Python Machine Learning

Wei-Meng Lee is a technologist and founder of Developer Learning Solutions (http://www.learn2develop.net), a technology company specializing in hands-on training on the latest mobile technologies. Wei-Meng has many years of training experiences and his training courses place special emphasis on the learning-by-doing approach. His hands-on approach to learning programming makes understanding the subject much easier than reading books, tutorials, and documentations. His name regularly appears in online and print publications such as DevX.com, MobiForge.com, and CoDe Magazine.

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 LearningLinear Regression 119 Types of Linear Regression 119 Linear Regression 120 Using the Boston Dataset 120 Data Cleansing 125 Feature Selection 126 Multiple Regression 128 T