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© 2017

Python Machine Learning Case Studies

Five Case Studies for the Data Scientist

Book

Table of contents

  1. Front Matter
    Pages i-xvii
  2. Danish Haroon
    Pages 1-43
  3. Danish Haroon
    Pages 45-94
  4. Danish Haroon
    Pages 95-128
  5. Danish Haroon
    Pages 129-160
  6. Danish Haroon
    Pages 161-196
  7. Back Matter
    Pages 197-204

About this book

Introduction

Embrace machine learning approaches and Python to enable automatic rendering of rich insights. The book uses a hands-on case study-based approach to crack real-world applications to which machine learning concepts can be applied. These smarter machines will enable your business processes to achieve efficiencies on minimal time and resources.

Python Machine Learning Case Study takes you through the steps to improve business processes and determine the pivotal points that frame strategies. You’ll see machine learning techniques that you can use to support your products and services. Moreover you’ll learn the pros and cons of each of the machine learning concepts presented.

By taking a step-by-step approach to coding in Python you’ll be able to understand the rationale behind model selection and decisions within the machine learning process. The book is equipped with practical examples along with code snippets to ensure th
at you understand the data science approach to solving real-world problems.

You will:
  • Gain insights into machine learning concepts 
  • Work on real-world applications of machine learning
  • Get a hands-on overview to Python from a machine learning point of view

Keywords

Machine Leraning Python Regression Time Series Modelling Data Analysys Clustering Bagging

Authors and affiliations

  1. 1.KarachiPakistan

About the authors

Danish Haroon currently leads the Data Sciences team at Market IQ Inc, a patented predictive analytics platform focused on providing actionable, real-time intelligence, culled from sentiment inflection points. He received his MBA from Karachi School for Business and Leadership, having served corporate clients and their data analytics requirements. Most recently, he led the data commercialization team at PredictifyME, a startup focused on providing predictive analytics for demand planning and real estate markets in the US market. His current research focuses on the amalgam of data sciences for improved customer experiences (CX).

Bibliographic information

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