Modern Data Mining with Python: A risk-managed approach to developing and deploying explainable and efficient algorithms using ModelOps Front Cover

Modern Data Mining with Python: A risk-managed approach to developing and deploying explainable and efficient algorithms using ModelOps

  • Length: 438 pages
  • Edition: 1
  • Publisher:
  • Publication Date: 2024-02-26
  • ISBN-10: 9355519141
  • ISBN-13: 9789355519146

Data miner’s survival kit for explainable, effective, and efficient algorithms enabling responsible decision-making

Key Features

  • Accessible, and case-based exploration of the most effective data mining techniques in Python.
  • An indispensable guide for utilizing AI potential responsibly.
  • Actionable insights on modeling techniques, deployment technologies, business needs, and the art of data science, for risk mitigation and better business outcomes.


“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.

After reading this book, readers will be equipped with the skills and knowledge necessary to use Python for data mining and analysis in an industry set-up. They will be able to analyze and implement algorithms on large structured and unstructured datasets.

  • Explore the data mining spectrum ranging from data exploration and statistics.
  • Gain hands-on experience applying modern algorithms to real-world problems in the financial industry.
  • Develop an understanding of various risks associated with model usage in regulated industries.
  • Gain knowledge about best practices and regulatory guidelines to mitigate model usage-related risk in key banking areas.
  • Develop and deploy risk-mitigated algorithms on self-serve ModelOps platforms.

Who this book is for

This book is for a wide range of early career professionals and students interested in data mining or data science with a financial services industry focus. Senior industry professionals, and educators, trying to implement data mining algorithms can benefit as well.

Table of Contents

1. Understanding Data Mining in a Nutshell

2. Basic Statistics and Exploratory Data Analysis

3. Digging into Linear Regression

4. Exploring Logistic Regression

5. Decision Trees with Bagging and Boosting

6. Support Vector Machines and K-Nearest Neighbors

7. Putting Dimensionality Reduction into Action

8. Beginning with Unsupervised Models

9. Structured Data Classification using Artificial Neural Networks

10. Language Modeling with Recurrent Neural Networks

11. Image Processing with Convolutional Neural Networks

12. Understanding Model Risk Management for Data Mining Models

13. Adopting ModelOps to Manage Model Risk

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