ScratchML

Bite-sized machine learning models built using Python, Numpy, Scikit-Learn, and TensorFlow - with practical examples to solidfy the theory behind the models. Use these Jupyter (and Colaboratory) notebooks as learning resources to discover the inner workings of essential predictive algorithms.

Installation

$ git clone https://github.com/jissac/ScratchML
cd ScratchML

Models

Predictive models used in this repository:

Logistic Regression

We'll build a logistic regression model from scratch in Python and use it in a binary classification problem - predicting if an image contains a cat or a dog:
Notebook
Blog Post

Convolutional Neural Nets

We'll then extend the binary image cat vs. dog classification task to a CNN architecture:
Notebook
Blog Post

Support Vector Machines

We'll use the Iris dataset to understand how SVMs can be used for classification
Notebook

We'll extend the application of SVMs on the Gender Recognition by Voice dataset
Notebook

Decision Trees

We'll use the basic Iris dataset to understand how Decision Trees can be used for classification
Notebook

K-Means and DBSCAN

Blog Post
Notebook

Generative Adversarial Networks

We'll build a GAN trained on the Fashion dataset.
Notebook

Datasets

Datasets used in this repository.

Gender Recognition by Voice

A collection of recorded voice samples from male and female speakers. Hosted by Kaggle

Fashion

Articles of clothing classified into 10 labels. Provided by Zalando Research. Hosted by Kaggle

Chest X-Rays of Pneumonia

Chest X-Ray images of patients with/without pneumonia. Hosted by Kaggle

Cats vs. Dogs

A subset of data from the Microsoft Research team hosted by Kaggle with images that contain either cats or dogs.

Iris Dataset

A classic dataset comparing the structural variation of three related Iris flower species - Iris-setosa, Iris-Versicolor, and Iris-Virginica.