Supervised Learning with Python: Concepts and Practical Implementation Using Python


Supervised Learning with Python: Concepts and Practical Implementation Using Python
By 作者:Vaibhav Verdhan
Paperback : 372 pages
ISBN-10 : 1484261550
ISBN-13 : 9781484261552
Product Dimensions : 15.5 x 2.1 x 23.5 cm
Publisher Finelybook 出版社 : Apress; 1st ed. Edition (8 Oct. 2020)
Language 语言: : English
The Book Description robot was collected from Amazon and arranged by Finelybook
Gain a thorough understanding of supervised learning algorithms By 作者: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

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