Machine Learning: End-to-End guide for Java developers电子书

Machine Learning: End-to-End guide for Java developers

Machine Learning: End-to-End guide for Java developers

Credits

Preface

What this learning path covers

What you need for this learning path

Who this learning path is for

Reader feedback

Customer support

Downloading the example code

Errata

Piracy

Questions

1. Module 1

1. Getting Started with Data Science

Problems solved using data science

Understanding the data science problem - solving approach

Using Java to support data science

Acquiring data for an application

The importance and process of cleaning data

Visualizing data to enhance understanding

The use of statistical methods in data science

Machine learning applied to data science

Using neural networks in data science

Deep learning approaches

Performing text analysis

Visual and audio analysis

Improving application performance using parallel techniques

Assembling the pieces

Summary

2. Data Acquisition

Understanding the data formats used in data science applications

Overview of CSV data

Overview of spreadsheets

Overview of databases

Overview of PDF files

Overview of JSON

Overview of XML

Overview of streaming data

Overview of audio/video/images in Java

Data acquisition techniques

Using the HttpUrlConnection class

Web crawlers in Java

Creating your own web crawler

Using the crawler4j web crawler

Web scraping in Java

Using API calls to access common social media sites

Using OAuth to authenticate users

Handing Twitter

Handling Wikipedia

Handling Flickr

Handling YouTube

Searching by keyword

Summary

3. Data Cleaning

Handling data formats

Handling CSV data

Handling spreadsheets

Handling Excel spreadsheets

Handling PDF files

Handling JSON

Using JSON streaming API

Using the JSON tree API

The nitty gritty of cleaning text

Using Java tokenizers to extract words

Java core tokenizers

Third-party tokenizers and libraries

Transforming data into a usable form

Simple text cleaning

Removing stop words

Finding words in text

Finding and replacing text

Data imputation

Subsetting data

Sorting text

Data validation

Validating data types

Validating dates

Validating e-mail addresses

Validating ZIP codes

Validating names

Cleaning images

Changing the contrast of an image

Smoothing an image

Brightening an image

Resizing an image

Converting images to different formats

Summary

4. Data Visualization

Understanding plots and graphs

Visual analysis goals

Creating index charts

Creating bar charts

Using country as the category

Using decade as the category

Creating stacked graphs

Creating pie charts

Creating scatter charts

Creating histograms

Creating donut charts

Creating bubble charts

Summary

5. Statistical Data Analysis Techniques

Working with mean, mode, and median

Calculating the mean

Using simple Java techniques to find mean

Using Java 8 techniques to find mean

Using Google Guava to find mean

Using Apache Commons to find mean

Calculating the median

Using simple Java techniques to find median

Using Apache Commons to find the median

Calculating the mode

Using ArrayLists to find multiple modes

Using a HashMap to find multiple modes

Using a Apache Commons to find multiple modes

Standard deviation

Sample size determination

Hypothesis testing

Regression analysis

Using simple linear regression

Using multiple regression

Summary

6. Machine Learning

Supervised learning techniques

Decision trees

Decision tree types

Decision tree libraries

Using a decision tree with a book dataset

Testing the book decision tree

Support vector machines

Using an SVM for camping data

Testing individual instances

Bayesian networks

Using a Bayesian network

Unsupervised machine learning

Association rule learning

Using association rule learning to find buying relationships

Reinforcement learning

Summary

7. Neural Networks

Training a neural network

Getting started with neural network architectures

Understanding static neural networks

A basic Java example

Understanding dynamic neural networks

Multilayer perceptron networks

Building the model

Evaluating the model

Predicting other values

Saving and retrieving the model

Learning vector quantization

Self-Organizing Maps

Using a SOM

Displaying the SOM results

Additional network architectures and algorithms

The k-Nearest Neighbors algorithm

Instantaneously trained networks

Spiking neural networks

Cascading neural networks

Holographic associative memory

Backpropagation and neural networks

Summary

8. Deep Learning

Deeplearning4j architecture

Acquiring and manipulating data

Reading in a CSV file

Configuring and building a model

Using hyperparameters in ND4J

Instantiating the network model

Training a model

Testing a model

Deep learning and regression analysis

Preparing the data

Setting up the class

Reading and preparing the data

Building the model

Evaluating the model

Restricted Boltzmann Machines

Reconstruction in an RBM

Configuring an RBM

Deep autoencoders

Building an autoencoder in DL4J

Configuring the network

Building and training the network

Saving and retrieving a network

Specialized autoencoders

Convolutional networks

Building the model

Evaluating the model

Recurrent Neural Networks

Summary

9. Text Analysis

Implementing named entity recognition

Using OpenNLP to perform NER

Identifying location entities

Classifying text

Word2Vec and Doc2Vec

Classifying text by labels

Classifying text by similarity

Understanding tagging and POS

Using OpenNLP to identify POS

Understanding POS tags

Extracting relationships from sentences

Using OpenNLP to extract relationships

Sentiment analysis

Downloading and extracting the Word2Vec model

Building our model and classifying text

Summary

10. Visual and Audio Analysis

Text-to-speech

Using FreeTTS

Getting information about voices

Gathering voice information

Understanding speech recognition

Using CMUPhinx to convert speech to text

Obtaining more detail about the words

Extracting text from an image

Using Tess4j to extract text

Identifying faces

Using OpenCV to detect faces

Classifying visual data

Creating a Neuroph Studio project for classifying visual images

Training the model

Summary

11. Mathematical and Parallel Techniques for Data Analysis

Implementing basic matrix operations

Using GPUs with DeepLearning4j

Using map-reduce

Using Apache's Hadoop to perform map-reduce

Writing the map method

Writing the reduce method

Creating and executing a new Hadoop job

Various mathematical libraries

Using the jblas API

Using the Apache Commons math API

Using the ND4J API

Using OpenCL

Using Aparapi

Creating an Aparapi application

Using Aparapi for matrix multiplication

Using Java 8 streams

Understanding Java 8 lambda expressions and streams

Using Java 8 to perform matrix multiplication

Using Java 8 to perform map-reduce

Summary

12. Bringing It All Together

Defining the purpose and scope of our application

Understanding the application's architecture

Data acquisition using Twitter

Understanding the TweetHandler class

Extracting data for a sentiment analysis model

Building the sentiment model

Processing the JSON input

Cleaning data to improve our results

Removing stop words

Performing sentiment analysis

Analysing the results

Other optional enhancements

Summary

2. Module 2

1. Applied Machine Learning Quick Start

Machine learning and data science

What kind of problems can machine learning solve?

Applied machine learning workflow

Data and problem definition

Measurement scales

Data collection

Find or observe data

Generate data

Sampling traps

Data pre-processing

Data cleaning

Fill missing values

Remove outliers

Data transformation

Data reduction

Unsupervised learning

Find similar items

Euclidean distances

Non-Euclidean distances

The curse of dimensionality

Clustering

Supervised learning

Classification

Decision tree learning

Probabilistic classifiers

Kernel methods

Artificial neural networks

Ensemble learning

Evaluating classification

Precision and recall

Roc curves

Regression

Linear regression

Evaluating regression

Mean squared error

Mean absolute error

Correlation coefficient

Generalization and evaluation

Underfitting and overfitting

Train and test sets

Cross-validation

Leave-one-out validation

Stratification

Summary

2. Java Libraries and Platforms for Machine Learning

The need for Java

Machine learning libraries

Weka

Java machine learning

Apache Mahout

Apache Spark

Deeplearning4j

MALLET

Comparing libraries

Building a machine learning application

Traditional machine learning architecture

Dealing with big data

Big data application architecture

Summary

3. Basic Algorithms – Classification, Regression, and Clustering

Before you start

Classification

Data

Loading data

Feature selection

Learning algorithms

Classify new data

Evaluation and prediction error metrics

Confusion matrix

Choosing a classification algorithm

Regression

Loading the data

Analyzing attributes

Building and evaluating regression model

Linear regression

Regression trees

Tips to avoid common regression problems

Clustering

Clustering algorithms

Evaluation

Summary

4. Customer Relationship Prediction with Ensembles

Customer relationship database

Challenge

Dataset

Evaluation

Basic naive Bayes classifier baseline

Getting the data

Loading the data

Basic modeling

Evaluating models

Implementing naive Bayes baseline

Advanced modeling with ensembles

Before we start

Data pre-processing

Attribute selection

Model selection

Performance evaluation

Summary

5. Affinity Analysis

Market basket analysis

Affinity analysis

Association rule learning

Basic concepts

Database of transactions

Itemset and rule

Support

Confidence

Apriori algorithm

FP-growth algorithm

The supermarket dataset

Discover patterns

Apriori

FP-growth

Other applications in various areas

Medical diagnosis

Protein sequences

Census data

Customer relationship management

IT Operations Analytics

Summary

6. Recommendation Engine with Apache Mahout

Basic concepts

Key concepts

User-based and item-based analysis

Approaches to calculate similarity

Collaborative filtering

Content-based filtering

Hybrid approach

Exploitation versus exploration

Getting Apache Mahout

Configuring Mahout in Eclipse with the Maven plugin

Building a recommendation engine

Book ratings dataset

Loading the data

Loading data from file

Loading data from database

In-memory database

Collaborative filtering

User-based filtering

Item-based filtering

Adding custom rules to recommendations

Evaluation

Online learning engine

Content-based filtering

Summary

7. Fraud and Anomaly Detection

Suspicious and anomalous behavior detection

Unknown-unknowns

Suspicious pattern detection

Anomalous pattern detection

Analysis types

Pattern analysis

Transaction analysis

Plan recognition

Fraud detection of insurance claims

Dataset

Modeling suspicious patterns

Vanilla approach

Dataset rebalancing

Anomaly detection in website traffic

Dataset

Anomaly detection in time series data

Histogram-based anomaly detection

Loading the data

Creating histograms

Density based k-nearest neighbors

Summary

8. Image Recognition with Deeplearning4j

Introducing image recognition

Neural networks

Perceptron

Feedforward neural networks

Autoencoder

Restricted Boltzmann machine

Deep convolutional networks

Image classification

Deeplearning4j

Getting DL4J

MNIST dataset

Loading the data

Building models

Building a single-layer regression model

Building a deep belief network

Build a Multilayer Convolutional Network

Summary

9. Activity Recognition with Mobile Phone Sensors

Introducing activity recognition

Mobile phone sensors

Activity recognition pipeline

The plan

Collecting data from a mobile phone

Installing Android Studio

Loading the data collector

Feature extraction

Collecting training data

Building a classifier

Reducing spurious transitions

Plugging the classifier into a mobile app

Summary

10. Text Mining with Mallet – Topic Modeling and Spam Detection

Introducing text mining

Topic modeling

Text classification

Installing Mallet

Working with text data

Importing data

Importing from directory

Importing from file

Pre-processing text data

Topic modeling for BBC news

BBC dataset

Modeling

Evaluating a model

Reusing a model

Saving a model

Restoring a model

E-mail spam detection

E-mail spam dataset

Feature generation

Training and testing

Model performance

Summary

11. What is Next?

Machine learning in real life

Noisy data

Class unbalance

Feature selection is hard

Model chaining

Importance of evaluation

Getting models into production

Model maintenance

Standards and markup languages

CRISP-DM

SEMMA methodology

Predictive Model Markup Language

Machine learning in the cloud

Machine learning as a service

Web resources and competitions

Datasets

Online courses

Competitions

Websites and blogs

Venues and conferences

Summary

A. References

3. Module 3

1. Machine Learning Review

Machine learning – history and definition

What is not machine learning?

Machine learning – concepts and terminology

Machine learning – types and subtypes

Datasets used in machine learning

Machine learning applications

Practical issues in machine learning

Machine learning – roles and process

Roles

Process

Machine learning – tools and datasets

Datasets

Summary

2. Practical Approach to Real-World Supervised Learning

Formal description and notation

Data quality analysis

Descriptive data analysis

Basic label analysis

Basic feature analysis

Visualization analysis

Univariate feature analysis

Categorical features

Continuous features

Multivariate feature analysis

Data transformation and preprocessing

Feature construction

Handling missing values

Outliers

Discretization

Data sampling

Is sampling needed?

Undersampling and oversampling

Stratified sampling

Training, validation, and test set

Feature relevance analysis and dimensionality reduction

Feature search techniques

Feature evaluation techniques

Filter approach

Univariate feature selection

Information theoretic approach

Statistical approach

Multivariate feature selection

Minimal redundancy maximal relevance (mRMR)

Correlation-based feature selection (CFS)

Wrapper approach

Embedded approach

Model building

Linear models

Linear Regression

Algorithm input and output

How does it work?

Advantages and limitations

Naïve Bayes

Algorithm input and output

How does it work?

Advantages and limitations

Logistic Regression

Algorithm input and output

How does it work?

Advantages and limitations

Non-linear models

Decision Trees

Algorithm inputs and outputs

How does it work?

Advantages and limitations

K-Nearest Neighbors (KNN)

Algorithm inputs and outputs

How does it work?

Advantages and limitations

Support vector machines (SVM)

Algorithm inputs and outputs

How does it work?

Advantages and limitations

Ensemble learning and meta learners

Bootstrap aggregating or bagging

Algorithm inputs and outputs

How does it work?

Random Forest

Advantages and limitations

Boosting

Algorithm inputs and outputs

How does it work?

Advantages and limitations

Model assessment, evaluation, and comparisons

Model assessment

Model evaluation metrics

Confusion matrix and related metrics

ROC and PRC curves

Gain charts and lift curves

Model comparisons

Comparing two algorithms

McNemar's Test

Paired-t test

Wilcoxon signed-rank test

Comparing multiple algorithms

ANOVA test

Friedman's test

Case Study – Horse Colic Classification

Business problem

Machine learning mapping

Data analysis

Label analysis

Features analysis

Supervised learning experiments

Weka experiments

Sample end-to-end process in Java

Weka experimenter and model selection

RapidMiner experiments

Visualization analysis

Feature selection

Model process flow

Model evaluation metrics

Evaluation on Confusion Metrics

ROC Curves, Lift Curves, and Gain Charts

Results, observations, and analysis

Summary

References

3. Unsupervised Machine Learning Techniques

Issues in common with supervised learning

Issues specific to unsupervised learning

Feature analysis and dimensionality reduction

Notation

Linear methods

Principal component analysis (PCA)

Inputs and outputs

How does it work?

Advantages and limitations

Random projections (RP)

Inputs and outputs

How does it work?

Advantages and limitations

Multidimensional Scaling (MDS)

Inputs and outputs

How does it work?

Advantages and limitations

Nonlinear methods

Kernel Principal Component Analysis (KPCA)

Inputs and outputs

How does it work?

Advantages and limitations

Manifold learning

Inputs and outputs

How does it work?

Advantages and limitations

Clustering

Clustering algorithms

k-Means

Inputs and outputs

How does it work?

Advantages and limitations

DBSCAN

Inputs and outputs

How does it work?

Advantages and limitations

Mean shift

Inputs and outputs

How does it work?

Advantages and limitations

Expectation maximization (EM) or Gaussian mixture modeling (GMM)

Input and output

How does it work?

Advantages and limitations

Hierarchical clustering

Input and output

How does it work?

Advantages and limitations

Self-organizing maps (SOM)

Inputs and outputs

How does it work?

Advantages and limitations

Spectral clustering

Inputs and outputs

How does it work?

Advantages and limitations

Affinity propagation

Inputs and outputs

How does it work?

Advantages and limitations

Clustering validation and evaluation

Internal evaluation measures

Notation

R-Squared

Dunn's Indices

Davies-Bouldin index

Silhouette's index

External evaluation measures

Rand index

F-Measure

Normalized mutual information index

Outlier or anomaly detection

Outlier algorithms

Statistical-based

Inputs and outputs

How does it work?

Advantages and limitations

Distance-based methods

Inputs and outputs

How does it work?

Advantages and limitations

Density-based methods

Inputs and outputs

How does it work?

Advantages and limitations

Clustering-based methods

Inputs and outputs

How does it work?

Advantages and limitations

High-dimensional-based methods

Inputs and outputs

How does it work?

Advantages and limitations

One-class SVM

Inputs and outputs

How does it work?

Advantages and limitations

Outlier evaluation techniques

Supervised evaluation

Unsupervised evaluation

Real-world case study

Tools and software

Business problem

Machine learning mapping

Data collection

Data quality analysis

Data sampling and transformation

Feature analysis and dimensionality reduction

PCA

Random projections

ISOMAP

Observations on feature analysis and dimensionality reduction

Clustering models, results, and evaluation

Observations and clustering analysis

Outlier models, results, and evaluation

Observations and analysis

Summary

References

4. Semi-Supervised and Active Learning

Semi-supervised learning

Representation, notation, and assumptions

Semi-supervised learning techniques

Self-training SSL

Inputs and outputs

How does it work?

Advantages and limitations

Co-training SSL or multi-view SSL

Inputs and outputs

How does it work?

Advantages and limitations

Cluster and label SSL

Inputs and outputs

How does it work?

Advantages and limitations

Transductive graph label propagation

Inputs and outputs

How does it work?

Advantages and limitations

Transductive SVM (TSVM)

Inputs and outputs

How does it work?

Advantages and limitations

Case study in semi-supervised learning

Tools and software

Business problem

Machine learning mapping

Data collection

Data quality analysis

Data sampling and transformation

Datasets and analysis

Feature analysis results

Experiments and results

Analysis of semi-supervised learning

Active learning

Representation and notation

Active learning scenarios

Active learning approaches

Uncertainty sampling

How does it work?

Least confident sampling

Smallest margin sampling

Label entropy sampling

Advantages and limitations

Version space sampling

Query by disagreement (QBD)

How does it work?

Query by Committee (QBC)

How does it work?

Advantages and limitations

Data distribution sampling

How does it work?

Expected model change

Expected error reduction

Variance reduction

Density weighted methods

Advantages and limitations

Case study in active learning

Tools and software

Business problem

Machine learning mapping

Data Collection

Data sampling and transformation

Feature analysis and dimensionality reduction

Models, results, and evaluation

Pool-based scenarios

Stream-based scenarios

Analysis of active learning results

Summary

References

5. Real-Time Stream Machine Learning

Assumptions and mathematical notations

Basic stream processing and computational techniques

Stream computations

Sliding windows

Sampling

Concept drift and drift detection

Data management

Partial memory

Full memory

Detection methods

Monitoring model evolution

Widmer and Kubat

Drift Detection Method or DDM

Early Drift Detection Method or EDDM

Monitoring distribution changes

Welch's t test

Kolmogorov-Smirnov's test

CUSUM and Page-Hinckley test

Adaptation methods

Explicit adaptation

Implicit adaptation

Incremental supervised learning

Modeling techniques

Linear algorithms

Online linear models with loss functions

Inputs and outputs

How does it work?

Advantages and limitations

Online Naïve Bayes

Inputs and outputs

How does it work?

Advantages and limitations

Non-linear algorithms

Hoeffding trees or very fast decision trees (VFDT)

Inputs and outputs

How does it work?

Advantages and limitations

Ensemble algorithms

Weighted majority algorithm

Inputs and outputs

How does it work?

Advantages and limitations

Online Bagging algorithm

Inputs and outputs

How does it work?

Advantages and limitations

Online Boosting algorithm

Inputs and outputs

How does it work?

Advantages and limitations

Validation, evaluation, and comparisons in online setting

Model validation techniques

Prequential evaluation

Holdout evaluation

Controlled permutations

Evaluation criteria

Comparing algorithms and metrics

Incremental unsupervised learning using clustering

Modeling techniques

Partition based

Online k-Means

Inputs and outputs

How does it work?

Advantages and limitations

Hierarchical based and micro clustering

Inputs and outputs

How does it work?

Advantages and limitations

Inputs and outputs

How does it work?

Advantages and limitations

Density based

Inputs and outputs

How does it work?

Advantages and limitations

Grid based

Inputs and outputs

How does it work?

Advantages and limitations

Validation and evaluation techniques

Key issues in stream cluster evaluation

Evaluation measures

Cluster Mapping Measures (CMM)

V-Measure

Other external measures

Unsupervised learning using outlier detection

Partition-based clustering for outlier detection

Inputs and outputs

How does it work?

Advantages and limitations

Distance-based clustering for outlier detection

Inputs and outputs

How does it work?

Exact Storm

Abstract-C

Direct Update of Events (DUE)

Micro Clustering based Algorithm (MCOD)

Approx Storm

Advantages and limitations

Validation and evaluation techniques

Case study in stream learning

Tools and software

Business problem

Machine learning mapping

Data collection

Data sampling and transformation

Feature analysis and dimensionality reduction

Models, results, and evaluation

Supervised learning experiments

Concept drift experiments

Clustering experiments

Outlier detection experiments

Analysis of stream learning results

Summary

References

6. Probabilistic Graph Modeling

Probability revisited

Concepts in probability

Conditional probability

Chain rule and Bayes' theorem

Random variables, joint, and marginal distributions

Marginal independence and conditional independence

Factors

Factor types

Distribution queries

Probabilistic queries

MAP queries and marginal MAP queries

Graph concepts

Graph structure and properties

Subgraphs and cliques

Path, trail, and cycles

Bayesian networks

Representation

Definition

Reasoning patterns

Causal or predictive reasoning

Evidential or diagnostic reasoning

Intercausal reasoning

Combined reasoning

Independencies, flow of influence, D-Separation, I-Map

Flow of influence

D-Separation

I-Map

Inference

Elimination-based inference

Variable elimination algorithm

Input and output

How does it work?

Advantages and limitations

Clique tree or junction tree algorithm

Input and output

How does it work?

Advantages and limitations

Propagation-based techniques

Belief propagation

Factor graph

Messaging in factor graph

Input and output

How does it work?

Advantages and limitations

Sampling-based techniques

Forward sampling with rejection

Input and output

How does it work?

Advantages and limitations

Learning

Learning parameters

Maximum likelihood estimation for Bayesian networks

Bayesian parameter estimation for Bayesian network

Prior and posterior using the Dirichlet distribution

Learning structures

Measures to evaluate structures

Methods for learning structures

Constraint-based techniques

Inputs and outputs

How does it work?

Advantages and limitations

Search and score-based techniques

Inputs and outputs

How does it work?

Advantages and limitations

Markov networks and conditional random fields

Representation

Parameterization

Gibbs parameterization

Factor graphs

Log-linear models

Independencies

Global

Pairwise Markov

Markov blanket

Inference

Learning

Conditional random fields

Specialized networks

Tree augmented network

Input and output

How does it work?

Advantages and limitations

Markov chains

Hidden Markov models

Most probable path in HMM

Posterior decoding in HMM

Tools and usage

OpenMarkov

Weka Bayesian Network GUI

Case study

Business problem

Machine learning mapping

Data sampling and transformation

Feature analysis

Models, results, and evaluation

Analysis of results

Summary

References

7. Deep Learning

Multi-layer feed-forward neural network

Inputs, neurons, activation function, and mathematical notation

Multi-layered neural network

Structure and mathematical notations

Activation functions in NN

Sigmoid function

Hyperbolic tangent ("tanh") function

Training neural network

Empirical risk minimization

Parameter initialization

Loss function

Gradients

Gradient at the output layer

Gradient at the Hidden Layer

Parameter gradient

Feed forward and backpropagation

How does it work?

Regularization

L2 regularization

L1 regularization

Limitations of neural networks

Vanishing gradients, local optimum, and slow training

Deep learning

Building blocks for deep learning

Rectified linear activation function

Restricted Boltzmann Machines

Definition and mathematical notation

Conditional distribution

Free energy in RBM

Training the RBM

Sampling in RBM

Contrastive divergence

Inputs and outputs

How does it work?

Persistent contrastive divergence

Autoencoders

Definition and mathematical notations

Loss function

Limitations of Autoencoders

Denoising Autoencoder

Unsupervised pre-training and supervised fine-tuning

Deep feed-forward NN

Input and outputs

How does it work?

Deep Autoencoders

Deep Belief Networks

Inputs and outputs

How does it work?

Deep learning with dropouts

Definition and mathematical notation

Inputs and outputs

How does it work?

Learning Training and testing with dropouts

Sparse coding

Convolutional Neural Network

Local connectivity

Parameter sharing

Discrete convolution

Pooling or subsampling

Normalization using ReLU

CNN Layers

Recurrent Neural Networks

Structure of Recurrent Neural Networks

Learning and associated problems in RNNs

Long Short Term Memory

Gated Recurrent Units

Case study

Tools and software

Business problem

Machine learning mapping

Data sampling and transfor

Feature analysis

Models, results, and evaluation

Basic data handling

Multi-layer perceptron

Parameters used for MLP

Code for MLP

Convolutional Network

Parameters used for ConvNet

Code for CNN

Variational Autoencoder

Parameters used for the Variational Autoencoder

Code for Variational Autoencoder

DBN

Parameter search using Arbiter

Results and analysis

Summary

References

8. Text Mining and Natural Language Processing

NLP, subfields, and tasks

Text categorization

Part-of-speech tagging (POS tagging)

Text clustering

Information extraction and named entity recognition

Sentiment analysis and opinion mining

Coreference resolution

Word sense disambiguation

Machine translation

Semantic reasoning and inferencing

Text summarization

Automating question and answers

Issues with mining unstructured data

Text processing components and transformations

Document collection and standardization

Inputs and outputs

How does it work?

Tokenization

Inputs and outputs

How does it work?

Stop words removal

Inputs and outputs

How does it work?

Stemming or lemmatization

Inputs and outputs

How does it work?

Local/global dictionary or vocabulary?

Feature extraction/generation

Lexical features

Character-based features

Word-based features

Part-of-speech tagging features

Taxonomy features

Syntactic features

Semantic features

Feature representation and similarity

Vector space model

Binary

Term frequency (TF)

Inverse document frequency (IDF)

Term frequency-inverse document frequency (TF-IDF)

Similarity measures

Euclidean distance

Cosine distance

Pairwise-adaptive similarity

Extended Jaccard coefficient

Dice coefficient

Feature selection and dimensionality reduction

Feature selection

Information theoretic techniques

Statistical-based techniques

Frequency-based techniques

Dimensionality reduction

Topics in text mining

Text categorization/classification

Topic modeling

Probabilistic latent semantic analysis (PLSA)

Input and output

How does it work?

Advantages and limitations

Text clustering

Feature transformation, selection, and reduction

Clustering techniques

Generative probabilistic models

Input and output

How does it work?

Advantages and limitations

Distance-based text clustering

Non-negative matrix factorization (NMF)

Input and output

How does it work?

Advantages and limitations

Evaluation of text clustering

Named entity recognition

Hidden Markov models for NER

Input and output

How does it work?

Advantages and limitations

Maximum entropy Markov models for NER

Input and output

How does it work?

Advantages and limitations

Deep learning and NLP

Tools and usage

Mallet

KNIME

Topic modeling with mallet

Business problem

Machine Learning mapping

Data collection

Data sampling and transformation

Feature analysis and dimensionality reduction

Models, results, and evaluation

Analysis of text processing results

Summary

References

9. Big Data Machine Learning – The Final Frontier

What are the characteristics of Big Data?

Big Data Machine Learning

General Big Data framework

Big Data cluster deployment frameworks

Hortonworks Data Platform

Cloudera CDH

Amazon Elastic MapReduce

Microsoft Azure HDInsight

Data acquisition

Publish-subscribe frameworks

Source-sink frameworks

SQL frameworks

Message queueing frameworks

Custom frameworks

Data storage

HDFS

NoSQL

Key-value databases

Document databases

Columnar databases

Graph databases

Data processing and preparation

Hive and HQL

Spark SQL

Amazon Redshift

Real-time stream processing

Machine Learning

Visualization and analysis

Batch Big Data Machine Learning

H2O as Big Data Machine Learning platform

H2O architecture

Machine learning in H2O

Tools and usage

Case study

Business problem

Machine Learning mapping

Data collection

Data sampling and transformation

Experiments, results, and analysis

Feature relevance and analysis

Evaluation on test data

Analysis of results

Spark MLlib as Big Data Machine Learning platform

Spark architecture

Machine Learning in MLlib

Tools and usage

Experiments, results, and analysis

k-Means

k-Means with PCA

Bisecting k-Means (with PCA)

Gaussian Mixture Model

Random Forest

Analysis of results

Real-time Big Data Machine Learning

SAMOA as a real-time Big Data Machine Learning framework

SAMOA architecture

Machine Learning algorithms

Tools and usage

Experiments, results, and analysis

Analysis of results

The future of Machine Learning

Summary

References

A. Linear Algebra

Vector

Scalar product of vectors

Matrix

Transpose of a matrix

Matrix addition

Scalar multiplication

Matrix multiplication

Properties of matrix product

Linear transformation

Matrix inverse

Eigendecomposition

Positive definite matrix

Singular value decomposition (SVD)

B. Probability

Axioms of probability

Bayes' theorem

Density estimation

Mean

Variance

Standard deviation

Gaussian standard deviation

Covariance

Correlation coefficient

Binomial distribution

Poisson distribution

Gaussian distribution

Central limit theorem

Error propagation

D. Bibliography

Index