Python Machine Learning

Python Machine Learning
by 作者: Sebastian Raschka
ISBN-10 书号: 1783555130
ISBN-13 书号: 9781783555130
Publisher Finelybook 出版日期: September 1,2015
Pages: 454
Publisher Finelybook 出版社: Packt
Description
More Information
Learn
Find out how different machine learning can be used to ask different data analysis questions
Learn how to build neural networks using Python libraries and tools such as Keras and Theano
Write clean and elegant Python code to optimize the strength of your machine learning 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
Learn sentiment analysis to delve deeper into textual and social media data
About
Machine learning is transforming the way businesses operate. Being able to understand trends and patterns in complex data is critical to success; it is today 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 machine learning 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.
Features
Leverage Python’s most powerful machine learning libraries for deep learning,data wrangling,and data visualization
Learn effective strategies and best practices to improve and optimize machine learning systems and algorithms
Ask – and answer – tough questions of your data with robust statistical models,built for a range of datasets
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