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Introduction to Machine Learning with Python: A Guide for Data Scientists – CyberWar

Introduction to Machine Learning with Python: A Guide for Data  #Cybersecurity #CyberAttack

  • 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.
  • Authors Andreas Müller and Sarah Guido focus on the practical aspects of using machine learning algorithms, rather than the math behind them.
  • 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

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.

@CyberDomain: Introduction to Machine Learning with Python: A Guide for Data #Cybersecurity #CyberAttack

Amazon Price: $49.99 $39.06 You save: $10.93 (22%). (as of September 11, 2017 04:44 – Details). Product prices and availability are accurate as of the date/time indicated and are subject to change. Any price and availability information displayed on the Amazon site at the time of purchase will apply to the purchase of this product.

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

Introduction to Machine Learning with Python: A Guide for Data Scientists – CyberWar

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