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Machine Learning with Python for Everyone, 1/e


Machine Learning with Python for Everyone, 1/e
Author(s)  Mark Fenner
ISBN  9789353944902
Imprint  Pearson Education
Copyright  2020
Pages  504
Binding  Paperback
List Price  Rs. 820.00
  
 
 

Students are rushing 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 &ldquocode-alongs," and easy-to-understand images focusing on mathematics only where it's necessary to make connections and deepen insight."
 

  • About the Author
  • Contents
  • Features
  • Downloadable Resources

Dr. Mark Fenner, owner of Fenner Training and Consulting, LLC, has taught computing and mathematics to diverse adult audiences since 1999, and holds a PhD in computer science. His research has included design, implementation, and performance of machine learning and numerical algorithms; developing learning systems to detect user anomalies; and probabilistic modeling of protein function.


 

 

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: Connections, Extensions, and Further Directions

 

1. Covers whatever learners need to succeed in data science with Python: process, code, and implementation


2. Enables learners to understand the machine learning process, leverage the powerful Python scikit-learn library, and master the algorithmic components of learning systems


3. Integrates clear narrative, carefully designed Python code, images, and interesting, intelligible datasets

 
Instructor Resources
 


 
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