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Machine Learning with Python for Everyone, 1/e


Machine Learning with Python for Everyone, 1/e
Author(s)  Simy Joy ,Payal Anand ,Priya Nair Rajeev
ISBN  9789353944902
Imprint  Pearson Education
Copyright  2020
Pages  504
Binding  Paperback
List Price  Rs. 860.00
  
 
 

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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