AI Basics is a comprehensive guide for anyone seeking to understand the foundational concepts, techniques, tools, and real-world applications of artificial intelligence. Structured across four parts, the book takes readers from the origins of AI to practical project development.

Part I, Foundations of Artificial Intelligence, introduces core ideas such as the evolution of AI, types of intelligence (narrow, general, and superintelligence), and how AI differs from machine learning and deep learning. It also builds the mathematical and programming foundations necessary for AI, including linear algebra, probability, and Python-based development using essential libraries like NumPy and Scikit-learn.

Part II, Core Techniques in AI, delves into machine learning and deep learning fundamentals. Readers learn about supervised and unsupervised learning, model training, overfitting, neural networks, backpropagation, and gradient descent. It also explores key domains like Natural Language Processing (NLP)—from text preprocessing to large language models—and Computer Vision, including CNNs and object detection.

Part III, Tools and Applications, introduces platforms like TensorFlow, PyTorch, Jupyter Notebooks, and cloud AI services. It examines AI’s transformative impact in healthcare, finance, transportation, and robotics, while also addressing ethical concerns like bias, explainability, and regulation.

Finally, Part IV, Building Your AI Journey, equips readers to develop and deploy AI projects. It outlines the full lifecycle—from defining problems and collecting data to model evaluation and deployment—emphasizing reproducibility, collaboration, and monitoring.

This book is designed for students, professionals, and enthusiasts aiming to enter the world of AI with a strong, practical foundation.