https://github.com/frankiecancino/ml-tutorials

Machine Learning Tutorials in the form of Jupyter Notebooks written in Python 3.
https://github.com/frankiecancino/ml-tutorials

jupyter-notebook machine-learning python tutorial

Last synced: 13 days ago
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Machine Learning Tutorials in the form of Jupyter Notebooks written in Python 3.

• Host: GitHub
• URL: https://github.com/frankiecancino/ml-tutorials
• Owner: frankiecancino
• Created: 2020-05-09T21:04:16.000Z (over 4 years ago)
• Default Branch: master
• Last Pushed: 2024-02-17T05:48:33.000Z (10 months ago)
• Last Synced: 2024-11-23T21:16:02.437Z (about 1 month ago)
• Topics: jupyter-notebook, machine-learning, python, tutorial
• Language: Jupyter Notebook
• Homepage:
• Size: 326 KB
• Stars: 9
• Watchers: 3
• Forks: 2
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Machine Learning Tutorials

This repo consists of different Jupyter Notebooks demonstrating how to use different machine learning techniques. New notebooks will continue to be pushed, as this repo is still active. For any requests or feedback, please use GitHub Issues.

## Getting Started

Please go through the [setup steps](https://github.com/frankiecancino/ML_Tutorials/blob/master/setup.md), if you do not have Python or the dependencies installed, prior to running the code yourself.

If you are new to machine learning, I recommend going through the Jupyter Notebooks in this order:

1. [Introduction to Machine Learning](https://github.com/frankiecancino/ML_Tutorials/blob/master/Intro_to_ML.ipynb)

2. [Linear Regression](https://github.com/frankiecancino/ML_Tutorials/blob/master/linear_regression.ipynb)

3. [Logistic Regression](https://github.com/frankiecancino/ML_Tutorials/blob/master/logistic_regression.ipynb)

4. [Evaluation Metrics](https://github.com/frankiecancino/ML_Tutorials/blob/master/evaluation_metrics.ipynb)

5. [Clustering](https://github.com/frankiecancino/ML_Tutorials/blob/master/clustering.ipynb)

6. [ARIMA](https://github.com/frankiecancino/ML-Tutorials/blob/master/arima.ipynb)

7. [Generalized Linear Models](https://github.com/frankiecancino/ML-Tutorials/blob/master/generalized_linear_models.ipynb)

8. [Gradient Descent](https://github.com/frankiecancino/ML_Tutorials/blob/master/gradient_descent.ipynb)

9. [N-Grams](https://github.com/frankiecancino/ML_Tutorials/blob/master/n_grams.ipynb)

10. [Neural Attention](https://github.com/frankiecancino/ML-Tutorials/blob/master/neural_attention.ipynb)