Hands-on machine learning with Scikit-Learn, Keras, and TensorFlow [electronic resource] : concepts, tools, and techniques to build intelligent systems / Aurélien Géron.

Includes bibliographical references and index.

Intro; Copyright; Table of Contents; Preface; The Machine Learning Tsunami; Machine Learning in Your Projects; Objective and Approach; Prerequisites; Roadmap; Changes in the Second Edition; Other Resources; Conventions Used in This Book; Code Examples; Using Code Examples; O'Reilly Online Learning; 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?; Examples of Applications; Types of Machine Learning Systems; Supervised/Unsupervised Learning; Batch and Online Learning.

Instance-Based Versus Model-Based LearningMain 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; Hyperparameter Tuning and Model Selection; Data Mismatch; 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 StructureCreate 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 ErrorsEvaluate Your System on the Test Set; Launch, Monitor, and Maintain Your System; Try It Out!; Exercises; Chapter 3. Classification; MNIST; Training a Binary Classifier; Performance Measures; Measuring Accuracy Using Cross-Validation; Confusion Matrix; Precision and Recall; Precision/Recall Trade-off; The ROC Curve; Multiclass Classification; Error Analysis; Multilabel Classification; Multioutput Classification; Exercises; Chapter 4. Training Models; Linear Regression; The Normal Equation; Computational Complexity; Gradient Descent.

Batch Gradient DescentStochastic Gradient Descent; Mini-batch Gradient Descent; Polynomial Regression; Learning Curves; Regularized Linear Models; Ridge Regression; Lasso Regression; Elastic Net; Early Stopping; Logistic Regression; Estimating Probabilities; Training and Cost Function; Decision Boundaries; Softmax Regression; Exercises; Chapter 5. Support Vector Machines; Linear SVM Classification; Soft Margin Classification; Nonlinear SVM Classification; Polynomial Kernel; Similarity Features; Gaussian RBF Kernel; Computational Complexity; SVM Regression; Under the Hood.

Online version restricted to NUS staff and students only through NUSNET.

Through a series of recent breakthroughs, deep learning has boosted the entire field of machine learning. Now, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data. The updated edition of this best-selling book uses concrete examples, minimal theory, and two production-ready Python frameworks-Scikit-Learn and TensorFlow 2-to help you gain an intuitive understanding of the concepts and tools for building intelligent systems. Practitioners will learn a range of techniques that they can quickly put to use on the job. Part 1 employs Scikit-Learn to introduce fundamental machine learning tasks, such as simple linear regression. Part 2, which has been significantly updated, employs Keras and TensorFlow 2 to guide the reader through more advanced machine learning methods using deep neural networks. With exercises in each chapter to help you apply what you've learned, all you need is programming experience to get started. NEW FOR THE SECOND EDITION:Updated all code to TensorFlow 2Introduced the high-level Keras APINew and expanded coverage including TensorFlow's Data API, Eager Execution, Estimators API, deploying on Google Cloud ML, handling time series, embeddings and more With Early Release ebooks, you get books in their earliest form-the author's raw and unedited content as he or she writes-so you can take advantage of these technologies long before the official release of these titles. You'll also receive updates when significant changes are made, new chapters are available, and the final ebook bundle is released.

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Description based on online resource; title from resource home page (ProQuest Ebook Central, viewed January 16, 2020).