Next-Generation Machine Learning with Spark: Covers XGBoost, LightGBM, Spark NLP, Distributed Deep Learning with Keras, and More

Table of Contents
About the Author
About the Technical Reviewer
Acknowledgments
Introduction
Chapter 1: Introduction to Machine Learning
	AI and Machine Learning Use Cases
		Retail
		Transportation
		Financial Services
		Healthcare and Biotechnology
		Manufacturing
		Government
	Machine Learning and Data
		Observations
			Features
			Class Labels
			Models
	Machine Learning Methods
		Supervised Learning
			Classification
				Binary Classification
				Multiclass Classification
				Multilabel Classification
			Classification and Regression Algorithms
				Support Vector Machine
				Logistic Regression
				Naïve Bayes
				Multilayer Perceptron
				Decision Trees
				Random Forest
				Gradient-Boosted Trees
				XGBoost
				LightGBM
			Regression
				Linear Regression
				Survival Regression
		Unsupervised Learning
			Clustering
				K-Means
			Topic Modeling
				Latent Dirichlet Allocation
			Anomaly Detection
				Isolation Forest
				One-Class Support Vector Machines
			Dimensionality Reduction
				Principal Component Analysis
			Recommendations
		Semi-supervised Learning
		Reinforcement Learning
	Deep Learning
	Neural Networks
	Convolutional Neural Networks
	Feature Engineering
		Feature Selection
			Filter Methods
			Wrapper Methods
			Embedded Methods
		Feature Importance
		Feature Extraction
		Feature Construction
	Model Evaluation
		Accuracy
		Precision
		Recall
		F1 Measure
		Area Under the Receiver Operating Characteristic (AUROC)
	Overfitting and Underfitting
	Model Selection
	Summary
	References
Chapter 2: Introduction to Spark and Spark MLlib
	Overview
		Cluster Managers
	Architecture
	Executing Spark Applications
		Cluster Mode
		Client Mode
	Introduction to the spark-shell
		SparkSession
		Resilient Distributed Dataset (RDD)
			Creating an RDD
				parallelize
				textFile
			Transformations
				Map
				FlatMap
				Filter
				Distinct
				ReduceByKey
				Keys
				Values
				Inner Join
				RightOuterJoin and LeftOuterJoin
				Union
				Subtract
				Coalesce
				Repartition
			Actions
				Collect
				Count
				Take
				Foreach
			Lazy Evaluation
			Caching
			Accumulator
			Broadcast Variable
	Spark SQL, Dataset, and DataFrames API
	Spark Data Sources
		CSV
		XML
		JSON
		Relational and MPP Databases
		Parquet
		HBase
		Amazon S3
		Solr
		Microsoft Excel
		Secure FTP
	Introduction to Spark MLlib
		Spark MLlib Algorithms
	ML Pipelines
		Pipeline
		Transformer
		Estimator
		ParamGridBuilder
		CrossValidator
		Evaluator
	Feature Extraction, Transformation, and Selection
		StringIndexer
		Tokenizer
		VectorAssembler
		StandardScaler
		StopWordsRemover
		n-gram
		OneHotEncoderEstimator
		SQLTransformer
		Term Frequency–Inverse Document Frequency (TF–IDF)
		Principal Component Analysis (PCA)
		ChiSqSelector
		Correlation
	Evaluation Metrics
		Area Under the Receiver Operating Characteristic (AUROC)
		F1 Measure
		Root Mean Squared Error (RMSE)
	Model Persistence
	Spark MLlib Example
	Graph Processing
	Beyond Spark MLlib: Third-Party Machine Learning Integrations
	Optimizing Spark and Spark MLlib with Alluxio
		Architecture
	Why Use Alluxio?
		Significantly Improves Big Data Processing Performance and Scalability
		Multiple Frameworks and Applications Can Share Data at Memory Speed
	Provides High Availability and Persistence in Case of Application Termination or Failure
	Optimizes Overall Memory Usage and Minimizes Garbage Collection
	Reduces Hardware Requirements
	Apache Spark and Alluxio
	Summary
	References
Chapter 3: Supervised Learning
	Classification
		Binary Classification
			Multiclass Classification
			Multilabel Classification
		Spark MLlib Classification Algorithms
			Logistic Regression
			Support Vector Machine
			Naïve Bayes
			Multilayer Perceptron
			Decision Trees
			Random Forest
			Gradient-Boosted Trees
				Random Forests vs. Gradient-Boosted Trees
		Third-Party Classification and Regression Algorithms
		Multiclass Classification with Logistic Regression
			Example
		Churn Prediction with Random Forest
			Parameters
			Example
			Feature Importance
		eXtreme Gradient Boosting with XGBoost4J-Spark
			Parameters
			Example
		LightGBM: Fast Gradient Boosting from Microsoft
			Parameters
			Example
		Sentiment Analysis with Naïve Bayes
			Example
	Regression
		Simple Linear Regression
			Example
		Multiple Regression with XGBoost4J-Spark
			Example
		Multiple Regression with LightGBM
	Summary
	References
Chapter 4: Unsupervised Learning
	Clustering with K-Means
	Example
	Topic Modeling with Latent Dirichlet Allocation (LDA)
		Stanford CoreNLP for Spark
		Spark NLP from John Snow Labs
			Pre-trained Pipelines
			Pre-trained Pipelines with Spark DataFrames
			Pre-trained Pipelines with Spark MLlib Pipelines
			Creating Your Own Spark MLlib Pipeline
			Spark NLP LightPipeline
			Spark NLP OCR Module
		Example
	Anomaly Detection with Isolation Forest
		Parameters
		Example
	Dimensionality Reduction with Principal Component Analysis
		Example
	Summary
	References
Chapter 5: Recommendations
	Types of Recommendation Engines
		Collaborative Filtering with Alternating Least Squares
		Parameters
		Example
	Market Basket Analysis with FP-Growth
		Example
		Content-Based Filtering
	Summary
	References
Chapter 6: Graph Analysis
	Introduction to Graphs
		Undirected Graph
			Directed Graph
			Directed Multigraph
			Property Graph
	Graph Analysis Use Cases
		Fraud Detection and Anti-Money Laundering (AML)
			Data Governance and Regulatory Compliance
			Risk Management
			Transportation
			Social Networking
			Network Infrastructure Management
	Introduction to GraphX
		Graph
		VertexRDD
		Edge
		EdgeRDD
		EdgeTriplet
		EdgeContext
		GraphX Example
		Graph Algorithms
			PageRank
			Dynamic PageRank
			Static PageRank
			TriangleCount
			ConnectedComponents
		GraphFrames
	Summary
	References
Chapter 7: Deep Learning
	Neural Networks
	A Brief History of Neural Networks
	Convolutional Neural Networks
		Convolutional Neural Network Architecture
	Feature Detection Layers
		Convolutional Layer
		Rectified Linear Unit (ReLU) Activation Function
		Pooling Layer
		Classification Layers
			Flatten Layer
			Fully Connected (Dense) Layer
			Dropout Layer
			Softmax and Sigmoid Functions
	Deep Learning Frameworks
		TensorFlow
		Theano
		PyTorch
		DeepLearning4J
		CNTK
		Keras
		Deep Learning with Keras
			Multiclass Classification Using the Iris Dataset
			Handwritten Digit Recognition with MNIST
	Distributed Deep Learning with Spark
		Model Parallelism vs. Data Parallelism
		Distributed Deep Learning Frameworks for Spark
			Deep Learning Pipelines
			BigDL
			CaffeOnSpark
			TensorFlowOnSpark
			TensorFrames
			Elephas
			Distributed Keras
	Elephas: Distributed Deep Learning with Keras and Spark
		Handwritten Digit Recognition with MNIST Using Elephas with Keras and Spark
	Distributed Keras (Dist-Keras)
		Handwritten Digit Recognition with MNIST Using Dist-Keras with Keras and Spark
		Dogs and Cats Image Classification
	Summary
	References
Index