Titre: Python Machine Learning –
Nom de fichier: python-machine-learning.pdf
Date de sortie: September 20, 2017
Broché: 595 pages
Auteur: Sebastian Raschka, Vahid Mirjalili
Key Features A practical approach to the frameworks of data science, machine learning, and deep learning
Use the most powerful Python libraries to implement machine learning and deep learning
Learn best practices to improve and optimize your machine learning systems and algorithms
Machine learning is eating the software world, and now deep learning is extending machine learning. This book is for developers and data scientists who want to master the world of artificial intelligence, with a practical approach to understanding and implementing machine learning, and how to apply the power of deep learning with Python.
This Second Edition of Sebastian Raschka’s Python Machine Learning is thoroughly updated to use the most powerful and modern Python open-source libraries, so that you can understand and work at the cutting-edge of machine learning, neural networks, and deep learning.
Written for developers and data scientists who want to create practical machine learning code, the authors have extended and modernized this best-selling book, to now include the influential TensorFlow library, and the Keras Python neural network library. The Scikit-learn code has also been fully updated to include recent innovations. The result is a new edition of this classic book at the cutting edge of machine learning.
Readers new to machine learning will find this classic book offers the practical knowledge and rich techniques they need to create and contribute to machine learning, deep learning, and modern data analysis. Raschka and Mirjalili introduce you to machine learning and deep learning algorithms, and show you how to apply them to practical industry challenges. By the end of the book, you’ll be ready to meet the new data analysis opportunities in today’s world .
Readers of the first edition will 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. Readers can learn and work with TensorFlow more deeply than ever before, and essential coverage of the Keras neural network library has been added, along with the most recent updates to Scikit-learn. Raschka and Mirjalili have updated this book to meet the most modern areas of machine learning, to give developers and data scientists a fresh and practical Python journey into machine learning.
What you will learn Use the key frameworks of data science, machine learning, and deep learning
Ask new questions of your data through machine learning models and neural networks
Work with the most powerful Python open-source libraries in machine learning
Build deep learning applications using Keras and TensorFlow
Embed your machine learning model in accessible web applications
Predict continuous target outcomes using regression analysis
Uncover hidden patterns and structures in data with clustering
Analyze images using deep learning techniques
Use sentiment analysis to delve deeper into textual and social media data
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- Building Machine Learning Systems with Python
- Machine Learning in Action
- Machine Learning with R
- Python for Data Analysis
- Data Science from Scratch: First Principles with Python
- Effective Python: 59 Specific Ways to Write Better Python
- Artificial Intelligence for Humans, Volume 1: Fundamental Algorithms
- Test-Driven Web Development with Python
- Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference
- Think Complexity: Complexity Science and Computational Modeling
- Python Algorithms: Mastering Basic Algorithms in the Python Language
- Fluent Python: Clear, Concise, and Effective Programming
- Two Scoops of Django: Best Practices for Django 1.6
- The Effective Engineer: How to Leverage Your Efforts In Software Engineering to Make a Disproportionate and Meaningful Impact
- Mining the Social Web: Analyzing Data from Facebook, Twitter, LinkedIn, and Other Social Media Sites
- Pattern Classification
- Programming Collective Intelligence: Building Smart Web 2.0 Applications
- Building Data Science Teams