AiLearn - My AI Learning Project

This repository contains my projects and experiments as I learn about artificial intelligence and machine learning. Thanks for the bilibili video: https://www.bilibili.com/video/BV1VZMLzMEUY

Projects:

1. Linear Regression Demo This demo shows a simple linear regression model using scikit-learn on the diabetes dataset.

Files

• linear_regression_demo.py: Python script implementing the linear regression model
• requirements.txt: Dependencies for the project

Usage

Run:

cd linear_regression && pip install -r requirements.txt && python linear_regression_demo.py

2. Logistic Regression Iris Demo This demo shows a logistic regression model for iris flower classification using scikit-learn.

Files

• logistic_regression_demo_iris_classification.ipynb: Jupyter notebook implementing the logistic regression model
• requirements.txt: Dependencies for the project

Usage

Run:

cd logistic_regression_iris && pip install -r requirements.txt && jupyter notebook logistic_regression_demo_iris_classification.ipynb

3. Logistic Regression Exam Demo This demo shows a logistic regression model to predict exam pass/fail based on Exam1 and Exam2 scores, comparing linear and quadratic decision boundaries.

Files

• logistic_regression_demo_exam.ipynb: Jupyter notebook implementing the logistic regression model
• examdata.csv: Dataset containing exam scores and pass/fail results

Usage

Run:

cd logistic_regression_exam && jupyter notebook logistic_regression_demo_exam.ipynb

4. 2D Data Cluster Classifier Demo
   This project demonstrates clustering and classification algorithms on 2D data, including KMeans, KNN, MeanShift, and DBSCAN, with visualization and accuracy evaluation.

Files

• 2Ddata_cluster classifier/demo.ipynb: Jupyter notebook with code and visualizations
• 2Ddata_cluster classifier/data.csv: Example dataset (not included, user should provide)
• 2Ddata_cluster classifier/README.md: Project-specific instructions

Usage

Run:

cd "2Ddata_cluster classifier" && jupyter notebook demo.ipynb

See the project README for details on requirements and usage.

5. Anomaly Detection Demo This project demonstrates anomaly detection techniques on sample data.

Files

• anomaly_detection/demo.ipynb: Jupyter notebook with code and visualizations
• anomaly_detection/anomaly_data.csv: Dataset for anomaly detection

Usage

Run:

cd anomaly_detection && jupyter notebook demo.ipynb

6. Decision Tree Demo This project demonstrates decision tree algorithms and visualizations.

Files

• decision_tree/demo.ipynb: Jupyter notebook with decision tree implementation

Usage

Run:

cd decision_tree && jupyter notebook demo.ipynb

7. PCA Demo This project demonstrates Principal Component Analysis (PCA) for dimensionality reduction.

Files

• PCA/demo.ipynb: Jupyter notebook with PCA implementation

Usage

Run:

cd PCA && jupyter notebook demo.ipynb