Supervised Learning with Python: Concepts and Practical Implementation Using Python Contributor(s): Verdhan, Vaibhav (Author) |
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ISBN: 1484261550 ISBN-13: 9781484261552 Publisher: Apress OUR PRICE: $49.49 Product Type: Paperback - Other Formats Published: October 2020 |
Additional Information |
BISAC Categories: - Computers | Intelligence (ai) & Semantics - Mathematics | Probability & Statistics - General - Computers | Programming Languages - General |
Physical Information: 0.81" H x 6.14" W x 9.21" (1.21 lbs) 372 pages |
Descriptions, Reviews, Etc. |
Publisher Description: 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, Na ve 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
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. |