https://github.com/gibbok/ai-learning
This repository hosts multiple python projects focused on AI learning.
https://github.com/gibbok/ai-learning
ai learning machine-learning sklearn tensorflow tutorial-exercises
Last synced: 3 months ago
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This repository hosts multiple python projects focused on AI learning.
- Host: GitHub
- URL: https://github.com/gibbok/ai-learning
- Owner: gibbok
- License: mit
- Created: 2023-12-25T19:30:59.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2024-05-16T05:40:41.000Z (about 1 year ago)
- Last Synced: 2025-02-08T15:36:02.649Z (5 months ago)
- Topics: ai, learning, machine-learning, sklearn, tensorflow, tutorial-exercises
- Language: Jupyter Notebook
- Homepage:
- Size: 50.4 MB
- Stars: 2
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# AI Learning
This repository hosts multiple `python` projects focused on AI learning.
All code examples are well-commented, making them suitable for use as self-tutorials.
## Project 1
Prediction using linear regression using `sklearn` and visualization using `matplotlib` and `seaborn`.
[Visit](./experiments/project1/README.md)
## Project 2
Expenditure prediction using linear regression with `sklearn` and `matplotlib`.
[Visit](./experiments/project2/README.md)
## Project 3
Sentiment analysis using `sklearn` and `pandas`, visualization using `matplotlib`.
[Visit](./experiments/project3/README.md)
## Project 4
Data analysis with network diagrams using `networkx`, `pandas`, and `matplotlib`.
[Visit](./experiments/project4/README.md)
## Project 5
Analyze frequency and decision tree classification using `sklearn`, `matplotlib`, `pandas`, and `seaborn`.
[Visit](./experiments/project5/README.md)
## Project 6
Image classification with neural network using `tensorflow`, `matplotlib`, and `numpy`, enhanced with image augmentation techniques.
[Visit](./experiments/project6/README.md)
## Project 7
Image classification using pre-trained ResNet50 model trained on ImageNet data using `keras`.
[Visit](./experiments/project7/README.md)
### Developer Tools
To run the project:
```shell
cd experiments
poetry shell
cd ../
make p1
```
Use the `make` command to run each project. For instance, project 2 is `make p2`, and project 3 is `make p3`.
Or using Jupyter Notebook:
```shell
cd experiments/project1
jupyter notebook
```
Or open Visual Studio Code with the root folder, and press `F5` to start debugging.
To add a dependency with Poetry:
```shell
cd experiments
poetry add YOUR_DEPENDENCY
```