Building LLM Applications

This course is amongst the top-rated technical courses and I am proud to say it's an extremely popular course So far I have taught this course to over 300 Professionals

 

The course is aimed to introduce you to Large Language Models in deeper detail on what is Transformer Architecture and how we utilize the Encoder and Decoder Models.

 

This is not a Langchain course but much more than that. We go into great depth to explain the building blocks of retrieval systems and how to utilize Open Source LLMs to build your own RAG-based architectures.

 

Learning Outcome:

 

- Gain a comprehensive understanding of the end-to-end LLM architecture

 

- Construct and deploy robust and effective models in real-world settings using Large

Language Models. 

 

- Learn to tackle practical machine learning problems and deliver results in production.

 

Additionally, students will gain a comprehensive understanding of the end-to-end machine learning pipeline, allowing them to construct and deploy robust and effective models in real-world settings using Large Language Models. Overall, students will emerge with greater confidence in their abilities to tackle practical machine learning problems and deliver results in production.

 

What you get from the course:

• Collect and preprocess data for large language models

• Train and fine-tune pre-trained large language models for specific tasks

• Evaluate the performance of large language models and select appropriate metrics

• Deploy large language models in real-world applications using APIs and Huggingface

• Understand ethical considerations involved in working with large language models, such as avoiding bias and ensuring transparency

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