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ML Demos

A collection of implementions for an assortment of machine learning and deep learning algorithms with basic overviews/explanations written using Jupyter notebooks. A list of implemented algorithms can be found below.

The purposes of these implementations is to emphasize familiarity with the built-in functions available in the scikit-learn and PyTorch libraries, so they only make use of the scikit-learn built-in.

List of Implmented Algorithms:

ML_demos/basic:

  • Regression.ipynb: linear and logistic regression
  • naiveBayes.ipynb: Naive Bayes
  • SVM.ipynb: support vector machine
  • kNN.ipynb: k-nearest neighbors
  • Trees.ipynb: decision tree and random forest
  • Clustering.ipynb: k-Means and DBScan
  • XGBoost.ipynb: xgboost
  • fromScratch.ipynb: various ML evaluation metrics, sampling, kNN, and feed-forward neural network from scratch (coding interview practice exercise)

ML_demos/deep_learning:

  • DNN.ipynb: simple multilayer perceptron implementation, implement a basic neural network
  • CNN.ipynb: implements a basic CNN
  • CNN_architectures.ipynb: implements some known CNN achitectures including LeNet, AlexNet, VGG, and ResNet
  • RNN.ipynb: implements an RNN with LSTM for time series data

ML_demos/reinforcement_learning:

To implement:

  • General deep learning
    • Transformer
    • GAN
    • Autoencoder
  • RL algorithms