Hello community, Myself Heet sankesara. I am a machine learning practitioner. I've been doing it for a year. I recently learned Haskell to implement Markov Logic Networks. I found the language intuitive. It is far easier to express logic and formulas in Haskell as compared to languages like Python or R. So I decided to do some data science using it but I couldn't because there is no library like Sklearn in Python. The few libraries I found are mostly disoriented and undermanaged. I want to work on machine learning library which can be used easily and efficiently by machine learning practitioners. It would be helpful for everyone to have a dedicated machine learning library. For a practitioner, it would be easier to tweak and test the model and try different algorithms quickly. For the community, the dedicated library leads the developers to focus on it and improve it which would result in a more efficient and flexible library. The list of algorithms I am planning to implement are as follows: 1. Linear and Logistic Regression 2. Ridge Regression 3. Perceptron 4. SVM classifier and regressor (Both Linear and Non-Linear) 5. Stochastic Gradient Descent 6. K-means clustering and KNN classifier 7. Naive Bayes 8. Decision trees 9. Random Forest 10. Gradient boosting 11. Ada boost 12. Voting classifier 13. Neural Network 14. Gradient Descent, Momentum, Nesterov accelerated gradient 15. Adaptive Moment Estimation (Adam optimizer) Please consider this idea for GSoC this year. I am be happy to talk about the idea and possible algorithms that can be implemented in the upcoming summer. With regards, Heet Sankesara