ML algorithm implementations from scratch in Python

I have studied and implemented below machine learning algorihtms from Scratch in Python as partial fulfillment of the MS in Data Science degree at the University of San Francisco.

Codes are in private repos because of the academic integrity and will be provided upon request.

E-mail : emreokcular@gmail.com

  1. Linear Models
    • Linear Regression with Gradient Descent (L1 and L2 Regularization)
    • Logistic Regression with Gradient Descent (L1 and L2 Regularization)
  2. Naive Bayes
  3. Decision Trees
  4. Random Forest
    • Bootstrapping
  5. Clustering
    • K-Means
    • K-Means++
  6. Boosting
    • AdaBoost
    • Gradient Boosting with MSE
    • Gradient Boosting with PyTorch Neural Network
  7. Recommendation Engine
    • Matrix Factorization
  8. Neural Networks with PyTorch
    • Deep Neural Network
    • CNN
    • RNN
  9. NLP
    • Named Entity Recognition with PyTorch Sliding Window NN
    • Continuous Bag of Words
  10. Feature Importance and Selection
    • Spearman’s rank correlation coefficient
    • minimal-redundancy-maximal-relevance (mRMR)
    • permutation importance
    • drop column importance
    • Automatic feature selection algorithm

You can also check the collection of useful Data Science helper functions and packages that I used in my Data Science journey.