Applied Supervised Learning with Python

Learn the technology that's the backbone of self-driven cars, facial recognition programs, and several other exciting services and applications
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Applied Supervised Learning with Python

Clement Manger

Learn the technology that's the backbone of self-driven cars, facial recognition programs, and several other exciting services and applications
This title is available to pre-order now and is expected to be published in
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Book Details

ISBN 139781789954920
Paperback403 pages

Book Description

Applied Supervised Learning with Python provides you a rich understanding of machine learning, one of the most pursued topics in information science, and Python, one of the most popular scripting languages. Through this book, you'll learn Jupyter Notebooks, the technology used in academic and commercial circles with in-line code running support.

You'll begin the book with an overview of supervised machine learning by looking at some intuitive and real-world examples. You'll study the tools and libraries that Python offers for machine learning. Then, you'll learn the basics of data science in Python, such as slicing, filtering, and merging. You'll explore various techniques used for assessing data quality, learn about regression models, and explore their applications. You'll also study classification and explore concepts, such as K-nearest neighbor classification and decision trees. You'll learn the famous ID3 algorithm that recursively forms splits data to make accurate decision trees. Next on the agenda will be neural networks, the internals of these models, why they work, and how you can modify different architectures to maximize performance. You'll find out more about ensemble modeling, Random Forest classifier, and other methods for combining the results from multiple models. Finally, you'll learn cross-validation to test your algorithm and check how well the model works on data it hasn't 'seen'.

With this book, you'll be equipped to work with machine learning algorithms or create some of your own!

Table of Contents

What You Will Learn

  • Understand the concept of supervised learning and its applications
  • Use machine learning Python libraries to build your supervised learning algorithms
  • Validate models using k-fold
  • Use xgboost to train and show performance of your model
  • Understand and work with decision trees
  • Use ensemble modelling techniques to create newer models
  • Apply various metrics to compare machine learning models

Authors

Table of Contents

Book Details

ISBN 139781789954920
Paperback403 pages
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