Python machine learning: Machine learning and deep learning with Python, scikit-learn, and TensorFlow
Unlock modern machine learning and deep learning techniques with Python by using the latest cutting-edge open source Python libraries. About This Book * Second edition of the bestselling book on Machine Learning * A practical approach to key frameworks in data science, machine learning, and deep learning * Use the most powerful Python libraries to implement machine learning and deep learning * Get to know the best practices to improve and optimize your machine learning systems and algorithms Wh…
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- Full title :Python machine learning : Machine learning and deep learning with Python, scikit-learn, and TensorFlow
Summary
Unlock modern machine learning and deep learning techniques with Python by using the latest cutting-edge open source Python libraries. About This Book * Second edition of the bestselling book on Machine Learning * A practical approach to key frameworks in data science, machine learning, and deep learning * Use the most powerful Python libraries to implement machine learning and deep learning * Get to know the best practices to improve and optimize your machine learning systems and algorithms Who This Book Is For If you know some Python and you want to use machine learning and deep learning, pick up this book. Whether you want to start from scratch or extend your machine learning knowledge, this is an essential and unmissable resource. Written for developers and data scientists who want to create practical machine learning and deep learning code, this book is ideal for developers and data scientists who want to teach computers how to learn from data.
What You Will Learn * Understand the key frameworks in data science, machine learning, and deep learning * Harness the power of the latest Python open source libraries in machine learning * Explore machine learning techniques using challenging real-world data * Master deep neural network implementation using the TensorFlow library * Learn the mechanics of classification algorithms to implement the best tool for the job * Predict continuous target outcomes using regression analysis * Uncover hidden patterns and structures in data with clustering * Delve deeper into textual and social media data using sentiment analysis In Detail Machine learning is eating the software world, and now deep learning is extending machine learning. Understand and work at the cutting edge of machine learning, neural networks, and deep learning with this second edition of Sebastian Raschka's bestselling book, Python Machine Learning.
Thoroughly updated using the latest Python open source libraries, this book offers the practical knowledge and techniques you need to create and contribute to machine learning, deep learning, and modern data analysis. Fully extended and modernized, Python Machine Learning Second Edition now includes the popular TensorFlow deep learning library. The scikit-learn code has also been fully updated to include recent improvements and additions to this versatile machine learning library. Sebastian Raschka and Vahid Mirjalili's unique insight and expertise introduce you to machine learning and deep learning algorithms from scratch, and show you how to apply them to practical industry challenges using realistic and interesting examples. By the end of the book, you'll be ready to meet the new data analysis opportunities in today's world. If you've read the first edition of this book, you'll be delighted to find a new balance of classical ideas and modern insights into machine learning.
Every chapter has been critically updated, and there are new chapters on key technologies. You'll be able to learn and work with TensorFlow more deeply than ever before, and get essential coverage of the Keras neural network library, along with the most recent updates to scikit-learn. Style and Approach Python Machine Learning Second Edition takes a practical, hands-on coding approach so you can learn about machine learning by coding with Python. This book moves fluently between the theoretical principles of machine learning and the practical details of implementation with Python
Edition Statements
- Publication Date:2017
- Edition:2. ed. - Fully revised and updated
Publication Statements
- Publisher(s):Packt Publishing
- Place of Publication:Birmingham
- Language:eng
- Publication Date:2017
Notes
Includes index
The Physical Object
- Description:595 s.
Edition Identifiers
- ISBN:9781787125933, 1787125939
- oclc:on1091602387
Table of Contents
Table of Contents Giving Computers the Ability to Learn from DataTraining Machine Learning Algorithms the Ability to Learn from DataA Tour of Machine Learning Classifiers Using ScikitBuilding Good Training Sets -- Data PreprocessingCompressing Data via Dimensionality ReductionLearning Best Practices for Model Evaluation and Hyperparameter TuningCombining Different Models for Ensemble LearningApplying Machine Learning to Sentiment AnalysisEmbedding a Machine Learning Model into a Web ApplicationPredicting Continuous Target VariablesWorking with Unlabeled Data -- Clustering AnalysisImplementing a Multilayer Artificial Neural Network from ScratchParallelizing Neural Network Training with TensorFlowGoing Deeper: The Mechanics of TensorFlowClassifying Images with Deep Convolutional Neural NetworksModeling Sequential Data using Recurrent Neural Networks.
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