book

Applied Supervised Learning with Python

Name: Applied Supervised Learning with Python
ISBN: 9781789954920

by Benjamin Johnston, Ishita Mathur

April 2019

Intermediate to advanced

404 pages

7h 25m

English

Packt Publishing

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Preface
About the BookAbout the AuthorsObjectivesAudienceApproachHardware RequirementsSoftware RequirementsConventionsInstallation and SetupInstalling the Code BundleAdditional Resources
Chapter 1
Python Machine Learning ToolkitIntroductionSupervised Machine LearningWhen to Use Supervised LearningWhy Python?Jupyter NotebooksExercise 1: Launching a Jupyter NotebookExercise 2: Hello WorldExercise 3: Order of Execution in a Jupyter NotebookExercise 4: Advantages of Jupyter NotebooksPython Packages and ModulespandasLoading Data in pandasExercise 5: Loading and Summarizing the Titanic DatasetExercise 6: Indexing and Selecting DataExercise 7: Advanced Indexing and Selectionpandas MethodsExercise 8: Splitting, Applying, and Combining Data SourcesLambda FunctionsExercise 9: Lambda FunctionsData Quality ConsiderationsManaging Missing DataClass ImbalanceLow Sample SizeActivity 1: pandas FunctionsSummary
Chapter 2
Exploratory Data Analysis and VisualizationIntroductionExploratory Data Analysis (EDA)Exercise 10: Importing Libraries for Data ExplorationSummary Statistics and Central ValuesStandard DeviationPercentilesExercise 11: Summary Statistics of Our DatasetMissing ValuesFinding Missing ValuesExercise 12: Visualizing Missing ValuesImputation Strategies for Missing ValuesExercise 13: Imputation Using pandasExercise 14: Imputation Using scikit-learnExercise 15: Imputation Using Inferred ValuesActivity 2: Summary Statistics and Missing ValuesDistribution of ValuesTarget VariableExercise 16: Plotting a Bar ChartCategorical DataExercise 17: Datatypes for Categorical VariablesExercise 18: Calculating Category Value CountsExercise 19: Plotting a Pie ChartContinuous DataExercise 20: Plotting a HistogramExercise 21: Skew and KurtosisActivity 3: Visually Representing the Distribution of ValuesRelationships within the DataRelationship between Two Continuous VariablesExercise 22: Plotting a Scatter PlotExercise 23: Correlation HeatmapExercise 24: PairplotRelationship between a Continuous and a Categorical VariableExercise 25: Bar ChartExercise 26: Box PlotRelationship between Two Categorical VariablesExercise 27: Stacked Bar ChartActivity 4: Relationships Within the DataSummary
Chapter 3
Regression AnalysisIntroductionRegression and Classification ProblemsData, Models, Training, and EvaluationLinear RegressionExercise 28: Plotting Data with a Moving AverageActivity 5: Plotting Data with a Moving AverageLeast Squares MethodThe scikit-learn Model APIExercise 29: Fitting a Linear Model Using the Least Squares MethodActivity 6: Linear Regression Using the Least Squares MethodLinear Regression with Dummy VariablesExercise 30: Introducing Dummy VariablesActivity 7: Dummy VariablesParabolic Model with Linear RegressionExercise 31: Parabolic Models with Linear RegressionActivity 8: Other Model Types with Linear RegressionGeneric Model TrainingGradient DescentExercise 32: Linear Regression with Gradient DescentExercise 33: Optimizing Gradient DescentActivity 9: Gradient DescentMultiple Linear RegressionExercise 34: Multiple Linear RegressionAutoregression ModelsExercise 35: Creating an Autoregression ModelActivity 10: AutoregressorsSummary
Chapter 4
ClassificationIntroductionLinear Regression as a ClassifierExercise 36: Linear Regression as a ClassifierLogistic RegressionExercise 37: Logistic Regression as a Classifier – Two-Class ClassifierExercise 38: Logistic Regression – Multiclass ClassifierActivity 11: Linear Regression Classifier – Two-Class ClassifierActivity 12: Iris Classification Using Logistic RegressionClassification Using K-Nearest NeighborsExercise 39: K-NN ClassificationExercise 40: Visualizing K-NN BoundariesActivity 13: K-NN Multiclass ClassifierClassification Using Decision TreesExercise 41: ID3 ClassificationExercise 42: Iris Classification Using a CART Decision TreeSummary
Chapter 5
Ensemble ModelingIntroductionExercise 43: Importing Modules and Preparing the DatasetOverfitting and UnderfittingUnderfittingOverfittingOvercoming the Problem of Underfitting and OverfittingBaggingBootstrappingBootstrap AggregationExercise 44: Using the Bagging ClassifierRandom ForestExercise 45: Building the Ensemble Model Using Random ForestBoostingAdaptive BoostingExercise 46: Adaptive BoostingGradient BoostingExercise 47: GradientBoostingClassifierStackingExercise 48: Building a Stacked ModelActivity 14: Stacking with Standalone and Ensemble AlgorithmsSummary
Chapter 6
Model EvaluationIntroductionExercise 49: Importing the Modules and Preparing Our DatasetEvaluation MetricsRegressionExercise 50: Regression MetricsClassificationExercise 51: Classification MetricsSplitting the DatasetHold-out DataK-Fold Cross-ValidationSamplingExercise 52: K-Fold Cross-Validation with Stratified SamplingPerformance Improvement TacticsVariation in Train and Test ErrorHyperparameter TuningExercise 53: Hyperparameter Tuning with Random SearchFeature ImportanceExercise 54: Feature Importance Using Random ForestActivity 15: Final Test ProjectSummary
Appendix
Chapter 1: Python Machine Learning ToolkitActivity 1: pandas FunctionsChapter 2: Exploratory Data Analysis and VisualizationActivity 2: Summary Statistics and Missing ValuesActivity 3: Visually Representing the Distribution of ValuesActivity 4: Relationships Within the DataChapter 3: Regression AnalysisActivity 5: Plotting Data with a Moving AverageActivity 6: Linear Regression Using the Least Squares MethodActivity 7: Dummy VariablesActivity 8: Other Model Types with Linear RegressionActivity 9: Gradient DescentActivity 10: AutoregressorsChapter 4: ClassificationActivity 11: Linear Regression Classifier – Two-Class ClassifierActivity 12: Iris Classification Using Logistic RegressionActivity 13: K-NN Multiclass ClassifierChapter 5: Ensemble ModelingActivity 14: Stacking with Standalone and Ensemble AlgorithmsChapter 6: Model EvaluationActivity 15: Final Test Project

Content preview from Applied Supervised Learning with Python

Preface

About

This section briefly introduces the authors, what this book covers, the technical skills you'll need to get started, and the hardware and software requirements required to complete all of the included activities and exercises.

About the Book

Machine learning—the ability of a machine to give correct answers based on input data—has revolutionized the way we do business. Applied Supervised Learning with Python provides a rich understanding of how you can apply machine learning techniques to your data science projects using Python. You'll explore Jupyter notebooks, a technology that's widely used in academic and commercial circles with support for running inline code.

With the help of fun examples, you'll gain experience working ...

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Applied Unsupervised Learning with Python

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