Modern Data Mining with Python

"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.

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.

WHAT YOU WILL LEARN

• 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.

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

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. He is a seasoned professional in the banking and consulting industry with experience modernizing retail, credit risk, and marketing platforms leveraging AI/ML techniques on modern-day cloud and MLOps infrastructure.

Dushyant Sengar also brings a wealth of experience in the model risk management (MRM) arena that enables him to provide a holistic and efficient road map for the AI- based product development lifecycle.

He holds an M.S. in data science from Northwestern University, Chicago, and a B.E. in Information technology from M.I.T.S. Gwalior, India. He has also been a freelance data science and analytics trainer during his India-based professional experience and is now a tenured speaker in the AI, Innovation, and Risk management-driven conference scene in the United States. Dushyant thrives on bringing cross-functional knowledge and teams together to ensure alignment between technology, risk, and business goals. He has a proven track record of mentoring students from various backgrounds and nurturing their data science potential.

When he is not working, you can often find him reading, writing, or exploring new places and cultures. He is passionate about using technology for social good, driven by a mission to spread technology and AI knowledge among enthusiasts for positive change.

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.

He is an alumni of prestigious institutions like Jawaharlal Nehru University, and Shri Ram College of commerce, University of Delhi, India.