國立虎尾科技大學 |

Mastering Machine Learning with Python in Six Steps = A Practical Implementation Guide to Predictive Data Analytics Using Python /

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Mastering Machine Learning with Python in Six Steps/ by Manohar Swamynathan.
其他題名:	A Practical Implementation Guide to Predictive Data Analytics Using Python /
作者:	Swamynathan, Manohar.
面頁冊數:	XVII, 457 p. 185 illus., 1 illus. in color.online resource. :
Contained By:	Springer Nature eBook
標題:	Artificial intelligence. -
電子資源:	https://doi.org/10.1007/978-1-4842-4947-5
ISBN:	9781484249475

Mastering Machine Learning with Python in Six Steps = A 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. 2019. - XVII, 457 p. 185 illus., 1 illus. in color.online resource.

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

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

559380
Artificial intelligence.

LC Class. No.: Q334-342

Dewey Class. No.: 006.3

Mastering Machine Learning with Python in Six Steps = A Practical Implementation Guide to Predictive Data Analytics Using Python /
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