AI-Builders-Bootcamp-1

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AI-Builders-Bootcamp-1

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Code repository for AI Builders Bootcamp #1

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# AI-Builders-Bootcamp-1
Code repository for AI Builders Bootcamp #1.

Course homepage on Maven: https://maven.com/shaw-talebi/ai-builders-bootcamp

## Session 1: Introduction, Software 1.0
Getting started with AI and building basic automation scripts with Python.

Examples:

- [Automated email broadcaster](https://github.com/ShawhinT/AI-Builders-Bootcamp-1/blob/main/session-1/example_1-email_broadcast.ipynb)
- [Automated report builder and emailer](https://github.com/ShawhinT/AI-Builders-Bootcamp-1/blob/main/session-1/example_3-report_builder_emailer.ipynb)

## Session 2: Software 2.0, Data Engineering, Machine Learning
Building ETL pipelines for machine learning projects.

Examples:
- [ETL of Gmail inbox](https://github.com/ShawhinT/AI-Builders-Bootcamp-1/blob/main/session-2/example_1-gmail_ETL.ipynb)
- [Training email classifier](https://github.com/ShawhinT/AI-Builders-Bootcamp-1/blob/main/session-2/example_2-email_classifier.ipynb)

## Session 3: Software 3.0, Foundation Models, Prompt Engineering
Here, we begin building AI systems with LLMs. Unlike machine learning, we don't need datasets to get started.

Examples:
- [Research paper summarizer](https://github.com/ShawhinT/AI-Builders-Bootcamp-1/blob/main/session-3/example_1-paper_summarizer.ipynb)
- [Text classifier](https://github.com/ShawhinT/AI-Builders-Bootcamp-1/blob/main/session-3/example_2-text-classifier.ipynb)
- [Local document QA with LLaMA 3.2 Vision](https://github.com/ShawhinT/AI-Builders-Bootcamp-1/blob/main/session-3/example_3-local_visual_QA.ipynb)

## Session 4: Software 3.0, RAG, Text Embeddings
Prompting LLMs ChatGPT-style only scratches the surface of what we can use modern language models for. We can also leverage RAG to improve model performance and text embeddings to make text computable.

Examples:
- [Analyzing unstructured survey results](https://github.com/ShawhinT/AI-Builders-Bootcamp-1/blob/main/session-4/example_1-unstructured_survey_analysis.ipynb)
- [Semantic search over blog posts](https://github.com/ShawhinT/AI-Builders-Bootcamp-1/blob/main/session-4/example_2-blog_semantic_search.ipynb)
- [RAG chatbot over blog series](https://github.com/ShawhinT/AI-Builders-Bootcamp-1/blob/main/session-4/example_3-blog_QA_RAG.ipynb)

## Session 5: Software 3.0, Fine-tuning
Although LLMs can solve a wide range of problems out-of-the-box, there are situations where more model customization is required. This can be achieved through model fine-tuning, which involves adapting a model to a particular use case through additional training.

Examples
- [Fine-tuning BERT for Text Classification](https://github.com/ShawhinT/AI-Builders-Bootcamp-1/blob/main/session-5/example_1-finetune_bert_classifier.ipynb)
- [Fine-tuning a LinkedIn Post Writer](https://github.com/ShawhinT/AI-Builders-Bootcamp-1/blob/main/session-5/example_2-linkedin_post_writer.ipynb)

## Session 6: AI Project Management
Technical skills are necessary but not sufficient for generating value with AI. In the final session, we review a project management framework for getting an ROI on AI projects and best practices.

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