Machine Learning For Dummies By John Paul Mueller & Luca Massaron
Informations about the book:
Title: Machine Learning For Dummies
Author: John Paul Mueller & Luca Massaron
Size: 12
Format: PDF
Year: 2016
Pages: 435
Book 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 Âmachine 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 Âmachine 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
CHAPTER 2: Learning in the Age of Big Data
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 Âprocessors 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 Âenvironments
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
Understanding the datasets used in this book
Defining the code repository
CHAPTER 5: Coding in R Using RStudio
Understanding the Basic Data Types
Working with Vectors
Organizing Data Using Lists
Working with Matrices
Creating a basic matrix
Changing the vector arrangement
Accessing individual elements
Naming the rows and columns
Interacting with Multiple Dimensions Using Arrays
Creating a basic array
Naming the rows and columns
Creating a Data Frame
Understanding factors
Creating a basic data frame
Interacting with data frames
Expanding a data frame
Performing Basic Statistical Tasks
Making decisions
Working with loops
vi Machine Learning For Dummies Performing looped tasks without loops
Working with functions
Finding mean and median
Charting your data
CHAPTER 6: Installing a Python Distribution
Choosing a Python Distribution with Machine Learning in Mind
Getting Continuum Analytics Anaconda
Getting Enthought Canopy Express
Getting pythonxy
Getting WinPython
Installing Python on Linux
Installing Python on Mac OS X
Installing Python on Windows
Downloading the Datasets and Example Code
Using Jupyter Notebook
Defining the code repository
Understanding the datasets used in this book
CHAPTER 7: Coding in Python Using Anaconda
Working with Numbers and Logic
Performing variable assignments
Doing arithmetic
Comparing data using Boolean expressions
Creating and Using Strings
Interacting with Dates
Creating and Using Functions
Creating reusable functions
Calling functions
Working with global and local variables
Using Conditional and Loop Statements
Making decisions using the if statement
Choosing between multiple options using nested decisions
Performing repetitive tasks using for
Using the while statement
Storing Data Using Sets, Lists, and Tuples
Creating sets
Performing operations on sets
Creating lists
Creating and using 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
Creating a matrix
Understanding basic operations
Performing matrix multiplication
Glancing at advanced matrix operations
Using vectorization effectively
Exploring the World of Probabilities
Operating on probabilities
Conditioning chance by Bayes’ theorem
Describing the Use of Statistics
CHAPTER 10: Descending the Right Curve
Interpreting Learning As Optimization
Supervised learning
Unsupervised learning
Reinforcement learning
The learning process
Exploring Cost Functions
Descending the Error Curve
Updating by Mini-Batch and Online
CHAPTER 11: Validating Machine Learning
Checking Out-of-Sample Errors
Looking for generalization
Getting to Know the Limits of Bias
Keeping Model Complexity in Mind
Keeping Solutions Balanced
Depicting learning curves
Training, Validating, and Testing
Resorting to Cross-Validation
Looking for Alternatives in Validation
Optimizing Cross-Validation Choices
Exploring the space of hyper-parameters
Avoiding Sample Bias and Leakage Traps
Watching out for snooping
CHAPTER 12: Starting with Simple Learners
Discovering the Incredible Perceptron
Falling short of a miracle
Touching 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 13: 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
Compressing Data
Delimiting Anomalous Data
CHAPTER 14: 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 algorithm
Tuning the K-Means Algorithm
Experimenting K-means reliability
Experimenting with how centroids converge
Searching for Classification by K-Nearest Neighbors
Leveraging the Correct K Parameter
Understanding the k parameter
Experimenting with a flexible algorithm
CHAPTER 15: Working with Linear Models the Easy Way
Starting to Combine Variables
Mixing Variables 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 selection
Learning One Example at a Time
Using gradient descent
Understanding how SGD is different
CHAPTER 16: Hitting Complexity with Neural Networks
Learning and Imitating from Nature
Going forth with feed-forward
Going even deeper down the rabbit hole
Getting Back with Backpropagation
Struggling with Overfitting
Understanding the problem
Opening the black box
Introducing Deep Learning
CHAPTER 17: Going a Step beyond Using Support
Vector Machines
Revisiting the Separation Problem: A New Approach
Explaining the Algorithm
Getting into the math of an SVM
Avoiding the pitfalls of nonseparability
Applying Nonlinearity
Demonstrating the kernel trick by example
Discovering the different kernels
Illustrating Hyper-Parameters
Classifying and Estimating with SVM
CHAPTER 18: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
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
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 21:Recommending Products and Movies
Realizing the Revolution
Downloading Rating Data
Trudging through the MovieLens dataset
Navigating through anonymous web data
Encountering the limits of rating data
Leveraging SVD
Considering the origins of SVD
Understanding the SVD connection
Seeing SVD in action
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
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 Âmachine 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 Âmachine 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
CHAPTER 2: Learning in the Age of Big Data
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 Âprocessors 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 Âenvironments
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
Understanding the datasets used in this book
Defining the code repository
CHAPTER 5: Coding in R Using RStudio
Understanding the Basic Data Types
Working with Vectors
Organizing Data Using Lists
Working with Matrices
Creating a basic matrix
Changing the vector arrangement
Accessing individual elements
Naming the rows and columns
Interacting with Multiple Dimensions Using Arrays
Creating a basic array
Naming the rows and columns
Creating a Data Frame
Understanding factors
Creating a basic data frame
Interacting with data frames
Expanding a data frame
Performing Basic Statistical Tasks
Making decisions
Working with loops
vi Machine Learning For Dummies Performing looped tasks without loops
Working with functions
Finding mean and median
Charting your data
CHAPTER 6: Installing a Python Distribution
Choosing a Python Distribution with Machine Learning in Mind
Getting Continuum Analytics Anaconda
Getting Enthought Canopy Express
Getting pythonxy
Getting WinPython
Installing Python on Linux
Installing Python on Mac OS X
Installing Python on Windows
Downloading the Datasets and Example Code
Using Jupyter Notebook
Defining the code repository
Understanding the datasets used in this book
CHAPTER 7: Coding in Python Using Anaconda
Working with Numbers and Logic
Performing variable assignments
Doing arithmetic
Comparing data using Boolean expressions
Creating and Using Strings
Interacting with Dates
Creating and Using Functions
Creating reusable functions
Calling functions
Working with global and local variables
Using Conditional and Loop Statements
Making decisions using the if statement
Choosing between multiple options using nested decisions
Performing repetitive tasks using for
Using the while statement
Storing Data Using Sets, Lists, and Tuples
Creating sets
Performing operations on sets
Creating lists
Creating and using 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
Creating a matrix
Understanding basic operations
Performing matrix multiplication
Glancing at advanced matrix operations
Using vectorization effectively
Exploring the World of Probabilities
Operating on probabilities
Conditioning chance by Bayes’ theorem
Describing the Use of Statistics
CHAPTER 10: Descending the Right Curve
Interpreting Learning As Optimization
Supervised learning
Unsupervised learning
Reinforcement learning
The learning process
Exploring Cost Functions
Descending the Error Curve
Updating by Mini-Batch and Online
CHAPTER 11: Validating Machine Learning
Checking Out-of-Sample Errors
Looking for generalization
Getting to Know the Limits of Bias
Keeping Model Complexity in Mind
Keeping Solutions Balanced
Depicting learning curves
Training, Validating, and Testing
Resorting to Cross-Validation
Looking for Alternatives in Validation
Optimizing Cross-Validation Choices
Exploring the space of hyper-parameters
Avoiding Sample Bias and Leakage Traps
Watching out for snooping
CHAPTER 12: Starting with Simple Learners
Discovering the Incredible Perceptron
Falling short of a miracle
Touching 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 13: 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
Compressing Data
Delimiting Anomalous Data
CHAPTER 14: 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 algorithm
Tuning the K-Means Algorithm
Experimenting K-means reliability
Experimenting with how centroids converge
Searching for Classification by K-Nearest Neighbors
Leveraging the Correct K Parameter
Understanding the k parameter
Experimenting with a flexible algorithm
CHAPTER 15: Working with Linear Models the Easy Way
Starting to Combine Variables
Mixing Variables 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 selection
Learning One Example at a Time
Using gradient descent
Understanding how SGD is different
CHAPTER 16: Hitting Complexity with Neural Networks
Learning and Imitating from Nature
Going forth with feed-forward
Going even deeper down the rabbit hole
Getting Back with Backpropagation
Struggling with Overfitting
Understanding the problem
Opening the black box
Introducing Deep Learning
CHAPTER 17: Going a Step beyond Using Support
Vector Machines
Revisiting the Separation Problem: A New Approach
Explaining the Algorithm
Getting into the math of an SVM
Avoiding the pitfalls of nonseparability
Applying Nonlinearity
Demonstrating the kernel trick by example
Discovering the different kernels
Illustrating Hyper-Parameters
Classifying and Estimating with SVM
CHAPTER 18: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
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
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 21:Recommending Products and Movies
Realizing the Revolution
Downloading Rating Data
Trudging through the MovieLens dataset
Navigating through anonymous web data
Encountering the limits of rating data
Leveraging SVD
Considering the origins of SVD
Understanding the SVD connection
Seeing SVD in action
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
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