges 43

Pandas DataFrame 45

Creating a DataFrame 45

Specifying the Index in a DataFrame 46

Generating Descriptive Statistics on the DataFrame 47

Extracting from DataFrames 49

Selecting the First and Last Five Rows 49

Selecting a Specific Column in a DataFrame 50

Slicing Based on Row Number 50

Slicing Based on Row and Column Numbers 51

Slicing Based on Labels 52

Selecting a Single Cell in a DataFrame 54

Selecting Based on Cell Value 54

Transforming DataFrames 54

Checking to See If a Result Is a DataFrame or Series 55

Sorting Data in a DataFrame 55

Sorting by Index 55

Sorting by Value 56

Applying Functions to a DataFrame 57

Adding and Removing Rows and Columns in a DataFrame 60

Adding a Column 61

Removing Rows 61

Removing Columns 62

Generating a Crosstab 63

Chapter 4 Data Visualization Using matplotlib 67

What Is matplotlib? 67

Plotting Line Charts 68

Adding Title and Labels 69

Styling 69

Plotting Multiple Lines in the Same Chart 71

Adding a Legend 72

Plotting Bar Charts 73

Adding Another Bar to the Chart 74

Changing the Tick Marks 75

Plotting Pie Charts 77

Exploding the Slices 78

Displaying Custom Colors 79

Rotating the Pie Chart 80

Displaying a Legend 81

Saving the Chart 82

Plotting Scatter Plots 83

Combining Plots 83

Subplots 84

Plotting Using Seaborn 85

Displaying Categorical Plots 86

Displaying Lmplots 88

Displaying Swarmplots 90

Chapter 5 Getting Started with Scikit-learn for Machine Learning 93

Introduction to Scikit-learn 93

Getting Datasets 94

Using the Scikit-learn Dataset 94

Using the Kaggle Dataset 97

Using the UCI (University of California, Irvine) Machine Learning Repository 97

Generating Your Own Dataset 98

Linearly Distributed Dataset 98

Clustered Dataset 98

Clustered Dataset Distributed in Circular Fashion 100

Getting Started with Scikit-learn 100

Using the LinearRegression Class for Fitting the Model 101

Making Predictions 102

Plotting the Linear Regression Line 102

Getting the Gradient and Intercept of the Linear Regression Line 103

Examining the Performance of the Model by Calculating the Residual Sum of Squares 104

Evaluating the Model Using a Test Dataset 105

Persisting the Model 106

Data Cleansing 107

Cleaning Rows with NaNs 108

Replacing NaN with the Mean of the Column 109

Removing Rows 109

Removing Duplicate Rows 110

Normalizing Columns 112

Removing Outliers 113

Tukey Fences 113

Z-Score 116

Chapter 6 Supervised Learning—Linear Regression 119

Types of Linear Regression 119

Linear Regression 120

Using the Boston Dataset 120

Data Cleansing 125

Feature Selection 126

Multiple Regression 128

Training the Model 131

Getting the Intercept and Coefficients 133

Plotting the 3D Hyperplane 133

Polynomial Regression 135

Formula for Polynomial Regression 138

Polynomial Regression in Scikit-learn 138

Understanding Bias and Variance 141

Using Polynomial Multiple Regression on the Boston Dataset 144

Plotting the 3D Hyperplane 146

Chapter 7 Supervised Learning—Classification Using Logistic Regression 151

What Is Logistic Regression? 151

Understanding Odds 153

Logit Function 153

Sigmoid Curve 154

Using the Breast Cancer Wisconsin (Diagnostic) Data Set 156

Examining the Relationship Between Features 156

Plotting the Features in 2D 157

Plotting in 3D 158

Training Using One Feature 161

Finding the Intercept and Coefficient 162

Plotting the Sigmoid Curve 162

Making Predictions 163

Training the Model Using All Features 164

Testing the Model 166

Getting the Confusion Matrix 166

Computing Accuracy, Recall, Precision, and Other Metrics 168

Receiver Operating Characteristic (ROC) Curve 171

Plotting the ROC and Finding the Area Under the Curve (AUC) 174

Chapter 8 Supervised Learning—Classification Using Support Vector Machines 177

What Is a Support Vector Machine? 177

Maximum Separability 178

Support Vectors 179

Formula for the Hyperplane 180

Using Scikit-learn for SVM 181

Plotting the Hyperplane and the Margins 184

Making Predictions 185

Kernel Trick 186

Adding a Third Dimension 187

Plotting the 3D Hyperplane 189

Types of Kernels 191

C 194

Radial Basis Function (RBF) Kernel 196

Gamma 197

Polynomial Kernel 199

Using SVM for Real-Life Problems 200

Chapter 9 Supervised Learning—Classification Using K-Nearest Neighbors (KNN) 205

What Is K-Nearest Neighbors? 205

Implementing KNN in Python 206

Plotting the Points 206

Calculating the Distance Between the Points 207

Implementing KNN 208

Making Predictions 209

Visualizing Different Values of K 209

Using Scikit-Learn’s KNeighborsClassifier Class for KNN 211

Exploring Different Values of K 213

Cross-Validation 216

Parameter-Tuning K 217

Finding the Optimal K 218

Chapter 10 Unsupervised Learning—Clustering Using K-Means 221

What Is Unsupervised Learning? 221

Unsupervised Learning Using K-Means 222

How Clustering in K-Means Works 222

Implementing K-Means in Python 225

Using K-Means in Scikit-learn 230

Evaluating Cluster Size Using the Silhouette Coefficient 232

Calculating the Silhouette Coefficient 233

Finding the Optimal K 234

Using K-Means to Solve Real-Life Problems 236

Importing the Data 237

Cleaning the Data 237

Plotting the Scatter Plot 238

Clustering Using K-Means 239

Finding the Optimal Size Classes 240

Chapter 11 Using Azure Machine Learning Studio 243

What Is Microsoft Azure Machine Learning Studio? 243

An Example Using the Titanic Experiment 244

Using Microsoft Azure Machine Learning Studio 246

Uploading Your Dataset 247

Creating an Experiment 248

Filtering the Data and Making Fields Categorical 252

Removing the Missing Data 254

Splitting the Data for Training and Testing 254

Training a Model 256

Comparing Against Other Algorithms 258

Evaluating Machine Learning Algorithms 260

Publishing the Learning Model as a Web Service 261

Publishing the Experiment 261

Testing the Web Service 263

Programmatically Accessing the Web Service 263

Chapter 12 Deploying Machine Learning Models 269

Deploying ML 269

Case Study 270

Loading the Data 271

Cleaning the Data 271

Examining the Correlation Between the Features 273

Plotting the Correlation Between Features 274

Evaluating the Algorithms 277

Logistic Regression 277

K-Nearest Neighbors 277

Support Vector Machines 278

Selecting the Best Performing Algorithm 279

Training and Saving the Model 279

Deploying the Model 280

Testing the Model 282

Creating the Client Application to Use the Model 283

Index 285

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