Machine Learning with Python for Everyone / Mark Fenner.

By:

Fenner, Mark

Material type: Text

TextLanguage: English Publication details: India : Pearson India Educational Services Pvt Ltd, 2022.Edition: 1st edDescription: xxvi, 473 p.: ill. ; 23 cmISBN:

9789353944902
9353944902

Uniform titles:

Machine Learning with Python for Everyone

Subject(s):

DDC classification:

23 006.31 FEN

Contents:

Forward Preface About the Author I First Steps 1. Introduction 2. Predicting Categories 3. Predicting Numerical Values: Getting Started with Regression II Evaluation 4. Evaluating 5. Evaluating Classifiers 6.Evaluating Regressors III More Methods and Fundamentals 7. More Classification Methods 8. More Regression methods 9. Manual Feature Engineering: manipulating Data For Fun and Profit 10. Models that Engineer features of us 11. Features Engineering for Domains: Domain Specific Learning

Summary: Students are crushing to master powerful machine learning techniques for improving decision-making and scaling analysis to immense datasets. Machine learning with Python for everyone brings together all they'll need to succeed: a practical understanding of the machine learning process, accessible code, skills for implementing that process with Python and the scikit-learn library, and real expertise in using learning systems intelligently.Reflecting 20 years of experience teaching non-specialists, the author teaches through carefully-crafted datasets that are complex enough to be interesting, but simple enough for non-specialists. Building on this foundation, the book presents real-world case studies that apply his lessons in detailed, nuanced ways. Throughout, he offers clear narratives, practical “code-alongs,” and easy-to-understand images -- focusing on Mathematics only where it’s necessary to make connection and deepen insight. table of Contents: Chapter 1: Let’s discuss learning Chapter 2: predicting categories: getting started with classification Chapter 3: predicting numerical values: getting started with regression Chapter 4: evaluating and comparing learners Chapter 5: evaluating classifiers Chapter 6: evaluating Regressors Chapter 7: more classification methods Chapter 8: more regression methods Chapter 9: manual feature engineering: manipulating data for fun and Profit Chapter 10: models that engineer features for us Chapter 11: feature engineering for domains: domain-specific learning online chapters Chapter 12: tuning hyperparameters and pipelines Chapter 13: combining learners Chapter 14: connecting, extensions, and further directions

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Holdings
Item type	Current library	Collection	Call number	Status	Date due	Barcode
Text Books	CUTN Central Library Generalia	Non-fiction	006.31 FEN (Browse shelf(Opens below))	Available		47796

Forward
Preface
About the Author
I First Steps
1. Introduction
2. Predicting Categories
3. Predicting Numerical Values: Getting Started with Regression

II Evaluation
4. Evaluating
5. Evaluating Classifiers
6.Evaluating Regressors

III More Methods and Fundamentals
7. More Classification Methods
8. More Regression methods
9. Manual Feature Engineering: manipulating Data For Fun and Profit
10. Models that Engineer features of us
11. Features Engineering for Domains: Domain Specific Learning

Students are crushing to master powerful machine learning techniques for improving decision-making and scaling analysis to immense datasets. Machine learning with Python for everyone brings together all they'll need to succeed: a practical understanding of the machine learning process, accessible code, skills for implementing that process with Python and the scikit-learn library, and real expertise in using learning systems intelligently.Reflecting 20 years of experience teaching non-specialists, the author teaches through carefully-crafted datasets that are complex enough to be interesting, but simple enough for non-specialists. Building on this foundation, the book presents real-world case studies that apply his lessons in detailed, nuanced ways. Throughout, he offers clear narratives, practical “code-alongs,” and easy-to-understand images -- focusing on Mathematics only where it’s necessary to make connection and deepen insight. table of Contents: Chapter 1: Let’s discuss learning Chapter 2: predicting categories: getting started with classification Chapter 3: predicting numerical values: getting started with regression Chapter 4: evaluating and comparing learners Chapter 5: evaluating classifiers Chapter 6: evaluating Regressors Chapter 7: more classification methods Chapter 8: more regression methods Chapter 9: manual feature engineering: manipulating data for fun and Profit Chapter 10: models that engineer features for us Chapter 11: feature engineering for domains: domain-specific learning online chapters Chapter 12: tuning hyperparameters and pipelines Chapter 13: combining learners Chapter 14: connecting, extensions, and further directions

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