Requirements

• Python knowledge
• Pandas Knowledge

Description

In a rich Artificial Intelligence Bootcamp, learn S-BERT and RAG(LLM) through Natural Processing Language (NLP) with Python, and develop a semantic text search engine API by solving a real problem of a purchasing analysis system.

As content:

1. Fundamentals.
   Learn the fundamentals of Data Science;

   • Learn the fundamentals of Machine Learning;

   • Learn the fundamentals of Natural Language Processing;

   • Learn the fundamentals of Data Cleaning, Word Embendings, Stopwords, and Lemmatization;

   • Learn the fundamentals of text search by keywords and semantic text search;

2. Practice Data Science to understand the problem, prepare the database and statistical analysis;

3. Practical project.
   This course is divided into two modules where you will learn concepts and build a text search application in a practical way.

   1. BERT
      In this module, you will work with:

      • Python to develop the application;

      • Data cleaning techniques to prepare the database;

      • Using the SpaCy library for Natural Language Processing;

      • Generating Word Embeddings and calculating similarity for data recovery;

      • Transformers model for data recovery by context;

      • S-BERT as a semantic text search tool;

      • Flask and Flassger for developing APIs.

   2. Retrieval-augmented generation (RAG)
      In this module you will work with:

      • Python to develop the application;

      • Large Language Models (LLMs). Advanced AI models that understand and generate natural language;

      • Using the OpenAI API to build AI products

      • LangChain to build applications that use LLMs;

      • Flask and Flassger for developing APIs.

4. Optional module: learn how to develop API with Flask.

Welcome and have fun.

Who this course is for:

• Interested in innovation in the latest and most valuable Data Science and Artificial Intelligence technologies.
• Interested in deepening Natural Language Processing (NLP) techniques
• Interested in building a semantic text search engine that evaluates synonyms in search terms.