Machine Learning : Hands-On for Developers and Technical Professionals.
Title:
Machine Learning : Hands-On for Developers and Technical Professionals.
Author:
Bell, Jason.
ISBN:
9781118889398
Personal Author:
Edition:
1st ed.
Physical Description:
1 online resource (407 pages)
Contents:
Cover -- TItle Page -- Copyright -- Contents -- Chapter 1 What Is Machine Learning? -- History of Machine Learning -- Alan Turing -- Arthur Samuel -- Tom M. Mitchell -- Summary Definition -- Algorithm Types for Machine Learning -- Supervised Learning -- Unsupervised Learning -- The Human Touch -- Uses for Machine Learning -- Software -- Stock Trading -- Robotics -- Medicine and Healthcare -- Advertising -- Retail and E-Commerce -- Gaming Analytics -- The Internet of Things -- Languages for Machine Learning -- Python -- R -- Matlab -- Scala -- Clojure -- Ruby -- Software Used in This Book -- Checking the Java Version -- Weka Toolkit -- Mahout -- SpringXD -- Hadoop -- Using an IDE -- Data Repositories -- UC Irvine Machine Learning Repository -- Infochimps -- Kaggle -- Summary -- Chapter 2 Planning for Machine Learning -- The Machine Learning Cycle -- It All Starts with a Question -- I Don't Have Data! -- Starting Local -- Competitions -- One Solution Fits All? -- Defining the Process -- Planning -- Developing -- Testing -- Reporting -- Refining -- Production -- Building a Data Team -- Mathematics and Statistics -- Programming -- Graphic Design -- Domain Knowledge -- Data Processing -- Using Your Computer -- A Cluster of Machines -- Cloud-Based Services -- Data Storage -- Physical Discs -- Cloud-Based Storage -- Data Privacy -- Cultural Norms -- Generational Expectations -- The Anonymity of User Data -- Don't Cross "The Creepy Line" -- Data Quality and Cleaning -- Presence Checks -- Type Checks -- Length Checks -- Range Checks -- Format Checks -- The Britney Dilemma -- What's in a Country Name? -- Dates and Times -- Final Thoughts on Data Cleaning -- Thinking about Input Data -- Raw Text -- Comma Separated Variables -- JSON -- YAML -- XML -- Spreadsheets -- Databases -- Thinking about Output Data.
Don't Be Afraid to Experiment -- Summary -- Chapter 3 Working with Decision Trees -- The Basics of Decision Trees -- Uses for Decision Trees -- Advantages of Decision Trees -- Limitations of Decision Trees -- Different Algorithm Types -- How Decision Trees Work -- Decision Trees in Weka -- The Requirement -- Training Data -- Using Weka to Create a Decision Tree -- Creating Java Code from the Classification -- Testing the Classifier Code -- Thinking about Future Iterations -- Summary -- Chapter 4 Bayesian Networks -- Pilots to Paperclips -- A Little Graph Theory -- A Little Probability Theory -- Coin Flips -- Conditional Probability -- Winning the Lottery -- Bayes' Theorem -- How Bayesian Networks Work -- Assigning Probabilities -- Calculating Results -- Node Counts -- Using Domain Experts -- A Bayesian Network Walkthrough -- Java APIs for Bayesian Networks -- Planning the Network -- Coding Up the Network -- Summary -- Chapter 5 Artificial Neural Networks -- What Is a Neural Network? -- Artificial Neural Network Uses -- High-Frequency Trading -- Credit Applications -- Data Center Management -- Robotics -- Medical Monitoring -- Breaking Down the Artificial Neural Network -- Perceptrons -- Activation Functions -- Multilayer Perceptrons -- Back Propagation -- Data Preparation for Artificial Neural Networks -- Artificial Neural Networks with Weka -- Generating a Dataset -- Loading the Data into Weka -- Configuring the Multilayer Perceptron -- Training the Network -- Altering the Network -- Increasing the Test Data Size -- Implementing a Neural Network in Java -- Create the Project -- The Code -- Converting from CSV to Arff -- Running the Neural Network -- Summary -- Chapter 6 Association Rules Learning -- Where Is Association Rules Learning Used? -- Web Usage Mining -- Beer and Diapers.
How Association Rules Learning Works -- Support -- Confidence -- Lift -- Conviction -- Defining the Process -- Algorithms -- Apriori -- FP-Growth -- Mining the Baskets-A Walkthrough -- Downloading the Raw Data -- Setting Up the Project in Eclipse -- Setting Up the Items Data File -- Setting Up the Data -- Running Mahout -- Inspecting the Results -- Putting It All Together -- Further Development -- Summary -- Chapter 7 Support Vector Machines -- What Is a Support Vector Machine? -- Where Are Support Vector Machines Used? -- The Basic Classification Principles -- Binary and Multiclass Classification -- Linear Classifiers -- Confidence -- Maximizing and Minimizing to Find the Line -- How Support Vector Machines Approach Classification -- Using Linear Classification -- Using Non-Linear Classification -- Using Support Vector Machines in Weka -- Installing LibSVM -- A Classification Walkthrough -- Implementing LibSVM with Java -- Summary -- Chapter 8 Clustering -- What Is Clustering? -- Where Is Clustering Used? -- The Internet -- Business and Retail -- Law Enforcement -- Computing -- Clustering Models -- How the K-Means Works -- Calculating the Number of Clusters in a Dataset -- K-Means Clustering with Weka -- Preparing the Data -- The Workbench Method -- The Command-Line Method -- The Coded Method -- Summary -- Chapter 9 Machine Learning in Real Time with Spring XD -- Capturing the Firehose of Data -- Considerations of Using Data in Real Time -- Potential Uses for a Real-Time System -- Using Spring XD -- Spring XD Streams -- Input Sources, Sinks, and Processors -- Learning from Twitter Data -- The Development Plan -- Configuring the Twitter API Developer Application -- Configuring Spring XD -- Starting the Spring XD Server -- Creating Sample Data -- The Spring XD Shell -- Streams 101 -- Spring XD and Twitter.
Setting the Twitter Credentials -- Creating Your First Twitter Stream -- Where to Go from Here -- Introducing Processors -- How Processors Work within a Stream -- Creating Your Own Processor -- Real-Time Sentiment Analysis -- How the Basic Analysis Works -- Creating a Sentiment Processor -- Spring XD Taps -- Summary -- Chapter 10 Machine Learning as a Batch Process -- Is It Big Data? -- Considerations for Batch Processing Data -- Volume and Frequency -- How Much Data? -- Which Process Method? -- Practical Examples of Batch Processes -- Hadoop -- Sqoop -- Pig -- Mahout -- Cloud-Based Elastic Map Reduce -- A Note about the Walkthroughs -- Using the Hadoop Framework -- The Hadoop Architecture -- Setting Up a Single-Node Cluster -- How MapReduce Works -- Mining the Hashtags -- Hadoop Support in Spring XD -- Objectives for This Walkthrough -- What's a Hashtag? -- Creating the MapReduce Classes -- Performing ETL on Existing Data -- Product Recommendation with Mahout -- Mining Sales Data -- Welcome to My Coffee Shop! -- Going Small Scale -- Writing the Core Methods -- Using Hadoop and MapReduce -- Using Pig to Mine Sales Data -- Scheduling Batch Jobs -- Summary -- Chapter 11 Apache Spark -- Spark: A Hadoop Replacement? -- Java, Scala, or Python? -- Scala Crash Course -- Installing Scala -- Packages -- Data Types -- Classes -- Calling Functions -- Operators -- Control Structures -- Downloading and Installing Spark -- A Quick Intro to Spark -- Starting the Shell -- Data Sources -- Testing Spark -- Spark Monitor -- Comparing Hadoop MapReduce to Spark -- Writing Standalone Programs with Spark -- Spark Programs in Scala -- Installing SBT -- Spark Programs in Java -- Spark Program Summary -- Spark SQL -- Basic Concepts -- Using SparkSQL with RDDs -- Spark Streaming -- Basic Concepts.
Creating Your First Stream with Scala -- Creating Your First Stream with Java -- MLib: The Machine Learning Library -- Dependencies -- Decision Trees -- Clustering -- Summary -- Chapter 12 Machine Learning with R -- Installing R -- Mac OSX -- Windows -- Linux -- Your First Run -- Installing R-Studio -- The R Basics -- Variables and Vectors -- Matrices -- Lists -- Data Frames -- Installing Packages -- Loading in Data -- Plotting Data -- Simple Statistics -- Simple Linear Regression -- Creating the Data -- The Initial Graph -- Regression with the Linear Model -- Making a Prediction -- Basic Sentiment Analysis -- Functions to Load in Word Lists -- Writing a Function to Score Sentiment -- Testing the Function -- Apriori Association Rules -- Installing the ARules Package -- The Training Data -- Importing the Transaction Data -- Running the Apriori Algorithm -- Inspecting the Results -- Accessing R from Java -- Installing the rJava Package -- Your First Java Code in R -- Calling R from Java Programs -- Setting Up an Eclipse Project -- Creating the Java/R Class -- Running the Example -- Extending Your R Implementations -- R and Hadoop -- The RHadoop Project -- A Sample Map Reduce Job in RHadoop -- Connecting to Social Media with R -- Summary -- Appendix A SpringXD Quick Start -- Installing Manually -- Starting SpringXD -- Creating a Stream -- Adding a Twitter Application Key -- Appendix B Hadoop 1.x Quick Start -- Downloading and Installing Hadoop -- Formatting the HDFS Filesystem -- Starting and Stopping Hadoop -- Process List of a Basic Job -- Appendix C Useful Unix Commands -- Using Sample Data -- Showing the Contents: cat, more, and less -- Example Command -- Expected Output -- Filtering Content: grep -- Example Command for Finding Text -- Example Output -- Sorting Data: sort -- Example Command for Basic Sorting -- Example Output.
Finding Unique Occurrences: uniq.
Abstract:
Dig deep into the data with a hands-on guide to machine learning Machine Learning: Hands-On for Developers and Technical Professionals provides hands-on instruction and fully-coded working examples for the most common machine learning techniques used by developers and technical professionals. The book contains a breakdown of each ML variant, explaining how it works and how it is used within certain industries, allowing readers to incorporate the presented techniques into their own work as they follow along. A core tenant of machine learning is a strong focus on data preparation, and a full exploration of the various types of learning algorithms illustrates how the proper tools can help any developer extract information and insights from existing data. The book includes a full complement of Instructor's Materials to facilitate use in the classroom, making this resource useful for students and as a professional reference. At its core, machine learning is a mathematical, algorithm-based technology that forms the basis of historical data mining and modern big data science. Scientific analysis of big data requires a working knowledge of machine learning, which forms predictions based on known properties learned from training data. Machine Learning is an accessible, comprehensive guide for the non-mathematician, providing clear guidance that allows readers to:Learn the languages of machine learning including Hadoop, Mahout, and WekaUnderstand decision trees, Bayesian networks, and artificial neural networksImplement Association Rule, Real Time, and Batch learningDevelop a strategic plan for safe, effective, and efficient machine learning By learning to construct a system that can learn from data, readers can increase their utility across industries. Machine learning sits at the core of deep dive data analysis and visualization, which is increasingly in
demand as companies discover the goldmine hiding in their existing data. For the tech professional involved in data science, Machine Learning: Hands-On for Developers and Technical Professionals provides the skills and techniques required to dig deeper.
Local Note:
Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2017. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries.
Subject Term:
Genre:
Electronic Access:
Click to View