Demystifying AI & Machine Learning: A Beginner's Guide to LLMs and Agentic AI

What you will learn:

Grasp core machine learning concepts and their applications.
Master the fundamentals of supervised, unsupervised, and reinforcement learning.
Understand the complete machine learning model-building process.
Become familiar with key machine learning algorithms.
Explore the inner workings of Large Language Models (LLMs).
Learn about prompt engineering and Retrieval-Augmented Generation (RAG).
Understand the principles and ethical implications of Agentic AI.
Explore real-world applications of Agentic AI.
Gain insight into MLOps and its importance in AI development.
Develop a solid foundation for further AI learning and exploration.

Description

Dive into the world of Artificial Intelligence with our comprehensive beginner-friendly course! No prior experience or coding skills are needed. We'll guide you through the core concepts of machine learning, from supervised and unsupervised learning to the intricacies of popular algorithms like regression and decision trees. You'll gain a deep understanding of how these models are built, evaluated, and improved.

Next, we explore the fascinating realm of Large Language Models (LLMs), such as ChatGPT. Uncover the mechanics behind LLMs, including tokens, parameters, and the transformative power of prompt engineering. We'll also explore techniques like Retrieval-Augmented Generation (RAG) that take LLM power to the next level.

Finally, we venture into the cutting-edge field of Agentic AI, where AI systems demonstrate autonomy, planning, and reasoning capabilities. Discover agent architecture, memory mechanisms, multi-agent systems, and the ethical implications of this rapidly evolving technology. We'll bridge the gap between theory and practice by examining real-world applications and exploring the essential role of MLOps in deploying intelligent systems. This course equips you with the essential knowledge and a clear vision of the exciting future of AI and its impact on various industries.

By the end, you'll understand:

Core Machine Learning principles
Popular ML algorithms and their applications
The inner workings of LLMs like ChatGPT
The capabilities and ethical considerations of Agentic AI
Practical applications of agent-based systems
MLOps and its relevance to AI development

Start your AI journey today – no coding experience needed!

Curriculum

Introduction to Machine Learning

This foundational section begins with a comparison of traditional programming and machine learning, setting the stage for understanding the core differences. It then delves into the three fundamental types of machine learning: supervised, unsupervised, and reinforcement learning. Key concepts like feature engineering, dataset splitting, the training vs. inference phases, model evaluation (overfitting/underfitting), hyperparameter tuning, and bias-variance trade-off are explored with clear explanations and examples, providing a robust groundwork for further learning.

Exploring Machine Learning Algorithms

This section introduces several popular machine learning algorithms, including linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), neural networks, and boosting algorithms. Each algorithm is explained in a straightforward manner, focusing on its purpose and application, ensuring a strong grasp of the diverse tools available in the machine learning toolkit.

Understanding Large Language Models (LLMs)

This section provides a comprehensive introduction to Large Language Models (LLMs). It explains what LLMs are and how they work, covering key concepts such as tokens, parameters, transformers, and the attention mechanism. You'll also learn about pre-training and fine-tuning, the crucial processes that shape these powerful models. The section further delves into prompt engineering and its impact on LLM performance, along with an examination of LLMs' limitations and the advantages of Retrieval-Augmented Generation (RAG).

Introduction to Agentic AI

This section introduces the groundbreaking concept of Agentic AI, explaining its characteristics and differentiating it from other AI approaches. It explores essential elements of Agentic AI, such as goals, tools, memory, planning, agent architectures (including different agent types), and the complexities of multi-agent systems. Real-world examples and the ethical considerations associated with Agentic AI deployment are also examined, providing a well-rounded understanding of this rapidly developing field.

Introduction to MLOps

This section provides a thorough introduction to MLOps, starting with a clear definition and tracing its evolution. It compares different AI approaches (ML, LLMs, and Agentic AI) and examines successful MLOps case studies to highlight best practices. A comparison between DevOps and MLOps is included, followed by an exploration of the emerging role of the MLOps engineer, showing the importance of MLOps in the modern AI landscape.

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