About the Course
This course introduces the fundamental concepts and techniques in artificial intelligence (AI), focusing on machine learning, deep learning, and large language models (LLMs). Students will gain insights into the evolving AI landscape, its history, and future trends through a mix of theoretical understanding and practical applications.
- Understand the foundational concepts of AI and its key applications.
- Differentiate between types of machine learning (supervised, unsupervised, reinforcement).
- Understand deep learning and natural language processing (NLP) techniques.
- Explore the architecture, use cases, and impact of large language models (LLMs).
- Implement techniques for fine-tuning LLMs and applying Retrieval-Augmented Generation (RAG).
- Stay informed about future trends and innovations in AI, especially related to LLMs.
This course is designed for learners with the following foundational knowledge: 1) Basic Programming Skills: Understanding of Python is recommended as many AI libraries (e.g., TensorFlow, PyTorch, Scikit-learn) are Python-based. Familiarity with basic data structures (lists, arrays, dictionaries) and control structures (loops, conditionals). 2) Mathematics: Knowledge of basic linear algebra and calculus will help in understanding machine learning algorithms, though not strictly required. Familiarity with probability and statistics concepts (mean, variance, distributions) will be useful for understanding data-driven models. 3) Basic Computer Science Concepts (optional but beneficial): A general understanding of algorithms and data processing, though detailed knowledge is not necessary, can help with grasping machine learning concepts more quickly.