Overview
- Hands-on approach for implementing supervised learning algorithms like decision tree, RF, SVM, and Neural Nets with Python
- Cover the mathematics of supervised learning algorithms in a lucid manner
- Discusses common challenges like overfitting, data imbalance, hyperparameter tuning, outlier treatment
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Table of contents (5 chapters)
Keywords
About this book
Gain a thorough understanding of supervised learning algorithms by developing use cases with Python. You will study supervised learning concepts, Python code, datasets, best practices, resolution of common issues and pitfalls, and practical knowledge of implementing algorithms for structured as well as text and images datasets.
You’ll start with an introduction to machine learning, highlighting the differences between supervised, semi-supervised and unsupervised learning. In the following chapters you’ll study regression and classification problems, mathematics behind them, algorithms like Linear Regression, Logistic Regression, Decision Tree, KNN, Naïve Bayes, and advanced algorithms like Random Forest, SVM, Gradient Boosting and Neural Networks. Python implementation is provided for all the algorithms. You’ll conclude with an end-to-end model development process including deployment and maintenance of the model.After reading Supervised Learning with Python you’ll have a broad understanding of supervised learning and its practical implementation, and be able to run the code and extend it in an innovative manner.What You'll Learn
- Review the fundamental building blocks and concepts of supervised learning using Python
- Develop supervised learning solutions for structured data as well as text and images
- Solve issues around overfitting, feature engineering, data cleansing, and cross-validation for building best fit models
- Understand the end-to-end model cycle from business problem definition to model deployment and model maintenance
- Avoid the common pitfalls and adhere to best practices while creating a supervised learning model using Python
Who This Book Is For
Data scientists or data analysts interested in best practices and standards for supervised learning, and using classification algorithms and regression techniques to develop predictive models.
Authors and Affiliations
About the author
Bibliographic Information
Book Title: Supervised Learning with Python
Book Subtitle: Concepts and Practical Implementation Using Python
Authors: Vaibhav Verdhan
DOI: https://doi.org/10.1007/978-1-4842-6156-9
Publisher: Apress Berkeley, CA
eBook Packages: Professional and Applied Computing, Apress Access Books, Professional and Applied Computing (R0)
Copyright Information: Vaibhav Verdhan 2020
Softcover ISBN: 978-1-4842-6155-2Published: 08 October 2020
eBook ISBN: 978-1-4842-6156-9Published: 07 October 2020
Edition Number: 1
Number of Pages: XX, 372
Number of Illustrations: 221 b/w illustrations, 5 illustrations in colour
Topics: Machine Learning, Artificial Intelligence, Professional Computing