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Python: Real-World Data Science

You're reading from   Python: Real-World Data Science Real-World Data Science

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Product type Course
Published in Jun 2016
Publisher
ISBN-13 9781786465160
Length 1255 pages
Edition 1st Edition
Languages
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Authors (5):
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Fabrizio Romano Fabrizio Romano
Author Profile Icon Fabrizio Romano
Fabrizio Romano
Phuong Vo.T.H Phuong Vo.T.H
Author Profile Icon Phuong Vo.T.H
Phuong Vo.T.H
Robert Layton Robert Layton
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Robert Layton
Sebastian Raschka Sebastian Raschka
Author Profile Icon Sebastian Raschka
Sebastian Raschka
Martin Czygan Martin Czygan
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Martin Czygan
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Table of Contents (12) Chapters Close

Table of Contents FREE CHAPTER
Python: Real-World Data Science
Meet Your Course Guide
What's so cool about Data Science?
Course Structure
Course Journey
The Course Roadmap and Timeline
1. Course Module 1: Python Fundamentals 2. Course Module 2: Data Analysis 3. Course Module 3: Data Mining 4. Course Module 4: Machine Learning Index

Chapter 3. A Tour of Machine Learning Classifiers Using scikit-learn

In this chapter, we will take a tour through a selection of popular and powerful machine learning algorithms that are commonly used in academia as well as in the industry. While learning about the differences between several supervised learning algorithms for classification, we will also develop an intuitive appreciation of their individual strengths and weaknesses. Also, we will take our first steps with the scikit-learn library, which offers a user-friendly interface for using those algorithms efficiently and productively.

The topics that we will learn about throughout this chapter are as follows:

  • Introduction to the concepts of popular classification algorithms
  • Using the scikit-learn machine learning library
  • Questions to ask when selecting a machine learning algorithm

Choosing a classification algorithm

Choosing an appropriate classification algorithm for a particular problem task requires practice: each algorithm...

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