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Machine Learning For Dummies

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Your no-nonsense guide to making sense of machine learning

Machine learning can be a mind-boggling concept for the masses, but those who are in the trenches of computer programming know just how invaluable it is. Without machine learning, fraud detection, web search results, real-time ads on web pages, credit scoring, automation, and email spam filtering wouldn't be possible, and this is only showcasing just a few of its capabilities. Written by two data science experts, Machine Learning For Dummies offers a much-needed entry point for anyone looking to use machine learning to accomplish practical tasks.

Covering the entry-level topics needed to get you familiar with the basic concepts of machine learning, this guide quickly helps you make sense of the programming languages and tools you need to turn machine learning-based tasks into a reality. Whether you're maddened by the math behind machine learning, apprehensive about AI, perplexed by preprocessing data—or anything in between—this guide makes it easier to understand and implement machine learning seamlessly.

Grasp how day-to-day activities are powered by machine learning Learn to 'speak' certain languages, such as Python and R, to teach machines to perform pattern-oriented tasks and data analysis Learn to code in R using R Studio Find out how to code in Python using Anaconda

Dive into this complete beginner's guide so you are armed with all you need to know about machine learning!

Author(s): John Paul Mueller; Luca Massaron
Series: For Dummies
Edition: 1
Publisher: Wiley

Language: English
Tags: AI; artificial intelligence; machine learning; ML; cybernetics

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