Machine Learning For Dummies

Table of contents

1. 1. Cover
   2. Introduction
      1. About This Book
      2. Foolish Assumptions
      3. Icons Used in This Book
      4. Beyond the Book
      5. Where to Go from Here
   3. Part 1: Introducing How Machines Learn
      1. Chapter 1: Getting the Real Story about AI
         1. Moving beyond the Hype
         2. Dreaming of Electric Sheep
         3. Overcoming AI Fantasies
         4. Considering the Relationship between AI and Machine Learning
         5. Considering AI and Machine Learning Specifications
         6. Defining the Divide between Art and Engineering
      2. Chapter 2: Learning in the Age of Big Data
         1. Defining Big Data
         2. Considering the Sources of Big Data
         3. Specifying the Role of Statistics in Machine Learning
         4. Understanding the Role of Algorithms
         5. Defining What Training Means
      3. Chapter 3: Having a Glance at the Future
         1. Creating Useful Technologies for the Future
         2. Discovering the New Work Opportunities with Machine Learning
         3. Avoiding the Potential Pitfalls of Future Technologies
   4. Part 2: Preparing Your Learning Tools
      1. Chapter 4: Installing an R Distribution
         1. Choosing an R Distribution with Machine Learning in Mind
         2. Installing R on Windows
         3. Installing R on Linux
         4. Installing R on Mac OS X
         5. Downloading the Datasets and Example Code
      2. Chapter 5: Coding in R Using RStudio
         1. Understanding the Basic Data Types
         2. Working with Vectors
         3. Organizing Data Using Lists
         4. Working with Matrices
         5. Interacting with Multiple Dimensions Using Arrays
         6. Creating a Data Frame
         7. Performing Basic Statistical Tasks
      3. Chapter 6: Installing a Python Distribution
         1. Choosing a Python Distribution with Machine Learning in Mind
         2. Installing Python on Linux
         3. Installing Python on Mac OS X
         4. Installing Python on Windows
         5. Downloading the Datasets and Example Code
      4. Chapter 7: Coding in Python Using Anaconda
         1. Working with Numbers and Logic
         2. Creating and Using Strings
         3. Interacting with Dates
         4. Creating and Using Functions
         5. Using Conditional and Loop Statements
         6. Storing Data Using Sets, Lists, and Tuples
         7. Defining Useful Iterators
         8. Indexing Data Using Dictionaries
         9. Storing Code in Modules
      5. Chapter 8: Exploring Other Machine Learning Tools
         1. Meeting the Precursors SAS, Stata, and SPSS
         2. Learning in Academia with Weka
         3. Accessing Complex Algorithms Easily Using LIBSVM
         4. Running As Fast As Light with Vowpal Wabbit
         5. Visualizing with Knime and RapidMiner
         6. Dealing with Massive Data by Using Spark
   5. Part 3: Getting Started with the Math Basics
      1. Chapter 9: Demystifying the Math Behind Machine Learning
         1. Working with Data
         2. Exploring the World of Probabilities
         3. Describing the Use of Statistics
      2. Chapter 10: Descending the Right Curve
         1. Interpreting Learning As Optimization
         2. Exploring Cost Functions
         3. Descending the Error Curve
         4. Updating by Mini-Batch and Online
      3. Chapter 11: Validating Machine Learning
         1. Checking Out-of-Sample Errors
         2. Getting to Know the Limits of Bias
         3. Keeping Model Complexity in Mind
         4. Keeping Solutions Balanced
         5. Training, Validating, and Testing
         6. Resorting to Cross-Validation
         7. Looking for Alternatives in Validation
         8. Optimizing Cross-Validation Choices
         9. Avoiding Sample Bias and Leakage Traps
      4. Chapter 12: Starting with Simple Learners
         1. Discovering the Incredible Perceptron
         2. Growing Greedy Classification Trees
         3. Taking a Probabilistic Turn
   6. Part 4: Learning from Smart and Big Data
      1. Chapter 13: Preprocessing Data
         1. Gathering and Cleaning Data
         2. Repairing Missing Data
         3. Transforming Distributions
         4. Creating Your Own Features
         5. Compressing Data
         6. Delimiting Anomalous Data
      2. Chapter 14: Leveraging Similarity
         1. Measuring Similarity between Vectors
         2. Using Distances to Locate Clusters
         3. Tuning the K-Means Algorithm
         4. Searching for Classification by K-Nearest Neighbors
         5. Leveraging the Correct K Parameter
      3. Chapter 15: Working with Linear Models the Easy Way
         1. Starting to Combine Variables
         2. Mixing Variables of Different Types
         3. Switching to Probabilities
         4. Guessing the Right Features
         5. Learning One Example at a Time
      4. Chapter 16: Hitting Complexity with Neural Networks
         1. Learning and Imitating from Nature
         2. Struggling with Overfitting
         3. Introducing Deep Learning
      5. Chapter 17: Going a Step beyond Using Support Vector Machines
         1. Revisiting the Separation Problem: A New Approach
         2. Explaining the Algorithm
         3. Applying Nonlinearity
         4. Illustrating Hyper-Parameters
         5. Classifying and Estimating with SVM
      6. Chapter 18: Resorting to Ensembles of Learners
         1. Leveraging Decision Trees
         2. Working with Almost Random Guesses
         3. Boosting Smart Predictors
         4. Averaging Different Predictors
   7. Part 5: Applying Learning to Real Problems
      1. Chapter 19: Classifying Images
         1. Working with a Set of Images
         2. Extracting Visual Features
         3. Recognizing Faces Using Eigenfaces
         4. Classifying Images
      2. Chapter 20: Scoring Opinions and Sentiments
         1. Introducing Natural Language Processing
         2. Understanding How Machines Read
         3. Using Scoring and Classification
      3. Chapter 21: Recommending Products and Movies
         1. Realizing the Revolution
         2. Downloading Rating Data
         3. Leveraging SVD
   8. Part 6: The Part of Tens
      1. Chapter 22: Ten Machine Learning Packages to Master
         1. Cloudera Oryx
         2. CUDA-Convnet
         3. ConvNetJS
         4. e1071
         5. gbm
         6. Gensim
         7. glmnet
         8. randomForest
         9. SciPy
         10. XGBoost
      2. Chapter 23: Ten Ways to Improve Your Machine Learning Models
         1. Studying Learning Curves
         2. Using Cross-Validation Correctly
         3. Choosing the Right Error or Score Metric
         4. Searching for the Best Hyper-Parameters
         5. Testing Multiple Models
         6. Averaging Models
         7. Stacking Models
         8. Applying Feature Engineering
         9. Selecting Features and Examples
         10. Looking for More Data
   9. About the Author
   10. Advertisement Page
   11. Connect with Dummies
   12. End User License Agreement