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Machine Learning with Python for Everyone

Machine Learning with Python for Everyone

Author(s):
  • Mark Fenner
  • Author: Mark Fenner
    • ISBN:9789353944902
    • 10 Digit ISBN:9353944902
    • Price:Rs. 820.00
    • Pages:504
    • Imprint:Pearson Education
    • Binding:Paperback
    • Status:Available


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    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."

     

    Table of Content

    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

    Salient Features

    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