Description
Artificial Intelligence for Data-Driven Innovation offers a structured introduction to the technologies and methods shaping modern data-driven decision-making. Designed for students, researchers, educators, and industry professionals, this book connects core data science principles with artificial intelligence and machine learning applications.
The book begins with essential data science concepts, terminology, and lifecycle stages, then moves through data collection, cleaning, transformation, feature engineering, exploratory data analysis, and visualization. Readers are introduced to machine learning fundamentals, including supervised, unsupervised, and reinforcement learning, along with algorithms such as regression, decision trees, neural networks, support vector machines, and K-means clustering.
Advanced coverage extends to deep learning, neural network architectures, natural language processing, computer vision, predictive analytics, generative AI, explainable AI, and emerging technologies. The book also emphasizes responsible development, addressing data privacy, security, bias, fairness, accountability, transparency, and ethical AI practices.
Practical relevance is a key strength, with industry-focused discussions spanning healthcare, finance, retail, manufacturing, transportation, and social media marketing. Examples demonstrate how AI and ML can support fraud detection, personalized recommendations, predictive maintenance, demand forecasting, diagnostics, and smarter decision-making.
A useful academic textbook and practical reference, this book helps readers build a broad foundation for understanding and applying AI-driven data science in real-world settings.
