Machine Learning With Python For Everyone, 1/E at Meripustak

Machine Learning With Python For Everyone, 1/E

Books from same Author: Mark Fenner

Books from same Publisher: PEARSON INDIA

Related Category: Author List / Publisher List


  • Retail Price: ₹ 860/- [ 5.00% off ]

    Seller Price: ₹ 817

Sold By: T K Pandey      Click for Bulk Order

Offer 1: Get ₹ 111 extra discount on minimum ₹ 500 [Use Code: Bharat]

Offer 2: Get 5.00 % + Flat ₹ 100 discount on shopping of ₹ 1500 [Use Code: IND100]

Offer 3: Get 5.00 % + Flat ₹ 300 discount on shopping of ₹ 5000 [Use Code: MPSTK300]

Free Shipping (for orders above ₹ 499) *T&C apply.

In Stock

Free Shipping Available



Click for International Orders
  • Provide Fastest Delivery

  • 100% Original Guaranteed
  • General Information  
    Author(s)Mark Fenner
    PublisherPEARSON INDIA
    Edition1st Edition
    ISBN9789353944902
    Pages504
    BindingPaperback
    LanguageEnglish
    Publish YearApril 2020

    Description

    PEARSON INDIA Machine Learning With Python For Everyone, 1/E by Mark Fenner

    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