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Mastering machine learning with Pyth...

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Mastering machine learning with Python in six stepsa practical implementation guide to predictive data analytics using Python /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Mastering machine learning with Python in six stepsby Manohar Swamynathan.
其他題名:	a practical implementation guide to predictive data analytics using Python /
作者:	Swamynathan, Manohar.
出版者:	Berkeley, CA :Apress :2019.
面頁冊數:	xvii, 455 p. :ill., digital ;24 cm.
Contained By:	Springer eBooks
標題:	Machine learning.
電子資源:	https://doi.org/10.1007/978-1-4842-4947-5
ISBN:	9781484249475$q(electronic bk.)

Mastering machine learning with Python in six stepsa practical implementation guide to predictive data analytics using Python /
Swamynathan, Manohar.

Mastering machine learning with Python in six stepsa practical implementation guide to predictive data analytics using Python /[electronic resource] :by Manohar Swamynathan. - 2nd ed. - Berkeley, CA :Apress :2019. - xvii, 455 p. :ill., digital ;24 cm.

Chapter 1: Step 1 - Getting Started with Python -- Chapter 2 : Step 2 - Introduction to Machine Learning -- Chapter 3: Step 3 - Fundamentals of Machine Learning -- Chapter 4: Step 4 - Model Diagnosis and Tuning -- Chapter 5: Step 5 - Text Mining, NLP AND Recommender Systems -- Chapter 6: Step 6 - Deep and Reinforcement Learning -- Chapter 7 : Conclusion.

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.

ISBN: 9781484249475$q(electronic bk.)

Standard No.: 10.1007/978-1-4842-4947-5doiSubjects--Topical Terms:

188639
Machine learning.

LC Class. No.: QA76.73.P98 / S936 2019

Dewey Class. No.: 005.133

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