Algorithms and Data Structures for Machine Learning and LLMs

To build efficient machine learning systems and work with modern large language models, you must understand the underlying computational structures that power them. Writing code that works is only the first step; writing code that scales requires a deep grasp of algorithms and data structures. This text-based course guides you from foundational computer science principles to their direct applications in modern AI, helping you transition from writing basic scripts to designing highly optimized, production-ready workflows for data processing, model evaluation, and vector search. What you'll learn: - Understand fundamental data structures like arrays, trees, and hash tables and how they underpin popular machine learning libraries. - Analyze algorithmic complexity using Big O notation to write highly efficient, scalable code. - Apply search and sorting algorithms to real-world data preparation and preprocessing pipelines. - Explore the mechanics of vector databases and similarity search algorithms used in modern LLM applications. - Implement basic graph algorithms to understand neural network structures and dependency resolution. - Practice solving algorithmic problems tailored to machine learning engineering and data science roles. You will start by exploring core definitions and time complexity before moving step-by-step through data structures, search algorithms, and practical applications in modern machine learning and language model pipelines. This course is designed for aspiring machine learning engineers, data scientists, and developers who want to strengthen their computer science fundamentals, with no prior algorithmic background required. Begin reading today to master the computational foundation of modern artificial intelligence.