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DescriptionDig deep into the data with a hands-on guide to machine learning with updated examples and more!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 learningBy 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.Table of ContentsCoverIntroductionAims of This Book“Hands-On” Means Hands-On“What About the Math?”What Will You Have Learned by the End?Balancing Theory and Hands-on LearningSource Code for This BookUsing GitCHAPTER 1: What Is Machine Learning?History of Machine LearningAlgorithm Types for Machine LearningThe Human TouchUses for Machine LearningLanguages for Machine LearningSoftware Used in This BookData RepositoriesSummaryCHAPTER 2: Planning for Machine LearningThe Machine Learning CycleIt All Starts with a QuestionI Don’t Have Data!One Solution Fits All?Defining the ProcessBuilding a Data TeamData ProcessingData StorageData PrivacyData Quality and CleaningThinking About Input DataThinking About Output DataDon’t Be Afraid to ExperimentSummaryCHAPTER 3: Data Acquisition TechniquesScraping DataUsing an APIMigrating DataSummaryCHAPTER 4: Statistics, Linear Regression, and RandomnessWorking with a Basic DatasetIntroducing Basic StatisticsUsing Simple Linear RegressionEmbracing RandomnessSummaryCHAPTER 5: Working with Decision TreesThe Basics of Decision TreesDecision Trees in WekaSummaryCHAPTER 6: ClusteringWhat Is Clustering?Where Is Clustering Used?Clustering ModelsK-Means Clustering with WekaSummaryCHAPTER 7: Association Rules LearningWhere Is Association Rules Learning Used?How Association Rules Learning WorksAlgorithmsMining the Baskets—A Walk-ThroughSummaryCHAPTER 8: Support Vector MachinesWhat Is a Support Vector Machine?Where Are Support Vector Machines Used?The Basic Classification PrinciplesHow Support Vector Machines Approach ClassificationUsing Support Vector Machines in WekaSummaryCHAPTER 9: Artificial Neural NetworksWhat Is a Neural Network?Artificial Neural Network UsesTrusting the Black BoxBreaking Down the Artificial Neural NetworkData Preparation for Artificial Neural NetworksArtificial Neural Networks with WekaImplementing a Neural Network in JavaDeveloping Neural Networks with DeepLearning4JSummaryCHAPTER 10: Machine Learning with Text DocumentsPreparing Text for AnalysisTF/IDFWord2VecBasic Sentiment AnalysisSummaryCHAPTER 11: Machine Learning with ImagesWhat Is an Image?Basic Classification with Neural NetworksConvolutional Neural NetworksTransfer LearningSummaryCHAPTER 12: Machine Learning Streaming with KafkaWhat You Will Learn in This ChapterFrom Machine Learning to Machine Learning EngineerFrom Batch Processing to Streaming Data ProcessingWhat Is Kafka?Installing KafkaTopics ManagementKafka Tool UIWriting Your Own Producers and ConsumersBuilding a Streaming Machine Learning SystemKafka TopicsKafka ConnectThe REST API MicroserviceProcessing Commands and EventsMaking PredictionsRunning the ProjectSummaryCHAPTER 13: Apache SparkSpark: A Hadoop Replacement?Java, Scala, or Python?Downloading and Installing SparkA Quick Intro to SparkComparing Hadoop MapReduce to SparkWriting Stand-Alone Programs with SparkSpark SQLSpark StreamingMLib: The Machine Learning LibrarySummaryCHAPTER 14: Machine Learning with RInstalling RYour First RunInstalling R-StudioThe R BasicsSimple StatisticsSimple Linear RegressionBasic Sentiment AnalysisApriori Association RulesAccessing R from JavaSummaryAPPENDIX A: Kafka Quick StartInstalling KafkaStarting ZookeeperStarting KafkaCreating TopicsListing TopicsDescribing a TopicDeleting TopicsRunning a Console ProducerRunning a Console ConsumerAPPENDIX B: The Twitter API Developer Application ConfigurationAPPENDIX C: Useful Unix CommandsUsing Sample DataShowing the Contents: cat, more, and lessFiltering Content: grepSorting Data: sortFinding Unique Occurrences: uniqShowing the Top of a File: headCounting Words: wcLocating Anything: findCombining Commands and Redirecting OutputPicking a Text EditorAPPENDIX D: Further ReadingMachine LearningStatisticsBig Data and Data ScienceVisualizationMaking DecisionsDatasetsBlogsUseful WebsitesThe Tools of the TradeIndexEnd User License AgreementAuthor BiographyJASON BELL has worked in software development for over thirty years, now he focuses on large volume data solutions and helping retail and finance customers gain insight from that data with machine learning. He is also an active committee member for several international technology conferences.