docs: setup readme

Author: lpm0073

README.md

@@ -1,75 +1,24 @@
-# Azure Machine Learning - Automated ML Example
+# Jupyter Notebook AI Algorithm Examples
 
-[![AzureML](https://a11ybadges.com/badge?logo=microsoft)](https://azure.microsoft.com/en-us/products/machine-learning/)
-[![Python](https://a11ybadges.com/badge?logo=python)](https://www.python.org/)<br>
-[![Unit Tests](https://github.com/FullStackWithLawrence/ai-algorithms/actions/workflows/test.yml/badge.svg?branch=main)](https://github.com/FullStackWithLawrence/ai-algorithms/actions/workflows/test.yml)
-![Release Status](https://github.com/FullStackWithLawrence/ai-algorithms/actions/workflows/release.yml/badge.svg?branch=main)
-![Auto Assign](https://github.com/FullStackWithLawrence/ai-algorithms/actions/workflows/auto-assign.yml/badge.svg)
+[![Python](https://a11ybadges.com/badge?logo=python)](https://www.python.org/)
+[![Jupyter](https://a11ybadges.com/badge?logo=jupyter)](https://docs.jupyter.org/en/latest/)<br>
 [![License: AGPL v3](https://img.shields.io/badge/License-AGPL_v3-blue.svg)](https://www.gnu.org/licenses/agpl-3.0)
 [![hack.d Lawrence McDaniel](https://img.shields.io/badge/hack.d-Lawrence%20McDaniel-orange.svg)](https://lawrencemcdaniel.com)
 
-Demonstrate basic usage of Azure Machine Learning's [Automated ML](https://azure.microsoft.com/en-us/solutions/automated-machine-learning) service. Implements the following:
+Demonstrate basic AI algorithms implemented using Python, including:
 
-- `AzureAIMLWorkspace`: Azure AI ML Studio workspace helper class. generates an authenticated instance of a workspace.
-- `AzureAIMLAssetsDataset`: Azure AI ML Studio data set. Provides helpers for managing ML Studio data sets, and for porting to/from kaggle data sets and local csv and Excel files.
-- `AzureAIMLStudioComputeCluster`: Azure AI ML Studio Compute - compute cluster object with helpers for instantitation.
-- `AzureAIMLStudioAssetsBatchEndpoint`: Azure AI ML Studio Assets - batch end point. Providers helpers for managing end points.
-- `AzureAIMLStudioAuthoringAutomatedML`: Azure AI ML Studio Authoring - Automated ML. Helper class for managing life cycle of 'automated ml' jobs.
+- Anomaly Detection: credit card detection using data from Kaggle
+- Regression: House price forecasting tool using data from Kaggle
+- K-Means: Cluster example using the Iris dataset from Kaggle
 
-There is also an [example production deployment](./titanic-survival-app/) of a model created using AutomatedML. See important deployment details, [here](./docs/AZURE_DEPLOYMENT.md).
+## Quickstart
 
-Note the following:
+1. Verify project requirements: [Python 3.13](https://www.python.org/)
 
-3. This project leverages [Github Actions](https://github.com/features/actions) for automated unit tests, build, deploy, in addition to automating various administrative tasks including for example, automating updates to 3rd party package requirements. Most of these are visible from the [Actions](https://github.com/FullStackWithLawrence/ai-algorithms/actions) tab above.
+2. Run `make init` 
 
-## Usage
+3. Run `source venv/bin/activate`
 
-```console
-python3 -m azure_ai.commands.help
-python3 -m azure_ai.commands.workspace
-python3 -m azure_ai.commands.compute_cluster cluster-name
-python3 -m azure_ai.commands.dataset_from_file maths ~/Desktop/gh/fswl/ai-algorithms/azure_ai/tests/data/maths.csv
-python3 -m azure_ai.commands.dataset_from_kaggle titanic heptapod/titanic
-```
-
-## Setup
-
-Works with Linux, Windows and macOS environments.
-
-1. Verify project requirements: [Python 3.13](https://www.python.org/), [NPM](https://www.npmjs.com/) [Docker](https://www.docker.com/products/docker-desktop/), and [Docker Compose](https://docs.docker.com/compose/install/). Docker will need around 1 vCPU, 2Gib memory, and 30Gib of storage space.
-
-2. Run `make` and add your credentials to the newly created `.env` file in the root of the repo.
-
-3. Create an Azure account. See this summary for account configuration important details that you'll need to address in order for Azure AI AutomatedML batch jobs to run correctly: [Azure Account Setup for AutoML](./docs/AZURE_ACCOUNT_SETUP.md)
-
-4. Add your Azure `config.json` to the root of this project. See [Azure ML Configuration Guide](./docs/AZURE_ML_CONFIG.md) for detailed instructions on setting up an Azure Workspace and Subscription, and downloading your `config.json` file.
-
-5. Add your Kaggle Api key to [.kaggle/kaggle.json](./.kaggle/kaggle.json), which you can generate from [https://www.kaggle.com/settings/account](https://www.kaggle.com/settings/account).
-
-6. Install and configure Azure cli. On Mac `brew install azure-cli`. On Windows download from [https://aka.ms/installazurecliwindows](https://aka.ms/installazurecliwindows).
-
-   - `az login`
-   - `az account list --output table`
-   - `az account set --subscription "your-subscription-id-or-name"`
-
-7. Initialize, build and run the application locally.
-
-```console
-git clone https://github.com/FullStackWithLawrence/ai-algorithms.git
-make                # scaffold a .env file in the root of the repo
-                    #
-                    # ****************************
-                    # STOP HERE!
-                    # ****************************
-                    # Review your .env file located in the project root folder.
-                    #
-make init           # Initialize Python virtual environment used for code auto-completion and linting
-make test           # Verify that your Python virtual environment was built correctly and that
-                    # azureml.core finds your config.json file.
-                    #
-make docker-build   # Build and configure all docker containers
-make docker-run     # Run docker container
-```
 
 ## Support