Uplift your tech and AI career with our cutting-edge SemSearch: Empowering Search with LLM Boot Camp program. Uncover the limitless potential of large language model and redefine your expertise in working with Semantic Search.
Our program trains you with the skills to generate Semantic search using large language models and introduce you an advanced approach to information retrieval, empowering the deep understanding of language models which showcase intense impact of semantic search. Jump into real world analysis with enormous numbers of datasets and embark on a journey that will skyrocket your proficiency and open doors to new opportunities in the tech and AI provinces.
Prerequisite
Anyone can attend the course.
Target Audience
Looking to take your career in AI and technology to new heights? Our program is designed to empower professional specialists of all backgrounds, whether you're a seasoned Gen AI researcher, machine learning engineer, data scientist, or tech professional involved in natural language understanding and natural language generation projects. With our program's comprehensive curriculum, you'll master the art of semantic search that is integrated with large language model for optimistic programming skills in retrieving information. By enrolling in our program, you'll be able to fetch the infinite opportunities in the dynamic world of AI and technology.
What I will learn?
Learn the introductory concepts of semantic search and semantic LLMs.
Explore various simple yet efficient techniques such as embedding, dense retrieval and reranking methods for semantic search with LLM.
Discover the endless possibilities of LLMs and learn how to utilize LLM libraries for best version of semantic search Learn how to write and refine prompts using the OpenAI API key generation.
Course Curriculum
Artificial Intelligence
The chapter 1 describes the importance of artificial intelligence and applications with detailed descriptions
Artificial Intelligence
00:00
Generative AI
The chapter 2 discusses the new kind of artificial intelligence called generative AI and various strategies of Generative AI. It also engages the models applications of Generative AI with detailed real time applications
Generative AI
00:00
Large Language Models
The chapter 3 explains the subdivision of generative AI called large language model which is used to progress the natural languages and provide the optimal information retrieval
Large Language Models
00:00
Prompt Engineering
The chapter 4 discusses the techniques and methods to achieve the implementation of Large Language model. In addition it explains the various techniques of prompt engineering
Prompt Engineering
00:00
Python libraries for semantic search
The chapter 5 explains about the various python libraries used for semantic search with its application
Python libraries for semantic search
00:00
Semantic Search using cosine similarity
The chapter 6 illustrates the implementation of semantic search using cosine similarity method
Semantic Search using cosine similarity
00:00
LAB
00:00
Assessment
Introduction to semantic LLMs
The chapter 7 describes the importance of large language model when its used with semantic search strategies. It also states the various applications of semantic Large language models in real time
Introduction to semantic LLMs
00:00
Keyword Based Search
The chapter 8 explains on how the search strategy is equipped with keyword based search, to search and retrieve the datas from large datasets using LLM libraries
Keyword Based Search
00:00
Lab 1 – keyword search
00:00
Embedding
The chapter 9 elevates on how the numerical represenations can be done while implementing searches in terms of embbedding and using LLM libraries associated with traditional python libraries such as Pandas
Embedding
00:00
Lab 2 – embeddings
00:00
Dense Retrieval
The chapter 10 dense retrieval concentrates on the dense retrieval instead of keyword search and an improvized search strategy is done in this chapter using cohere and weaviate libraries
Dense Retrieval
00:00
Lab 3 – dense retrieval
00:00
Reranking
The chapter 11 brings the optimized search results that is implemented using reranking technique in the search technique using LLM libraries
Reranking
00:00
Lab 4 – reranking
00:00
Generating Answers
The chapter 12 gives a detailed presentation of a simple generative model that uses LLM libraries for embedding, reranking , dense retrieval and finally brings an optimized results for a semantic search
Generating Answers
00:00
Lab 5 – generating answers
00:00
Semantic Search using FAISS
The chapter 13 illustrates the semantic search strategy using FAISS library
