Description :

The development of artificial intelligence has transformed the way humans work, learn, and make decisions. Behind these intelligent technologies are the fundamental concepts of machine learning and neural networks, the foundation of modern systems. *Artificial Intelligence for Beginners: Concepts, Logic, and Case Studies in Neural Networks* presents a systematic and applicable learning guide for understanding the fundamentals of artificial intelligence from beginner to intermediate levels.

This book covers the concepts of artificial intelligence, machine learning, and neural networks step by step, starting with simple mathematical foundations, the structure of artificial neurons, perceptrons, and activation functions, and continuing through the training process using loss functions and gradient descent. Furthermore, readers are introduced to model evaluation, error analysis, and a series of applied case studies, structured step by step. These include simple regression for predicting house prices, exam scores, and weight, binary and multi-class classification using logistic regression and softmax, model evaluation with real-world datasets, and an introduction to deep learning through image classification using CNNs on MNIST and CIFAR-10, simple residual networks, and ResNet-based transfer learning for real-world image classification. All discussions are complemented by illustrations, tables, relevant equations, and implementation examples using the Python programming language that can be put into practice immediately. To support more practical learning, this book also provides program code for each case study so readers can try, modify, and evaluate the models independently. The code is designed to run both in a local Python environment and through Google Colab, making the learning process more accessible, interactive, and suitable for both independent study and classroom practice.

Written as a tutorial textbook, offering both a practical guide and an easy-to-understand introductory material, this book is intended for diploma three (DIII), diploma four (DIV), undergraduate (S1) students, lecturers, teachers, novice practitioners, and the general public interested in learning the basics of artificial intelligence, machine learning, and neural networks. This book not only explains the theory but also guides readers to understand how artificial intelligence models work mathematically and computationally. With a structured and easy-to-follow approach, readers will gain a complete picture of the machine learning model development process, from dataset development to evaluation and interpretation of results. Through a concise, clear, and applicable presentation, this book is expected to be a gateway to exploring the world of artificial intelligence in more depth.