Description
Artificial Intelligence for Data-Driven Innovation offers a structured introduction to the technologies and methods that connect data science, machine learning, and artificial intelligence. Designed for students, researchers, academics, professionals, and technology enthusiasts, the book explains how data can be transformed into meaningful insights, predictions, and innovative solutions.
The book begins with data science fundamentals and progresses through the complete data workflow, including data collection, cleaning, preprocessing, exploratory data analysis, modeling, evaluation, deployment, and monitoring. It also covers practical preparation techniques such as feature engineering, data transformation, encoding, integration, and dimensionality reduction.
Readers are introduced to supervised, unsupervised, and reinforcement learning, along with core algorithms and concepts such as regression, decision trees, neural networks, clustering, model evaluation, overfitting, and feature engineering. The text also explores advanced pattern-recognition approaches, including time-series analysis, anomaly detection, PCA, clustering, and deep learning.
The later sections connect AI and ML with practical applications across healthcare, finance, retail and e-commerce, manufacturing, transportation, social media, and marketing. Examples include fraud detection, recommendation systems, demand forecasting, predictive maintenance, customer segmentation, and sentiment analysis.
