Hands-on Scikit-Learn for Machine Learning Applications

1. Front Matter

   Pages i-xiii

   PDF

2. Introduction to Scikit-Learn

   • David Paper

   Pages 1-35

3. Classification from Simple Training Sets

   • David Paper

   Pages 37-69

4. Classification from Complex Training Sets

   • David Paper

   Pages 71-104

5. Predictive Modeling Through Regression

   • David Paper

   Pages 105-136

6. Scikit-Learn Classifier Tuning from Simple Training Sets

   • David Paper

   Pages 137-163

7. Scikit-Learn Classifier Tuning from Complex Training Sets

   • David Paper

   Pages 165-188

8. Scikit-Learn Regression Tuning

   • David Paper

   Pages 189-213

9. Putting It All Together

   • David Paper

   Pages 215-237

10. Back Matter

    Pages 239-242

    PDF

Aspiring data science professionals can learn the Scikit-Learn library along with the fundamentals of machine learning with this book. The book combines the Anaconda Python distribution with the popular Scikit-Learn library to demonstrate a wide range of supervised and unsupervised machine learning algorithms. Care is taken to walk you through the principles of machine learning through clear examples written in Python that you can try out and experiment with at home on your own machine.


All applied math and programming skills required to master the content are covered in this book. In-depth knowledge of object-oriented programming is not required as working and complete examples are provided and explained. Coding examples are in-depth and complex when necessary. They are also concise, accurate, and complete, and complement the machine learning concepts introduced. Working the examples helps to build the skills necessary to understand and apply complexmachine learning algorithms.


Hands-on Scikit-Learn for Machine Learning Applications is an excellent starting point for those pursuing a career in machine learning. Students of this book will learn the fundamentals that are a prerequisite to competency. Readers will be exposed to the Anaconda distribution of Python that is designed specifically for data science professionals, and will build skills in the popular Scikit-Learn library that underlies many machine learning applications in the world of Python.





What You'll Learn

• Work with simple and complex datasets common to Scikit-Learn
  
• Manipulate data into vectors and matrices for algorithmic processing
  
• Become familiar with the Anaconda distribution used in data science
• Apply machine learning with Classifiers, Regressors, and Dimensionality Reduction
  
• Tune algorithms and find the best algorithms for each dataset
  
• Load data from and save to CSV, JSON, Numpy, and Pandas formats
  

Who This Book Is For


The aspiring data scientist yearning to break into machine learning through mastering the underlying fundamentals that are sometimes skipped over in the rush to be productive. Some knowledge of object-oriented programming and very basic applied linear algebra will make learning easier, although anyone can benefit from this book.

• Scikit-Learn
• Anaconda Distribution
• Python
• NumPy
• Machine Learning
• Data Science
• Classifiers
• Confusion Matrix
• Regressors
• Dimensionality Reduction
• Pandas Format
• Algorithmic Processing

• Logan, USA

  David Paper

Dr. David Paper is a professor at Utah State University in the Management Information Systems department. He wrote the book Web Programming for Business: PHP Object-Oriented Programming with Oracle and he has over 70 publications in refereed journals such as Organizational Research Methods, Communications of the ACM, Information & Management, Information Resource Management Journal, Communications of the AIS, Journal of Information Technology Case and Application Research, and Long Range Planning. He has also served on several editorial boards in various capacities, including associate editor. Besides growing up in family businesses, Dr. Paper has worked for Texas Instruments, DLS, Inc., and the Phoenix Small Business Administration. He has performed IS consulting work for IBM, AT&T, Octel, Utah Department of Transportation, and the Space Dynamics Laboratory. Dr. Paper's teaching and research interests include data science, process reengineering, object-oriented programming, electronic customer relationship management, change management, e-commerce, and enterprise integration.

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