| 书目名称 | Supervised Learning with Python | | 副标题 | Concepts and Practic | | 编辑 | Vaibhav Verdhan | | 视频video | | | 概述 | 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.Dis | | 图书封面 |  | | 描述 | .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 concept | | 出版日期 | Book 2020 | | 关键词 | Supervised Learning; Python; Machine Learning; Regression Analysis; Decision Tree; Randon Forest; Neural N | | 版次 | 1 | | doi | https://doi.org/10.1007/978-1-4842-6156-9 | | isbn_softcover | 978-1-4842-6155-2 | | isbn_ebook | 978-1-4842-6156-9 | | copyright | Vaibhav Verdhan 2020 |
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发表于 2025-3-21 19:36:26
