Fundamentals of Machine Learning and Neural Network Training

Demystify how machine learning models work under the hood without getting lost in overwhelming mathematical jargon. This introductory text-based course guides you through core concepts, architecture components, and training workflows used to build intelligent applications today. What you will learn: Learn key terminology, foundational concepts, and standard machine learning pipelines. Understand the core architecture of neural networks, weights, and loss functions. Explore how models process data using vector embeddings and tokenization. Practice reading and writing simple code snippets for training and evaluating foundational models. Examine modern AI workflows, including basic fine-tuning and retrieval-augmented generation concepts. Apply best practices for data preprocessing, validation, and model optimization. Starting with fundamental definitions and basic programming concepts, you will progress step by step through core training algorithms to modern AI architectures. Designed for beginners, developers, and tech enthusiasts seeking a solid foundation in artificial intelligence with no prior machine learning experience required. Begin reading today to build your understanding of modern software and machine learning.