Sebastian Raschka - Python Machine Learning [2015, PDF/EPUB, ENG] torrent

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Python Machine Learning

Год издания: 2015
Автор: Sebastian Raschka
Жанр или тематика: Программирование

Издательство: Packt Publishing
ISBN: 9781783555130
Язык: Английский

Формат: PDF/EPUB
Качество: Издательский макет или текст (eBook)
Интерактивное оглавление: Да
Количество страниц: 454

Описание: Machine learning and predictive analytics are transforming the way businesses and other organizations operate. Being able to understand trends and patterns in complex data is critical to success, becoming one of the key strategies for unlocking growth in a challenging contemporary marketplace. Python can help you deliver key insights into your data – its unique capabilities as a language let you build sophisticated algorithms and statistical models that can reveal new perspectives and answer key questions that are vital for success.

Python Machine Learning gives you access to the world of predictive analytics and demonstrates why Python is one of the world’s leading data science languages. If you want to ask better questions of data, or need to improve and extend the capabilities of your machine learning systems, this practical data science book is invaluable. Covering a wide range of powerful Python libraries, including scikit-learn, Theano, and Keras, and featuring guidance and tips on everything from sentiment analysis to neural networks, you’ll soon be able to answer some of the most important questions facing you and your organization.

What You Will Learn

- Explore how to use different machine learning models to ask different questions of your data
- Learn how to build neural networks using Keras and Theano
- Find out how to write clean and elegant Python code that will optimize the strength of your algorithms
- Discover how to embed your machine learning model in a web application for increased accessibility
- Predict continuous target outcomes using regression analysis
- Uncover hidden patterns and structures in data with clustering
- Organize data using effective pre-processing techniques
- Get to grips with sentiment analysis to delve deeper into textual and social media data
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Table of Contents
1: Giving Computers the Ability to Learn from Data
2: Training Machine Learning Algorithms for Classification
3: A Tour of Machine Learning Classifiers Using Scikit-learn
4: Building Good Training Sets – Data Preprocessing
5: Compressing Data via Dimensionality Reduction
6: Learning Best Practices for Model Evaluation and Hyperparameter Tuning
7: Combining Different Models for Ensemble Learning
8: Applying Machine Learning to Sentiment Analysis
9: Embedding a Machine Learning Model into a Web Application
10: Predicting Continuous Target Variables with Regression Analysis
11: Working with Unlabeled Data – Clustering Analysis
12: Training Artificial Neural Networks for Image Recognition
13: Parallelizing Neural Network Training with Theano
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