Books Applied Machine Learning for Data Science Practitioners Book by Vidya Subramanian

"Applied Machine Learning for Data Science Practitioners" by Vidya Subramanian is a comprehensive reference book designed to guide data scientists through the end-to-end process of solving machine learning problems with practical applications to real-world business challenges. Unlike many texts that focus heavily on specific algorithms or coding, this book helps practitioners evaluate various data science techniques and algorithms to select the best solutions based on the business context, available data, and desired outcomes.

Key aspects covered include:

Data preparation techniques such as cleaning, integration, transformation, compression, visualization, and exploratory data analysis.

Model validation strategies including cross-validation tailored to different data distributions like independent, imbalanced, or grouped data.

Regression and classification models with suitable performance metrics.

Clustering methods categorized by partition, hierarchy, fuzzy theory, distribution, density, and graph theory.

Anomaly detection concepts, including handling noise, rare events, and outliers.

Model performance optimization including selection, decision trees, and ensemble methods.

Ethical considerations in machine learning.

Guidance on deploying and monitoring machine learning models in production.

The book assumes readers have a basic understanding of business problem-solving with data, high school level math, statistics, and coding skills. It contains about 656 pages and is published by John Wiley & Sons in 2025.