Foundations of AI and Neural Networks in Python

Artificial intelligence is transforming every industry, but understanding how these systems actually learn can feel overwhelming. This course demystifies the core mechanics of neural networks, guiding you from biological inspirations to modern deep learning architectures. You will transition from a curious beginner to a confident practitioner capable of designing, training, and evaluating artificial neural networks. You will understand the mathematical foundations of gradient descent, work with industry-standard Python libraries, and write clean, structured code for machine learning tasks. What you'll learn: - Understand the fundamental concepts of artificial intelligence, neural network architectures, and key machine learning terminology - Explain the mechanics of perceptrons, activation functions, forward propagation, and loss functions - Apply backpropagation and gradient descent optimization to train neural networks effectively - Build and evaluate classification models using TensorFlow and Keras in Jupyter Notebook - Prepare datasets efficiently using modern Python data-handling conventions and libraries - Explore modern AI paradigms, including vector embeddings and the foundational concepts behind transformer models The course begins with foundational AI concepts and biological inspirations before moving step-by-step through mathematical optimization, neural network training, and practical model implementation. You will read clear explanations, analyze structured code examples, and study real-world machine learning workflows. This course is designed for beginners with basic Python knowledge who want to enter the field of artificial intelligence. No prior experience with machine learning or advanced mathematics is required. Start reading today to build your foundation in artificial intelligence and deep learning.