Description: 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 learningAdvantages and shortcomings of widely used machine learning algorithmsHow to represent data processed by machine learning, including which data aspects to focus onAdvanced methods for model evaluation and parameter tuningThe concept of pipelines for chaining models and encapsulating your workflowMethods for working with text data, including text-specific processing techniquesSuggestions for improving your machine learning and data science skills.
Price: 48 USD
Location: Delcevo
End Time: 2024-11-17T17:56:19.000Z
Shipping Cost: 14 USD
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Restocking Fee: No
Return shipping will be paid by: Buyer
All returns accepted: Returns Accepted
Item must be returned within: 30 Days
Refund will be given as: Money Back
Item Length: 9.2 in
Item Height: 0.9 in
Item Width: 7 in
Author: Andreas Müller, Sarah Guido
Publication Name: Introduction to Machine Learning with Python : a Guide for Data Scientists
Format: Trade Paperback
Language: English
Subject: Programming / Algorithms, Natural Language Processing, Programming Languages / Python
Publisher: O'reilly Media, Incorporated
Publication Year: 2016
Type: Textbook
Subject Area: Computers
Item Weight: 24.3 Oz
Number of Pages: 398 Pages