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Introduction to Machine Learning with Python : A Guide for Data Scientists by Sarah Guido and Andreas Müller (2016, Trade Paperback)

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About this product

Product Identifiers

Publisher
O'reilly Media, Incorporated
ISBN-10
1449369413
ISBN-13
9781449369415
eBay Product ID (ePID)
207764802

Product Key Features

Number of Pages
392 Pages
Publication Name
Introduction to Machine Learning with Python : a Guide for Data Scientists
Language
English
Publication Year
2016
Subject
Programming / Algorithms, Natural Language Processing, Programming Languages / Python
Type
Textbook
Subject Area
Computers
Author
Sarah Guido, Andreas Müller
Format
Trade Paperback

Dimensions

Item Height
0.8 in
Item Weight
25 Oz
Item Length
9.2 in
Item Width
7 in

Additional Product Features

Intended Audience
Scholarly & Professional
LCCN
2017-394288
Dewey Edition
23
Illustrated
Yes
Dewey Decimal
006.31
Synopsis
Machine learning has become an integral part of many commercial applications and research projects, but this field is not exclusive to large companies with extensive research teams. If you use Python, even as a beginner, this book will teach you practical ways to build your own machine learning solutions. With all the data available today, machine learning applications are limited only by your imagination. You'll learn the steps necessary to create a successful machine-learning application with Python and the scikit-learn library. Authors Andreas M ller and Sarah Guido focus on the practical aspects of using machine learning algorithms, rather than the math behind them. Familiarity with the NumPy and matplotlib libraries will help you get even more from this book. With this book, you'll learn: Fundamental concepts and applications of machine learning Advantages and shortcomings of widely used machine learning algorithms How to represent data processed by machine learning, including which data aspects to focus on Advanced methods for model evaluation and parameter tuning The concept of pipelines for chaining models and encapsulating your workflow Methods for working with text data, including text-specific processing techniques Suggestions for improving your machine learning and data science skills, Machine learning has become an integral part of many commercial applications and research projects, but this field is not exclusive to large companies with extensive research teams. If you use Python, even as a beginner, this book will teach you practical ways to build your own machine learning solutions. With all the data available today, machine learning applications are limited only by your imagination. You'??ll learn the steps necessary to create a successful machine-learning application with Python and the scikit-learn library. Authors Andreas Müller and Sarah Guido focus on the practical aspects of using machine learning algorithms, rather than the math behind them. Familiarity with the NumPy and matplotlib libraries will help you get even more from this book. With this book, you'??ll learn: Fundamental concepts and applications of machine learning Advantages and shortcomings of widely used machine learning algorithms How to represent data processed by machine learning, including which data aspects to focus on Advanced methods for model evaluation and parameter tuning The concept of pipelines for chaining models and encapsulating your workflow Methods for working with text data, including text-specific processing techniques Suggestions for improving your machine learning and data science skills
LC Classification Number
Q325.5

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5 out of 5 stars
by gaffig4_64Aug 25, 2020
Beginners Machine Learning book
Best elementary book for Data Science and Machine Learning
Verified purchase: Yes | Condition: Pre-owned
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Excelente
Texto claro y del nivel requerido, que cumple con las expectativas esperadas.
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