https://github.com/mohitmishra786/ml-and-dl
This repository gives beginners and newcomers in the field of AI and ML a chance to understand the inner workings of popular learning algorithms by presenting them with a simple way to analyze the implementation of ML and DL algorithms in pure python using only numpy as a backend for linear algebraic computations.
https://github.com/mohitmishra786/ml-and-dl
artificial-intelligence artificial-neural-networks basic coding deep-learning deep-neural-networks machine-learning machine-learning-algorithms machine-learning-practice machine-learning-projects ml ml-project mlops python python3 pytorch pytorch-implementation tensorflow
Last synced: 9 months ago
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This repository gives beginners and newcomers in the field of AI and ML a chance to understand the inner workings of popular learning algorithms by presenting them with a simple way to analyze the implementation of ML and DL algorithms in pure python using only numpy as a backend for linear algebraic computations.
- Host: GitHub
- URL: https://github.com/mohitmishra786/ml-and-dl
- Owner: mohitmishra786
- License: mit
- Created: 2021-11-17T17:54:06.000Z (over 4 years ago)
- Default Branch: main
- Last Pushed: 2023-06-25T08:58:37.000Z (about 3 years ago)
- Last Synced: 2025-07-11T18:49:14.061Z (12 months ago)
- Topics: artificial-intelligence, artificial-neural-networks, basic, coding, deep-learning, deep-neural-networks, machine-learning, machine-learning-algorithms, machine-learning-practice, machine-learning-projects, ml, ml-project, mlops, python, python3, pytorch, pytorch-implementation, tensorflow
- Language: Jupyter Notebook
- Homepage:
- Size: 10.8 MB
- Stars: 3
- Watchers: 1
- Forks: 4
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# Machine Learning & Deep Learning
### Install
This project requires **Python** and the following Python libraries installed:
- [NumPy](http://www.numpy.org/)
- [Pandas](http://pandas.pydata.org/)
- [matplotlib](http://matplotlib.org/)
- [scikit-learn](http://scikit-learn.org/stable/)
You will also need to have software installed to run and execute a [Jupyter Notebook](http://jupyter.org/install.html).
If you do not have Python installed yet, it is highly recommended that you install the [Anaconda](https://www.anaconda.com/download/) distribution of Python, which already has the above packages and more included.
### Code
Template code is provided in the `boston_housing.ipynb` notebook file. You will also be required to use the included `visuals.py` Python file and the `housing.csv` dataset file to complete your work. While some code has already been implemented to get you started, you will need to implement additional functionality when requested to successfully complete the project. Note that the code included in `visuals.py` is meant to be used out-of-the-box and not intended for students to manipulate. If you are interested in how the visualizations are created in the notebook, please feel free to explore this Python file.
### Run
In a terminal or command window, navigate to the top-level project directory `boston_housing/` (that contains this README) and run one of the following commands:
```bash
ipython notebook any_notebook.ipynb
```
or
```bash
jupyter notebook any_notebook.ipynb
```
or open with Juoyter Lab
```bash
jupyter lab
```
This will open the Jupyter Notebook software and project file in your browser.
### Data
Dataset are already included inside the model file