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Mastering Machine Learning with Python in Six Steps : A Practical Implementation Guide to Predictive Data Analytics Using Python - Second Edition

By: (Author) Manohar Swamynathan

Extended Catalogue

Ksh 9,900.00

Format: Paperback or Softback

ISBN-10: 1484249461

ISBN-13: 9781484249468

Edition: Second Edition

Publisher: APress

Imprint: APress

Country of Manufacture: US

Country of Publication: GB

Publication Date: Oct 2nd, 2019

Print length: 457 Pages

Weight: 870 grams

Dimensions (height x width x thickness): 24.70 x 25.50 x 2.70 cms

Product Classification: Computing & information technology
Databases
Artificial intelligence

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  • Description

  • Reviews

Explore fundamental to advanced Python 3 topics in six steps, all designed to make you a worthy practitioner. This updated version’s approach is based on the “six degrees of separation” theory, which states that everyone and everything is a maximum of six steps away and presents each topic in two parts: theoretical concepts and practical implementation using suitable Python 3 packages. You’ll start with the fundamentals of Python 3 programming language, machine learning history, evolution, and the system development frameworks. Key data mining/analysis concepts, such as exploratory analysis, feature dimension reduction, regressions, time series forecasting and their efficient implementation in Scikit-learn are covered as well. You’ll also learn commonly used model diagnostic and tuning techniques. These include optimal probability cutoff point for class creation, variance, bias, bagging, boosting, ensemble voting, grid search, random search, Bayesian optimization, and the noise reduction technique for IoT data.  Finally, you’ll review advanced text mining techniques, recommender systems, neural networks, deep learning, reinforcement learning techniques and their implementation. All the code presented in the book will be available in the form of iPython notebooks to enable you to try out these examples and extend them to your advantage. What You'll LearnUnderstand machine learning development and frameworksAssess model diagnosis and tuning in machine learningExamine text mining, natuarl language processing (NLP), and recommender systemsReview reinforcement learning and CNNWho This Book Is ForPython developers, data engineers, and machine learning engineers looking to expand their knowledge or career into machine learning area.

Explore fundamental to advanced Python 3 topics in six steps, all designed to make you a worthy practitioner. This updated version''s approach is based on the "six degrees of separation" theory, which states that everyone and everything is a maximum of six steps away and presents each topic in two parts: theoretical concepts and practical implementation using suitable Python 3 packages.

You''ll start with the fundamentals of Python 3 programming language, machine learning history, evolution, and the system development frameworks. Key data mining/analysis concepts, such as exploratory analysis, feature dimension reduction, regressions, time series forecasting and their efficient implementation in Scikit-learn are covered as well. You''ll also learn commonly used model diagnostic and tuning techniques. These include optimal probability cutoff point for class creation, variance, bias, bagging, boosting, ensemble voting, grid search, random search, Bayesian optimization, and the noise reduction technique for IoT data. 

Finally, you''ll review advanced text mining techniques, recommender systems, neural networks, deep learning, reinforcement learning techniques and their implementation. All the code presented in the book will be available in the form of iPython notebooks to enable you to try out these examples and extend them to your advantage.

What You''ll Learn

  • Understand machine learning development and frameworks
  • Assess model diagnosis and tuning in machine learning
  • Examine text mining, natuarl language processing (NLP), and recommender systems
  • Review reinforcement learning and CNN

Who This Book Is For

Python developers, data engineers, and machine learning engineers looking to expand their knowledge or career into machine learning area.


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