Python Machine Learning

Douban
Python Machine Learning

Accedi o registrati per recensire o aggiungere questo elemento alla tua collezione.

ISBN: 9781783555130
Autore: Sebastian Raschka
Casa editrice: Packt Publishing - ebooks Account
data di pubblicazione: 2015 -9
Formato: Paperback
Prezzo: USD 44.99
Numero di pagine: 454

/ 10

3 valutazioni

Non ci sono abbastanza valutazioni
Prendi in prestito oppure Acquista

Sebastian Raschka   

Sinossi

About This Book
Leverage Python' s most powerful open-source 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
Who This Book Is For
If you want to find out how to use Python to start answering critical questions of your data, pick up Python Machine Learning – whether you want to get started from scratch or want to extend your data science knowledge, this is an essential and unmissable resource.
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
Style and approach
Python Machine Learning connects the fundamental theoretical principles behind machine learning to their practical application in a way that focuses you on asking and answering the right questions. It walks you through the key elements of Python and its powerful machine learning libraries, while demonstrating how to get to grips with a range of statistical models.

Altre edizioni
Commenti
Recensioni
笔记