Modern Data Mining with Python: A Risk-Managed Approach to Developing and Deploying Explainable and Efficient Algorithms Using Modelops
"Modern Data Mining with Python" is a guidebook for responsibly implementing data mining techniques that involve collecting, storing, and analyzing large amounts of structured and unstructured data to extract useful insights and patterns. Enter into the world of data mining and machine learning. Use insights from various data sources, from social media to credit card transactions. Master statistical tools, explore data trends, and patterns. Understand decision trees and artificial neural networks (ANNs). Manage high-dimensional data with dimensionality reduction. Explore binary classification with logistic regression. Spot concealed patterns with unsupervised learning. Analyze text with recurrent neural networks (RNNs) and visuals with convolutional neural networks (CNNs). Ensure model compliance with regulatory standards.
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Modern Data Mining with Python: A Risk-Managed Approach to Developing and Deploying Explainable and Efficient Algorithms Using Modelops
"Modern Data Mining with Python" is a guidebook for responsibly implementing data mining techniques that involve collecting, storing, and analyzing large amounts of structured and unstructured data to extract useful insights and patterns. Enter into the world of data mining and machine learning. Use insights from various data sources, from social media to credit card transactions. Master statistical tools, explore data trends, and patterns. Understand decision trees and artificial neural networks (ANNs). Manage high-dimensional data with dimensionality reduction. Explore binary classification with logistic regression. Spot concealed patterns with unsupervised learning. Analyze text with recurrent neural networks (RNNs) and visuals with convolutional neural networks (CNNs). Ensure model compliance with regulatory standards.
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Modern Data Mining with Python: A Risk-Managed Approach to Developing and Deploying Explainable and Efficient Algorithms Using Modelops

Modern Data Mining with Python: A Risk-Managed Approach to Developing and Deploying Explainable and Efficient Algorithms Using Modelops

by Dushyant Singh Sengar, Vikash Chandra
Modern Data Mining with Python: A Risk-Managed Approach to Developing and Deploying Explainable and Efficient Algorithms Using Modelops

Modern Data Mining with Python: A Risk-Managed Approach to Developing and Deploying Explainable and Efficient Algorithms Using Modelops

by Dushyant Singh Sengar, Vikash Chandra

Overview

"Modern Data Mining with Python" is a guidebook for responsibly implementing data mining techniques that involve collecting, storing, and analyzing large amounts of structured and unstructured data to extract useful insights and patterns. Enter into the world of data mining and machine learning. Use insights from various data sources, from social media to credit card transactions. Master statistical tools, explore data trends, and patterns. Understand decision trees and artificial neural networks (ANNs). Manage high-dimensional data with dimensionality reduction. Explore binary classification with logistic regression. Spot concealed patterns with unsupervised learning. Analyze text with recurrent neural networks (RNNs) and visuals with convolutional neural networks (CNNs). Ensure model compliance with regulatory standards.

Product Details

ISBN-13: 9789355519146
Publisher: Bpb Publications
Publication date: 03/26/2024
Pages: 438
Product dimensions: 7.50(w) x 9.25(h) x 0.89(d)

About the Author

Dushyant Singh Sengar is a passionate leader in AI and Risk management with experience building high-performing teams and leading organizations to become data-driven. His extensive 18 years of experience on both sides of the Atlantic spans various roles, including model development, risk assessment, and driving AI product development initiatives.

Vikash Chandra is a data scientist and software developer having industry experience in executing and implementing projects in the area of predictive analytics and machine learning across multiple business domains. He has experience in handling and modifying large quantities of both structured and unstructured data leveraging SAS, R, Python, and other big data technologies.
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