Hands-on Machine Learning With Scikit-learn And Tensorflow - Info and Reading Options

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Cover -- Copyright -- Table of Contents -- Preface -- The Machine Learning Tsunami -- Machine Learning in Your Projects -- Objective and Approach -- Prerequisites -- Roadmap -- Other Resources -- Conventions Used in This Book -- Using Code Examples -- O'Reilly Safari -- How to Contact Us -- Acknowledgments -- Part I. The Fundamentals of Machine Learning -- Chapter 1. The Machine Learning Landscape -- What Is Machine Learning? -- Why Use Machine Learning? -- Types of Machine Learning Systems -- Supervised/Unsupervised Learning -- Batch and Online Learning -- Instance-Based Versus Model-Based Learning -- Main Challenges of Machine Learning -- Insufficient Quantity of Training Data -- Nonrepresentative Training Data -- Poor-Quality Data -- Irrelevant Features -- Overfitting the Training Data -- Underfitting the Training Data -- Stepping Back -- Testing and Validating -- Exercises -- Chapter 2. End-to-End Machine Learning Project -- Working with Real Data -- Look at the Big Picture -- Frame the Problem -- Select a Performance Measure -- Check the Assumptions -- Get the Data -- Create the Workspace -- Download the Data -- Take a Quick Look at the Data Structure -- Create a Test Set -- Discover and Visualize the Data to Gain Insights -- Visualizing Geographical Data -- Looking for Correlations -- Experimenting with Attribute Combinations -- Prepare the Data for Machine Learning Algorithms -- Data Cleaning -- Handling Text and Categorical Attributes -- Custom Transformers -- Feature Scaling -- Transformation Pipelines -- Select and Train a Model -- Training and Evaluating on the Training Set -- Better Evaluation Using Cross-Validation -- Fine-Tune Your Model -- Grid Search -- Randomized Search -- Ensemble Methods -- Analyze the Best Models and Their Errors -- Evaluate Your System on the Test Set -- Launch, Monitor, and Maintain Your System

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