Machine Learning Notebooks

This is a series of research/tutorial jupyter notebooks demonstrating basic concepts and applications of various machine learning techniques in python. Using scikit-learn, numpy, and keras we plan to cover basics like simple neural networks, preprocessing data, dimensionality reduction/visualization of high-dimensional datasets and even hyperparameter search. Check back regularly for new notebooks as we continue our research.

Notebooks

• Preprocessing Data: Learn to preprocess data using normalization and standardization on input features.
  • Pokemon classification
  • Human activity classification using mobile accelerometer data
• Hyperparameter Search: Automate the process of selecting/fine tuning the hyperparameters used in a neural network.
• Clustering and Dimensionality Reduction: Use K-means clustering that groups similar english words using GloVe embeddings.