/intro-to-machine-learning-with-pytorch

This repo helps keep track about exercises, jupyter notebooks and datasets on the introduction to machine learning (pytorch) udacity nanodegree program.

Primary LanguageJupyter NotebookMIT LicenseMIT

Machine Learning - Introduction Nanodegree Program

IMLND is a repo in which keeps track of the progress of the introduction to mahcine learning using pytorch offred by Udacity.

Table of Contents

1. Introduction to Machine Learning

2. Supervised Learning

3. Deep Learning

4. Unsupervised Learning

5. Installation

6. Run

7. Program Certificate

8. License

Introduction to Machine Learning

This section discusses the topic of What is Machine Learning?

Supervised Learning

Linear Regression.

Perceptron Algorithm.

Decision Trees.

Naive Bayes.

Support Vector Machines.

Ensemble Methods.

Model Evaluation Metrics.

Training and Tuning.

Deep Learning

Introduction to Neural Networks.

Implementing Gradient Descent.

Training Neural Networks.

Deep Learning with PyTorch.

Unsupervised Learning

Clustering.

Hierarchical and Density Based Clustering.

Gaussian Mixture Models and Cluster Validation.

Dimensionality Reduction and PCA.

Random Projection and ICA.

Installation

This project requires Python 3.6.0 and the following Python libraries installed:

Run

In a terminal or command window, navigate to the top-level project directory intro-to-machine-learning-with-pytorch/ (that contains this README) and run the following command:

jupyter notebook <your_archive>.ipynb

on any Jupyter Notebook. This will open the iPython Notebook software and project file in your browser.

Each project will be committed in the projects/ folder.

I Finally Graduated! 😊

Verify here

License

This project uses the MIT License.