Introduction 1 About This Book 1 Foolish Assumptions 2 Icons Used in This Book 3 Beyond the Book 3 Where to Go from Here 4 Part 1: Introducing How Machines Learn 5 Chapter 1: Getting the Real Story about AI 7 Moving beyond the Hype 8 Dreaming of Electric Sheep 9 Understanding the history of AI and machine learning 10 Exploring what machine learning can do for AI 11 Considering the goals of machine learning 12 Defining machine learning limits based on hardware 12 Overcoming AI Fantasies 13 Discovering the fad uses of AI and machine learning 14 Considering the true uses of AI and machine learning 15 Being useful; being mundane 16 Considering the Relationship between AI and Machine Learning 17 Considering AI and Machine Learning Specifications 18 Defining the Divide between Art and Engineering 19 Predicting the Next AI Winter 20 Chapter 2: Learning in the Age of Big Data 23 Considering the Machine Learning Essentials 24 Defining Big Data 25 Considering the Sources of Big Data 26 Building a new data source 26 Using existing data sources 29 Locating test data sources 29 Specifying the Role of Statistics in Machine Learning 30 Understanding the Role of Algorithms 31 Defining what algorithms do 32 Considering the five main techniques 32 Defining What Training Means 34 Chapter 3: Having a Glance at the Future 37 Creating Useful Technologies for the Future 38 Considering the role of machine learning in robots 38 Using machine learning in health care 39 Creating smart systems for various needs 40 Using machine learning in industrial settings 40 Understanding the role of updated processors and other hardware 41 Discovering the New Work Opportunities with Machine Learning 42 Working for a machine 42 Working with machines 43 Repairing machines 44 Creating new machine learning tasks 44 Devising new machine learning environments 45 Avoiding the Potential Pitfalls of Future Technologies 46 Part 2: Preparing Your Learning Tools 47 Chapter 4: Installing a Python Distribution 49 Using Anaconda for Machine Learning 50 Getting Anaconda 50 Defining why Anaconda is used in this book 51 Installing Anaconda on Linux 52 Installing Anaconda on Mac OS X 53 Installing Anaconda on Windows 54 Downloading the Datasets and Example Code 57 Using Jupyter Notebook 57 Defining the code repository 59 Understanding the datasets used in this book 64 Chapter 5: Beyond Basic Coding in Python 67 Defining the Basics You Should Know 68 Considering Python basics 68 Working with functions 72 Working with modules 76 Storing Data Using Sets, Lists, and Tuples 78 Creating sets 78 Performing operations on sets 78 Using lists 79 Creating and using tuples 82 Defining Useful Iterators 83 Working with ranges 83 Iterating multiple lists using zip 84 Working with generators using yield 84 Indexing Data Using Dictionaries 85 Creating dictionaries 85 Storing and retrieving data from dictionaries 85 Chapter 6: Working with Google Colab 87 Defining Google Colab 88 Understanding what Google Colab does 88 Considering the online coding difference 90 Using local runtime support 91 Working with Google Colab features 91 Getting a Google Account 94 Creating the account 94 Signing in 95 Working with Notebooks 96 Creating a new notebook 96 Opening existing notebooks 97 Uploading a notebook 99 Saving notebooks 100 Downloading notebooks 103 Performing Common Tasks 103 Creating code cells 104 Creating text cells 106 Creating special cells 107 Editing cells 108 Moving cells 108 Using Hardware Acceleration 108 Viewing Your Notebook 109 Displaying the table of contents 110 Getting notebook information 110 Checking code execution 110 Executing the Code 111 Sharing Your Notebook 112 Getting Help 113 Part 3: Getting Started with the Math Basics 115 Chapter 7: Demystifying the Math Behind Machine Learning 117 Working with Data 118 Learning the terminology 119 Understanding scalar and vector operations 120 Performing vector multiplication 123 Creating a matrix 123 Understanding basic operations 125 Performing matrix multiplication 126 Glancing at advanced matrix operations 128 Using vectorization effectively 129 Exploring the World of Probabilities 130 Getting an overview of probability 130 Operating on probabilities 131 Conditioning chance by Bayes'' theorem 132 Describing the Use of Statistics 135 Chapter 8: Descending the Gradient 139 Acknowledging Different Kinds of Learning 140 Supervised learning 140 Unsupervised learning 141 Reinforcement learning 141 The learning process 142 Mapping an unknown function 142 Exploring cost functions 145 Descending the optimization curve 147 Optimizing with big data 148 Leveraging sampling 149 Using parallelism 150 Learning out-of-core 151 Chapter 9: Validating Machine Learning 153 Considering the Use of Example Data 154 Checking Out-of-Sample Errors 155 Understanding the concept of samples 155 Looking for the holy grail of generalization 156 Experimenting how bias and variance work 158 Keeping model complexity in mind 160 Keeping solutions balanced 162 Depicting learning curves 163 Training, Validating, and Testing 165 Considering the split 165 Resorting to cross-validation 166 Looking for alternatives in validation 167 Optimizing by Cross-Validation 169 Sources of predictive performance 169 Exploring the hyper-parameter space 170 Selecting relevant features 171 Avoiding Sample Bias and Leakage Traps 173 Chapter 10: Starting with Simple Learners 175 Discovering the Incredible Perceptron 176 Falling short of a miracle 176 Hitting the nonseparability limit 179 Growing Greedy Classification Trees 180 Predicting outcomes by splitting data 181 Pruning overgrown trees 185 Taking a Probabilistic Turn 188 Understanding Naïve Bayes 189 Estimating response with Naïve Bayes 192 Part 4: Learning from Smart and Big Data 197 Chapter 11: Preprocessing Data 199 Gathering and Cleaning Data 200 Repairing Missing Data 201 Identifying missing data 201 Choosing the right replacement strategy 203 Transforming Distributions 205 Creating Your Own Features 207 Understanding the need to create features 207 Creating features automatically 208 Explaining the basics of SVD 210 Reorganizing data 212 Delimiting Anomalous Data 215 Using a univariate strategy .215 Resorting to Multivariate Models 217 Chapter 12: Leveraging Similarity 221 Measuring Similarity between Vectors 222 Understanding similarity 222 Computing distances for learning 223 Using Distances to Locate Clusters 224 Checking assumptions and expectations 226 Inspecting the gears of the K-means algorithm 227 Tuning the K-Means Algorithm 229 Experimenting with K-means reliability 230 Experimenting with how centroids converge 233 Finding Similarity by K-Nearest Neighbors 238 Understanding the k parameter 238 Experimenting with a flexible algorithm 240 Chapter 13: Working with Linear Models the Easy Way 243 Starting to Combine Features 244 Getting an overview of regression 244 Solving problems with a machine learning approach 247 Understanding R squared 249 Mixing Features of Different Types 251 Switching to Probabilities 255 Specifying a binary response 255 Handling multiple classes 259 Guessing the Right Features 259 Defining the outcome of features that don''t work together 259 Solving overfitting by using greedy selection 260 Addressing overfitting by regularization 262 Learning One Example at a Time 264 Using gradient descent 264 Understanding how SGD is different 265 Chapter 14: Hitting Complexity with Neural Networks 271 Revising the Perceptron 272 Pushing forth with feed-forward 274 Going even deeper down the rabbit hole 276 Pulling back with backpropagation 280 Representing the Way of Learning of a Network 283 Understanding the problem with overfitting 283 Choosing a framework 285 Getting your copy of TensorFlow and Keras 286 Opening the black box 289 Introducing Deep Learning 294 Understanding some deep learning essentials 295 Explaining the magic of convolutions 296 Understanding recurrent neural networks 300 Chapter 15: Going a Step Beyond Using Support Vector Machines 307 Revisiting the Separation Problem 308 Ex.