LLM Application Engineering and Development Certification

This comprehensive course on Evaluating and Applying LLM Capabilities equips you with the skills to analyze, implement, and assess large language models in real-world scenarios. Begin with core capabilities, learn summarization, translation, and how LLMs power industry-relevant content generation. Progress to interactive and analytical applications—explore chatbots, virtual assistants, and sentiment analysis with hands-on demos using LangChain and ChromaDB. Conclude with benchmarking and evaluation—master frameworks like ROUGE, GLUE, SuperGLUE, and BIG-bench to measure model accuracy, relevance, and performance. To be successful in this course, you should have a basic understanding of LLMs, Python, and NLP fundamentals. By the end of this course, you will be able to: - Explore LLM Capabilities: Understand summarization, translation, and their applications - Build LLM Applications: Create chatbots and sentiment analysis tools using real-world tools - Evaluate Model Performance: Use ROUGE, GLUE, and BIG-bench to benchmark LLMs - Analyze Use Cases: Assess benefits, limitations, and deployment of LLM-powered solutions Ideal for AI developers, ML engineers, and GenAI professionals.

This LLM Fine-Tuning course equips you with the skills to optimize and deploy domain-specific large language models for advanced Generative AI applications. Begin with foundational concepts—learn supervised fine-tuning, parameter-efficient methods (PEFT), and reinforcement learning with human feedback (RLHF). Master data preparation, hyperparameter tuning, and key evaluation strategies. Progress to implementation using LLM frameworks and libraries, and apply best practices for model selection, bias monitoring, and overfitting control. Conclude with hands-on demos—fine-tune Falcon-7B and build an image generation app using LangChain and OpenAI DALL·E. You should have a solid background in Python, deep learning fundamentals, and prior exposure to large language models. By the end of this course, you will be able to: - Fine-tune LLMs using PEFT, RLHF, and supervised methods - Prepare datasets and optimize hyperparameters for tuning - Evaluate and deploy fine-tuned models using GenAI frameworks - Apply tuning concepts in real-world use cases like Falcon-7B and DALL·E apps Ideal for AI developers, ML engineers, and GenAI researchers.

This LangChain for Advanced Generative AI Workflows course equips you with the skills to build scalable, retrieval-augmented applications using large language models. Begin with foundational concepts—learn how Model I/O, document loaders, and text splitters prepare and structure data for GenAI tasks. Progress to embedding techniques and vector stores for efficient semantic search and data retrieval. Master LangChain’s retrieval methods and chain types such as Sequential, Stuff, Refine, and Map Reduce to manage complex workflows. Conclude with LangChain Memory and Agents—develop context-aware systems and integrate local LLMs like Falcon for real-world applications. To be successful in this course, you should have a solid understanding of Python, language models, and basic generative AI concepts. By the end of this course, you will be able to: - Structure and process unstructured data using LangChain I/O tools - Use embeddings and vector stores for semantic search and retrieval - Build multi-step GenAI workflows using LangChain chains and retrievers - Create context-aware applications with LangChain memory and agents Ideal for AI developers, ML engineers, and GenAI practitioners.