Introduction to Machine Learning with Python: A guide for data scientists
By: Muller, Andreas C.
Contributor(s): Guido, Sarah.
Material type:
BookPublisher: Mumbai Shroff Publishers & Distributors Pvt. Ltd. 2018Description: 378.ISBN: 978-93-5213-457-1.Subject(s): Computer Science | PythonDDC classification: 005.133 Summary: 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
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BSDU Knowledge Resource Center, Jaipur | 005.133 MUL (Browse shelf) | Available | 017668 | ||
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BSDU Knowledge Resource Center, Jaipur | Reference | 005.133 MUL (Browse shelf) | Not For Loan | 017669 |
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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.
Contents:
Introduction
Supervised Learning
Unsupervised Learning and Preprocessing
Representing Data and Engineering Features
Model Evaluation and Improvement
Algorithm Chains and Pipelines
Working with Text Data
Wrapping Up
Index
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
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