AI/ML From Scratch

A "Pa-ir-ent Programming Adventure with my (now) 13-yo, exploring concepts in machine learning and artificial intelligence from concepts and tools to hands-on projects. The goal is use freely available tools and resources, and publish our learnings in a blog for later recall, and to help others.

Plan

The plan is to approach this on three levels:

Fundamentals = learn basic terminology and concepts for machine learning and artificial intelligence by doing relevant courses or exploring online resources.
Projects = apply learning by doing relevant projects using various hardware and software platforms available, starting with MIT Scratch and Micro:Bit and building up to Python, JavaScript and RaspberryPi/Arduino.
Website = write up our learnings in regular blog posts and tutorials on an associated website. This helps reinforce learning and potentially helps others following similar journeys.

We'll look at this in three sections:

Fundamentals = learning the basics of machine learning and AI from concepts to code
Projects = creating meaningful projects to help us learn by doing - using
Tutorials = sharing our insights with others by writing blog posts and creating step-by-step tutorials

Checkout the Resources page for useful links. Checkout the Setup page to learn how our blog/website was setup.

HAPPY LEARNING TRAILS! 🤖

AI, ML & Competitive Programming's Projects

azure-openai-demos

Azure Open AI (demos, documentation, accelerators).

azure-search-openai-demo

A sample app for the Retrieval-Augmented Generation pattern running in Azure, using Azure Cognitive Search for retrieval and Azure OpenAI large language models to power ChatGPT-style and Q&A experiences.

chat-with-your-data-solution-accelerator

A Solution Accelerator for the RAG pattern running in Azure, using Azure Cognitive Search for retrieval and Azure OpenAI large language models to power ChatGPT-style and Q&A experiences. This includes most common requirements and best practices.

deep-learning-ai

Exploring use of GitHub Codespaces with Jupyter Notebooks - and the iJavaScript kernel

label-studio-examples

Example Code to Supplement the Label Studio Blog

learn_prompting

Prompt Engineering, Generative AI, and LLM Guide by Learn Prompting | Join our discord for the largest Prompt Engineering learning community

lida-codespaces

Codespaces environment for exploring projects using Microsoft Lida

notebooks

Notebooks using the Hugging Face libraries 🤗

prompt-me

Exploring Generative AI, LLM, OpenAI models and Prompt Engineering - a #100DaysOfPrompts journey

prompts-for-edu

Prompts for Education - Focus on Students, Educators and Administrators

responsible-ai-toolbox

Responsible AI Toolbox is a suite of tools providing model and data exploration and assessment user interfaces and libraries that enable a better understanding of AI systems. These interfaces and libraries empower developers and stakeholders of AI systems to develop and monitor AI more responsibly, and take better data-driven actions.