Features of this project:

1. No external library used – sklearn, tensorflow, pytorch etc…
2. Uses a fit() and predict() function approach – just like sklearn. This makes any model very easy to plug and use.
3. Properly commented code with an OOP based approach.
4. I have utilised these implementations in my own pet projects on real datasets and they work without any errors. Obviously, not optimised for speed but they are more for learning the concept and math.

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List of the algorithms:

• Supervised Learning
  • Regression Algorithms
    • Linear Regression
    • Ridge Regression
    • Lasso Regression
    • Decision Trees Regressor
    • K-Nearest Neighbors Regressor
    • Neural Networks
  • Classification Algorithms
    • Binary Logistic Regression
    • Softmax Regression
    • Decision Tree Classifier
    • Adaboost Binary Classifier
    • K-Nearest Neighbors Classifier
    • Neural Network Classifier

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• Unsupervised Learning
  • K-Means
  • Gaussian Mixture Models
  • Variational Autoencoder (In Progress)

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Github Link – https://github.com/aditya1702/Machine-Learning-and-Data-Science/tree/master/Implementation%20of%20Machine%20Learning%20Algorithms

Suggestions are always welcome. Hope you enjoy 🙂

Online Portfolio – https://adityavyas17.com