Name: AI Powered Semantic Search Courses in Kochi, Kerala | Dtree
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Empowering Semantic Search with a LLM

Categories Artificial Intelligence

Course Duration: 6h

₹39,999.00

Objective
Prerequisite
Audience
Course Content

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.

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.

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

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

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

Python libraries for semantic search
The chapter 5 explains about the various python libraries used for semantic search with its application

Semantic Search using cosine similarity
The chapter 6 illustrates the implementation of semantic search using cosine similarity method

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

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

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

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

Reranking
The chapter 11 brings the optimized search results that is implemented using reranking technique in the search technique using LLM libraries

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

Empowering Semantic Search with a LLM

Objectives

Prerequisite

Target Audience

What I will learn?

Course Curriculum

Artificial Intelligence The chapter 1 describes the importance of artificial intelligence and applications with detailed descriptions

Artificial Intelligence

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

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

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

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

Semantic Search using cosine similarity The chapter 6 illustrates the implementation of semantic search using cosine similarity method

Semantic Search using cosine similarity

LAB

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

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

Lab 1 – keyword search

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

Lab 2 – embeddings

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

Lab 3 – dense retrieval

Reranking The chapter 11 brings the optimized search results that is implemented using reranking technique in the search technique using LLM libraries

Reranking

Lab 4 – reranking

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

Lab 5 – generating answers

Semantic Search using FAISS The chapter 13 illustrates the semantic search strategy using FAISS library

Semantic Search using FAISS

Assessment

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Artificial Intelligence
The chapter 1 describes the importance of artificial intelligence and applications with detailed descriptions

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

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

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

Python libraries for semantic search
The chapter 5 explains about the various python libraries used for semantic search with its application

Semantic Search using cosine similarity
The chapter 6 illustrates the implementation of semantic search using cosine similarity method

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

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

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

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

Reranking
The chapter 11 brings the optimized search results that is implemented using reranking technique in the search technique using LLM libraries

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

Semantic Search using FAISS
The chapter 13 illustrates the semantic search strategy using FAISS library