book

Thoughtful Machine Learning with Python

Name: Thoughtful Machine Learning with Python
Author: Matthew Kirk
ISBN: 9781491924136

by Matthew Kirk

January 2017

Intermediate to advanced

218 pages

5h 10m

English

O'Reilly Media, Inc.

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Preface
Conventions Used in This BookUsing Code ExamplesO’Reilly SafariHow to Contact UsAcknowledgments
1. Probably Approximately Correct Software
Writing Software RightSOLIDTesting or TDDRefactoringWriting the Right SoftwareWriting the Right Software with Machine LearningWhat Exactly Is Machine Learning?The High Interest Credit Card Debt of Machine LearningSOLID Applied to Machine LearningMachine Learning Code Is Complex but Not ImpossibleTDD: Scientific Method 2.0Refactoring Our Way to KnowledgeThe Plan for the Book
2. A Quick Introduction to Machine Learning
What Is Machine Learning?Supervised LearningUnsupervised LearningReinforcement LearningWhat Can Machine Learning Accomplish?Mathematical Notation Used Throughout the BookConclusion
3. K-Nearest Neighbors
How Do You Determine Whether You Want to Buy a House?How Valuable Is That House?Hedonic RegressionWhat Is a Neighborhood?K-Nearest NeighborsMr. K’s Nearest NeighborhoodDistancesTriangle InequalityGeometrical DistanceComputational DistancesStatistical DistancesCurse of DimensionalityHow Do We Pick K?Guessing KHeuristics for Picking KValuing Houses in SeattleAbout the DataGeneral StrategyCoding and Testing DesignKNN Regressor ConstructionKNN TestingConclusion
4. Naive Bayesian Classification
Using Bayes’ Theorem to Find Fraudulent OrdersConditional ProbabilitiesProbability SymbolsInverse Conditional Probability (aka Bayes’ Theorem)Naive Bayesian ClassifierThe Chain RuleNaiveté in Bayesian ReasoningPseudocountSpam FilterSetup NotesCoding and Testing DesignData SourceEmailObjectTokenization and ContextSpamTrainerError Minimization Through Cross-ValidationConclusion
5. Decision Trees and Random Forests
The Nuances of MushroomsClassifying Mushrooms Using a Folk TheoremFinding an Optimal Switch PointInformation GainGINI ImpurityVariance ReductionPruning TreesEnsemble LearningWriting a Mushroom ClassifierConclusion
6. Hidden Markov Models
Tracking User Behavior Using State MachinesEmissions/Observations of Underlying StatesSimplification Through the Markov AssumptionUsing Markov Chains Instead of a Finite State MachineHidden Markov ModelEvaluation: Forward-Backward AlgorithmMathematical Representation of the Forward-Backward AlgorithmUsing User BehaviorThe Decoding Problem Through the Viterbi AlgorithmThe Learning ProblemPart-of-Speech Tagging with the Brown CorpusSetup NotesCoding and Testing DesignThe Seam of Our Part-of-Speech Tagger: CorpusParserWriting the Part-of-Speech TaggerCross-Validating to Get Confidence in the ModelHow to Make This Model BetterConclusion
7. Support Vector Machines
Customer Happiness as a Function of What They SaySentiment Classification Using SVMsThe Theory Behind SVMsDecision BoundaryMaximizing BoundariesKernel Trick: Feature TransformationOptimizing with SlackSentiment AnalyzerSetup NotesCoding and Testing DesignSVM Testing StrategiesCorpus ClassCorpusSet ClassModel Validation and the Sentiment ClassifierAggregating SentimentExponentially Weighted Moving AverageMapping Sentiment to Bottom LineConclusion
8. Neural Networks
What Is a Neural Network?History of Neural NetsBoolean LogicPerceptronsHow to Construct Feed-Forward Neural NetsInput LayerHidden LayersNeuronsActivation FunctionsOutput LayerTraining AlgorithmsThe Delta RuleBack PropagationQuickPropRPropBuilding Neural NetworksHow Many Hidden Layers?How Many Neurons for Each Layer?Tolerance for Error and Max EpochsUsing a Neural Network to Classify a LanguageSetup NotesCoding and Testing DesignThe DataWriting the Seam Test for LanguageCross-Validating Our Way to a Network ClassTuning the Neural NetworkPrecision and Recall for Neural NetworksWrap-Up of ExampleConclusion
9. Clustering
Studying Data Without Any BiasUser CohortsTesting Cluster MappingsFitness of a ClusterSilhouette CoefficientComparing Results to Ground TruthK-Means ClusteringThe K-Means AlgorithmDownside of K-Means ClusteringEM ClusteringAlgorithmThe Impossibility TheoremExample: Categorizing MusicSetup NotesGathering the DataCoding DesignAnalyzing the Data with K-MeansEM Clustering Our DataThe Results from the EM Jazz ClusteringConclusion

10. Improving Models and Data Extraction
Debate ClubPicking Better DataFeature SelectionExhaustive SearchRandom Feature SelectionA Better Feature Selection AlgorithmMinimum Redundancy Maximum Relevance Feature SelectionFeature Transformation and Matrix FactorizationPrincipal Component AnalysisIndependent Component AnalysisEnsemble LearningBaggingBoostingConclusion
11. Putting It Together: Conclusion
Machine Learning Algorithms RevisitedHow to Use This Information to Solve ProblemsWhat’s Next for You?
Index

Overview

Gain the confidence you need to apply machine learning in your daily work. With this practical guide, author Matthew Kirk shows you how to integrate and test machine learning algorithms in your code, without the academic subtext.

Featuring graphs and highlighted code examples throughout, the book features tests with Python’s Numpy, Pandas, Scikit-Learn, and SciPy data science libraries. If you’re a software engineer or business analyst interested in data science, this book will help you:

Reference real-world examples to test each algorithm through engaging, hands-on exercises
Apply test-driven development (TDD) to write and run tests before you start coding
Explore techniques for improving your machine-learning models with data extraction and feature development
Watch out for the risks of machine learning, such as underfitting or overfitting data
Work with K-Nearest Neighbors, neural networks, clustering, and other algorithms

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