AI-generated summaries for the Building Agentic AI Applications for Beginners bootcamp hosted by Codecademy. The bootcamp is a 12+ week live program covering the full path from Python fundamentals to agentic AI systems.
Disclaimer: These summaries were generated using large language models (LLMs) and may contain inaccuracies, misinterpretations, or missing context. They are intended as study aids, not as a replacement for attending sessions or reading official materials. If you spot an error, please open an issue or submit a pull request — every contribution is welcome!
Each file is a detailed summary of a single live session, following a consistent structure:
- TL;DR — a one-paragraph overview of the session
- Topics Covered — bullet-point list of everything discussed
- Key Concepts Explained — deeper breakdowns with definitions, examples, and caveats
- Code Snippets — pseudocode or actual code walked through during the session
- Mistakes / Corrections — notable errata or live debugging moments
- Recap of Q&A — student questions and instructor answers
| Week | Sessions | Topics |
|---|---|---|
| 01 | 3 | Python fundamentals — variables, data types, operators, strings, slicing, lists, tuples, sets, bootcamp logistics |
| 02 | 3 | Dictionaries, control flow, loops, functions, NumPy, Pandas, Matplotlib/Seaborn, intro to agentic AI concepts |
| 03 | 3 | Predictive AI, ML foundations (train/test, features/labels), generative vs. agentic AI overview, tokenization, embeddings, context windows, RAG intro, LLM landscape |
| 04 | 3 | Supervised learning (regression, classification), feature scaling, KNN, decision trees, random forest, ensemble methods, customer churn practical, K-Means clustering |
| 05 | 3 | NLP (tokenization, stemming, lemmatization, POS tagging, stop words), Bag of Words, TF-IDF, Word2Vec, transformers (BERT/GPT), prompt engineering |
| 06 | 3 | Generative AI deep dive, LLMs, RAG architecture (chunking, embeddings, vector DBs, FAISS), building RAG apps with LangChain and Streamlit, project reviews |
| 07 | 3 | APIs vs. open-source models, Ollama setup, local RAG, quantization, RAG vs. fine-tuning, Hugging Face pipelines, hybrid RAG chatbot assignment |
| 08 | 3 | RAG evaluation — LLM-as-a-judge, BLEU/ROUGE/METEOR, RAGAS framework, model comparison (TinyLlama, Llama 3.2, Groq), RAG project showcase |
| 09 | 2 | Agentic AI concepts & components, ReAct, LangChain tools, React agents, LangGraph state graphs |
| 10 | 3 | CrewAI multi-agent orchestration, content marketing pipeline, AWS EC2 deployment, interview prep, intro to n8n |
| 11 | 2 | n8n workflows — lead capture, social media automation, AI news agent, end-to-end RAG with Pinecone & Hugging Face |
| 12 | 3 | Multi-agent systems & guardrails (LangGraph + n8n), MCP, end-to-end LangGraph/CrewAI projects, career prep |
| Date | File | Title | Topics Discussed |
|---|---|---|---|
| 2026-03-21 | week01-session01.md | Bootcamp Overview & Python Basics | Bootcamp logistics, AI industry timeline, variables, keywords, data types, operators, Google Colab |
| 2026-03-22 | week01-session02.md | Strings, Slicing, and Data Structures | Strings, slicing, lists, tuples, sets, mutability |
| 2026-03-26 | week01-session03.md | Doubt-Clearing — Logistics, Career Paths & Client Delivery | Break week plan, career paths, client delivery, Q&A |
| Date | File | Title | Topics Discussed |
|---|---|---|---|
| 2026-03-28 | week02-session01.md | Dictionaries, Control Flow, Loops & Functions | Dicts, if/else, for/while loops, functions, arguments |
| 2026-03-29 | week02-session02.md | NumPy, Pandas & Visualization Libraries | NumPy arrays, Pandas DataFrames, Matplotlib, Seaborn |
| 2026-04-02 | week02-session03.md | Break Week Plan, Assignments & Intro to Agentic AI | Assignment workflow, agentic AI preview, break-week guidance |
| Date | File | Title | Topics Discussed |
|---|---|---|---|
| 2026-04-18 | week03-session01.md | Predictive AI, ML Foundations & Generative vs. Agentic AI | Predictive AI, train/test split, features/labels, GenAI vs agentic AI |
| 2026-04-19 | week03-session02.md | Tokenization, Embeddings, Context Windows, RAG & LLM Options | Tokenization, embeddings, context windows, RAG intro, LLM landscape |
| 2026-04-23 | week03-session03.md | Doubt-Clearing — APIs, Portfolio & Assignment Walkthrough | APIs, portfolio tips, assignment walkthrough |
| Date | File | Title | Topics Discussed |
|---|---|---|---|
| 2026-04-25 | week04-session01.md | Regression, Feature Scaling & Classification Intro | Linear/logistic regression, feature scaling, classification intro |
| 2026-04-26 | week04-session02.md | Classification, Ensemble Learning & Customer Churn Practical | KNN, decision trees, random forest, ensemble methods, churn demo |
| 2026-04-30 | week04-session03.md | Doubt-Clearing — K-Means, Elbow Method & Transfer Learning | K-Means clustering, elbow method, transfer learning |
| Date | File | Title | Topics Discussed |
|---|---|---|---|
| 2026-05-02 | week05-session01.md | NLP Basics — Tokenization, Preprocessing & Classical Embeddings | Tokenization, stemming, lemmatization, POS, BoW, TF-IDF |
| 2026-05-03 | week05-session02.md | Word2Vec, Transformers (BERT/GPT), LLMs & Client LLM Selection | Word2Vec, BERT/GPT, transformer architecture, LLM selection |
| 2026-05-07 | week05-session03.md | Q&A, Prompt Engineering & AI Dashboard Demo | Prompt engineering, Q&A, AI dashboard demo |
| Date | File | Title | Topics Discussed |
|---|---|---|---|
| 2026-05-09 | week06-session01.md | Generative AI Deep Dive — LLMs, RAG, Vector DBs & Chunking | LLM internals, RAG architecture, chunking, embeddings, vector DBs |
| 2026-05-10 | week06-session02.md | Building RAG-Based Applications | LangChain RAG pipeline, FAISS, Streamlit RAG apps |
| 2026-05-14 | week06-session03.md | Doubt-Clearing — Project Reviews, Copilot Agents & LinkedIn Strategy | Project reviews, Copilot agents, LinkedIn strategy |
| Date | File | Title | Topics Discussed |
|---|---|---|---|
| 2026-05-16 | week07-session01.md | APIs vs. Open Source Models — Ollama Setup & Local RAG | API vs local models, Ollama, local RAG setup |
| 2026-05-17 | week07-session02.md | RAG vs. Fine-Tuning, Quantization & Hybrid RAG Chatbot Assignment | RAG vs fine-tuning, quantization, Hugging Face, hybrid chatbot |
| 2026-05-21 | week07-session03.md | Doubt-Clearing — Website Chatbot Assignment (RAG + Q&A) | Website chatbot assignment, RAG Q&A |
| Date | File | Title | Topics Discussed |
|---|---|---|---|
| 2026-05-30 | week08-session01.md | Choosing the Best LLM for Your Use Case | LLM selection criteria, evaluation overview |
| 2026-05-31 | week08-session02.md | Multi-Stage Metrics, RAGAS & Comparing Groq Models | BLEU/ROUGE/METEOR, RAGAS, TinyLlama vs Llama 3.2 vs Groq |
| 2026-06-04 | week08-session03.md | RAG Project Showcase & Feedback Session | Student project demos, AWS EC2 deployment tips, guardrails preview |
| Date | File | Title | Topics Discussed |
|---|---|---|---|
| 2026-06-06 | week09-session01.md | Agentic AI Concepts, Components & LangChain Tools | GenAI vs agents, ReAct, agent characteristics & components, custom LangChain tools |
| 2026-06-07 | week09-session02.md | LangChain React Agents, LangGraph & State Graphs | React agents, tool binding, LangChain vs LangGraph, nodes/edges/state graphs |
| Date | File | Title | Topics Discussed |
|---|---|---|---|
| 2026-06-13 | week10-session01.md | Introduction to CrewAI: Multi-Agent Orchestration | Crews, agents, tasks, kickoff(), report summarization & email writer projects |
| 2026-06-14 | week10-session02.md | CrewAI Content Marketing Pipeline & AWS EC2 Deployment | Multi-agent content pipeline, Streamlit deploy on EC2, nohup |
| 2026-06-18 | week10-session03.md | Interview Prep, CV Review & Intro to n8n | Resume XYZ format, RAG interview tips, n8n lead-capture workflow |
| Date | File | Title | Topics Discussed |
|---|---|---|---|
| 2026-06-20 | week11-session01.md | Lead Capturing, Social Media Automation & AI News Agent | Lead form → Sheets → Gmail, social media automation, AI news agent with memory |
| 2026-06-21 | week11-session02.md | End-to-End RAG on n8n with Pinecone & Hugging Face | n8n RAG pipeline, HF embeddings, Pinecone, Gmail auto-reply |
| Date | File | Title | Topics Discussed |
|---|---|---|---|
| 2026-06-27 | week12-session01.md | Multi-Agent Systems & Guardrails (LangGraph + n8n) | Single vs multi-agent, input/output guardrails, LangGraph & n8n implementations |
| 2026-06-28 | week12-session02.md | MCP & End-to-End Projects (LangGraph + CrewAI) | Model Context Protocol, MCP in n8n, LangGraph & CrewAI assistant projects |
| 2026-07-02 | week12-session03.md | Career Prep — Interviews, LinkedIn & Resumes | Portfolio projects, interview Qs, LinkedIn automation, resume best practices |
Contributions of any kind are welcome! You can help by:
- Fixing errors — correct inaccuracies, typos, or misleading explanations
- Adding context — fill in gaps or expand on topics that were covered too briefly
- Improving formatting — clean up code snippets, tables, or section structure
- Adding resources — link to relevant documentation, papers, tutorials, or tools mentioned in sessions
- Adding new summaries — contribute notes from sessions not yet covered
To contribute, fork the repository, make your changes, and open a pull request. If you're unsure about something, feel free to open an issue to discuss it first.
This project is licensed under the MIT License.