Machine Learning for Dummies

Title Page
Copyright 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
			Understanding the history of AI and machine learning
			Exploring what machine learning can do for AI
			Considering the goals of machine learning
			Defining machine learning limits based on hardware
		Overcoming AI Fantasies
			Discovering the fad uses of AI and machine learning
			Considering the true uses of AI and machine learning
			Being useful; being mundane
		Considering the Relationship between AI and Machine Learning
		Considering AI and Machine Learning Specifications
		Defining the Divide between Art and Engineering
		Predicting the Next AI Winter
	Chapter 2 Learning in the Age of Big Data
		Considering the Machine Learning Essentials
		Defining Big Data
		Considering the Sources of Big Data
			Building a new data source
			Using existing data sources
			Locating test data sources
		Specifying the Role of Statistics in Machine Learning
		Understanding the Role of Algorithms
			Defining what algorithms do
			Considering the five main techniques
		Defining What Training Means
	Chapter 3 Having a Glance at the Future
		Creating Useful Technologies for the Future
			Considering the role of machine learning in robots
			Using machine learning in health care
			Creating smart systems for various needs
			Using machine learning in industrial settings
			Understanding the role of updated processors and other hardware
		Discovering the New Work Opportunities with Machine Learning
			Working for a machine
			Working with machines
			Repairing machines
			Creating new machine learning tasks
			Devising new machine learning environments
		Avoiding the Potential Pitfalls of Future Technologies
Part 2 Preparing Your Learning Tools
	Chapter 4 Installing a Python Distribution
		Using Anaconda for Machine Learning
			Getting Anaconda
			Defining why Anaconda is used in this book
		Installing Anaconda on Linux
		Installing Anaconda on Mac OS X
		Installing Anaconda on Windows
		Downloading the Datasets and Example Code
			Using Jupyter Notebook
			Defining the code repository
			Understanding the datasets used in this book
	Chapter 5 Beyond Basic Coding in Python
		Defining the Basics You Should Know
			Considering Python basics
			Working with functions
			Working with modules
		Storing Data Using Sets, Lists, and Tuples
			Creating sets
			Performing operations on sets
			Using lists
			Creating and using tuples
		Defining Useful Iterators
			Working with ranges
			Iterating multiple lists using zip
			Working with generators using yield
		Indexing Data Using Dictionaries
			Creating dictionaries
			Storing and retrieving data from dictionaries
	Chapter 6 Working with Google Colab
		Defining Google Colab
			Understanding what Google Colab does
			Considering the online coding difference
			Using local runtime support
			Working with Google Colab features
		Getting a Google Account
			Creating the account
			Signing in
		Working with Notebooks
			Creating a new notebook
			Opening existing notebooks
			Uploading a notebook
			Saving notebooks
			Downloading notebooks
		Performing Common Tasks
			Creating code cells
			Creating text cells
			Creating special cells
			Editing cells
			Moving cells
		Using Hardware Acceleration
		Viewing Your Notebook
			Displaying the table of contents
			Getting notebook information
			Checking code execution
		Executing the Code
		Sharing Your Notebook
		Getting Help
Part 3 Getting Started with the Math Basics
	Chapter 7 Demystifying the Math Behind Machine Learning
		Working with Data
			Learning the terminology
			Understanding scalar and vector operations
			Performing vector multiplication
			Creating a matrix
			Understanding basic operations
			Performing matrix multiplication
			Glancing at advanced matrix operations
			Using vectorization effectively
		Exploring the World of Probabilities
			Getting an overview of probability
			Operating on probabilities
			Conditioning chance by Bayes’ theorem
		Describing the Use of Statistics
	Chapter 8 Descending the Gradient
		Acknowledging Different Kinds of Learning
			Supervised learning
			Unsupervised learning
			Reinforcement learning
		The learning process
			Mapping an unknown function
			Exploring cost functions
			Descending the optimization curve
		Optimizing with big data
			Leveraging sampling
			Using parallelism
			Learning out-of-core
	Chapter 9 Validating Machine Learning
		Considering the Use of Example Data
		Checking Out-of-Sample Errors
			Understanding the concept of samples
			Looking for the holy grail of generalization
			Experimenting how bias and variance work
			Keeping model complexity in mind
			Keeping solutions balanced
			Depicting learning curves
		Training, Validating, and Testing
			Considering the split
			Resorting to cross-validation
			Looking for alternatives in validation
		Optimizing by Cross-Validation
			Sources of predictive performance
			Exploring the hyper-parameter space
			Selecting relevant features
		Avoiding Sample Bias and Leakage Traps
	Chapter 10 Starting with Simple Learners
		Discovering the Incredible Perceptron
			Falling short of a miracle
			Hitting the nonseparability limit
		Growing Greedy Classification Trees
			Predicting outcomes by splitting data
			Pruning overgrown trees
		Taking a Probabilistic Turn
			Understanding Naïve Bayes
			Estimating response with Naïve Bayes
Part 4 Learning from Smart and Big Data
	Chapter 11 Preprocessing Data
		Gathering and Cleaning Data
		Repairing Missing Data
			Identifying missing data
			Choosing the right replacement strategy
		Transforming Distributions
		Creating Your Own Features
			Understanding the need to create features
			Creating features automatically
			Explaining the basics of SVD
			Reorganizing data
		Delimiting Anomalous Data
			Using a univariate strategy
			Resorting to Multivariate Models
	Chapter 12 Leveraging Similarity
		Measuring Similarity between Vectors
			Understanding similarity
			Computing distances for learning
		Using Distances to Locate Clusters
			Checking assumptions and expectations
			Inspecting the gears of the K-means algorithm
		Tuning the K-Means Algorithm
			Experimenting with K-means reliability
			Experimenting with how centroids converge
		Finding Similarity by K-Nearest Neighbors
			Understanding the k parameter
			Experimenting with a flexible algorithm
	Chapter 13 Working with Linear Models the Easy Way
		Starting to Combine Features
			Getting an overview of regression
			Solving problems with a machine learning approach
			Understanding R squared
		Mixing Features of Different Types
		Switching to Probabilities
			Specifying a binary response
			Handling multiple classes
		Guessing the Right Features
			Defining the outcome of features that don’t work together
			Solving overfitting by using greedy selection
			Addressing overfitting by regularization
		Learning One Example at a Time
			Using gradient descent
			Understanding how SGD is different
	Chapter 14 Hitting Complexity with Neural Networks
		Revising the Perceptron
			Pushing forth with feed-forward
			Going even deeper down the rabbit hole
			Pulling back with backpropagation
		Representing the Way of Learning of a Network
			Understanding the problem with overfitting
			Choosing a framework
			Getting your copy of TensorFlow and Keras
			Opening the black box
		Introducing Deep Learning
			Understanding some deep learning essentials
			Explaining the magic of convolutions
			Understanding recurrent neural networks
	Chapter 15 Going a Step Beyond Using Support Vector Machines
		Revisiting the Separation Problem
		Explaining the Algorithm
			Avoiding the pitfalls of nonseparability
			Applying nonlinearity
			Explaining the kernel trick by example
		Classifying and Estimating with SVM
	Chapter 16 Resorting to Ensembles of Learners
		Leveraging Decision Trees
			Growing a forest of trees
			Understanding the importance measures
		Working with Almost Random Guesses
			Bagging predictors with Adaboost
		Boosting Smart Predictors
			Meeting again with gradient descent
			Considering the state of the art in tabular data
		Averaging Different Predictors
			Blending solutions
			Stacking diverse solutions
Part 5 Applying Learning to Real Problems
	Chapter 17 Classifying Images
		Working with a Set of Images
		Revising the State of the Art in Computer Vision
		Extracting Visual Features
		Recognizing Faces Using Eigenfaces
		Classifying Images
	Chapter 18 Scoring Opinions and Sentiments
		Introducing Natural Language Processing
		Revising the State of the Art in NLP
		Understanding How Machines Read
			Defining the input data
			Processing and enhancing text
			Scraping textual datasets from the web
			Handling problems with raw text
		Using Scoring and Classification
			Performing classification tasks
			Analyzing reviews from e-commerce
	Chapter 19 Recommending Products and Movies
		Realizing the Revolution of E-Commerce
		Downloading Rating Data
			Trudging through the MovieLens dataset
			Navigating through anonymous web data
			Encountering the limits of rating data
			Considering collaborative filtering
		Catching the Limits of Behavioral Data
		Integrating Text and Behaviors
			Viewing the attributes
			Obtaining statistics
		Leveraging SVD
			Understanding the SVD connection
			Seeing SVD in action
Part 6 The Part of Tens
	Chapter 20 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
	Chapter 21 Ten Guidelines for Ethical Data Usage
		Obtaining Permission
		Using Sanitization Techniques
		Avoiding Data Inference
		Using Generalizations Correctly
		Shunning Discriminatory Practices
		Detecting Black Swans in Code
		Understanding the Process
		Considering the Consequences of an Action
		Balancing Decision Making
		Verifying a Data Source
	Chapter 22 Ten Machine Learning Packages to Master
		Gensim
		imbalanced-learn
		OpenCV
		SciPy
		SHAP
		Statsmodels
		Modin
		PyTorch
		Poetry
		Snorkel
Index
EULA