🧠 Building Your First Custom ChatGPT with OpenAI’s API

Summary:
A step-by-step tutorial on building a custom AI assistant using OpenAI’s GPT API, tailored to specific workflows (like a code reviewer, writing assistant, or customer support bot).

You’ll Learn:

How to set up an OpenAI API key
How to structure prompts and responses
Using function calling and retrieval augmentation
Deploying the assistant on a simple web app

Outline:

Setting up your environment (openai, dotenv, etc.)
Writing your first prompt template
Handling API responses in Python
Adding context memory (short-term conversation history)
Bonus: Deploying to Streamlit or Flask

Tutorial: Predicting House Prices in Sydney with Machine Learning.

What you’ll get: a rigorous, reproducible pipeline that goes from data acquisition (open sources), through exploratory data analysis and geospatial feature engineering (distance to beaches, train stations, CBD), to modeling with XGBoost / LightGBM, evaluation, and interpretation (SHAP). All code examples are ready to copy into a Jupyter notebook. TL;

Production-ready RAG: Investor Q&A over Financial Filings

Summary: This tutorial walks through building a Retrieval-Augmented Generation (RAG) system for a finance use case — an Investor Q&A assistant that answers questions using a company's SEC filings, earnings call transcripts, and analyst research. You'll get architecture guidance, production considerations, and runnable sample code

Beyond Accuracy: A Practical Guide to Model Evaluation Metrics.

Precision, Recall, F1-Score for classification; MAE, RMSE for regression; Perplexity for LLMs.

LLM: Prompt Optimization

Master prompt optimization for generative AI, chatbots, and search. Learn techniques, tools, and workflows that improve LLM output accuracy, relevance, and efficiency, essential skills for AI developers and prompt engineers in Sydney and beyond.

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Tutorial: Predicting House Prices in Sydney with Machine Learning.

Production-ready RAG: Investor Q&A over Financial Filings

Beyond Accuracy: A Practical Guide to Model Evaluation Metrics.

LLM: Prompt Optimization