Foundation Course on AI, Machine Learning and Generative AI

About this course

What you'll learn (Key takeaways):

• Develop a strong understanding of fundamental AI/ML principles and the role of LLMs within the AI ecosystem.
• Explore different types of machine learning, including Supervised, Unsupervised, and Reinforcement Learning, with real-world examples.
• Understand core machine learning concepts like model lifecycle, overfitting, loss functions, and evaluation metrics.
• Gain proficiency in Natural Language Processing (NLP) essentials and text representation techniques.
• Master advanced prompt engineering techniques, including Zero-shot, Few-shot, Role, Persona, and Chain-of-Thought prompting.
• Learn to integrate LLMs into applications using orchestration frameworks like LangChain.
• Understand and utilize vector databases for enhanced retrieval in LLM applications.
• Implement Retrieval-Augmented Generation (RAG) architectures to improve accuracy and reduce hallucinations in enterprise applications.
• Discover how LLMs can interact with external systems through tool and function calling.
• Build LangChain agents that leverage external tools to automate workflows.
• Delve into advanced agent design patterns and implement robust memory mechanisms for intelligent agents.
• Understand deployment strategies for LLM applications on cloud platforms, including containerization and serverless options.
• Learn key metrics and best practices for monitoring LLM performance and cost.
• Gain knowledge of ethical AI, responsible LLM development, and security best practices for LLMs.
• Explore advanced generative AI for code generation, data analysis, visualization, and creative content.

Understand advanced RAG architectures like multi-hop and self-correcting RAG.Design complex, autonomous multi-agent systems and implement human-in-the-loop strategies.

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