Suresha Parashivamurthy's Projects
Learn how to responsibly deliver value with ML.
Codes related to various ML Hackathons
ML-AI Community | Open Source | Built in Bharat for the World | Data science problem statements and solutions
🎓 Sharing course notes on all topics related to machine learning, NLP, and AI.
12 weeks, 24 lessons, classic Machine Learning for all
Prediction of Crop Yield for farmers based on weather, satellite data
Fun machine learning stuff
Time Series Decomposition techniques and random forest algorithm on sales data
Deep Learning and Finite Element Method for Physical Systems Modeling
A project-based course on the foundations of MLOps with a focus on intuition and application.
MLOps examples
Create a fully automated, end-to-end IRIS Training and Deployment using Azure MLOps
Free MLOps course from DataTalks.Club
Python package for implementing a number of Machine Learning, Randomized Optimization and SEarch algorithms.
This repository contains a throughout explanation on how to create different deep learning models in Keras for multivariate (tabular) time series prediction.
Natural Language Processing Tutorials(NLP) with Julia and Python
:new: Demo code for the Natural Language Understanding Service.
Gathers machine learning and Tensorflow deep learning models for NLP problems, 1.13 < Tensorflow < 2.0
Scikit-Learn, NLTK, Spacy, Gensim, Textblob and more
OpenMDAO repository.
Highly cited and useful papers related to machine learning, deep learning, AI, game theory, reinforcement learning
Fuzzy string matching, grouping, and evaluation.
Forecasted product sales using time series models such as Holt-Winters, SARIMA and causal methods, e.g. Regression. Evaluated performance of models using forecasting metrics such as, MAE, RMSE, MAPE and concluded that Linear Regression model produced the best MAPE in comparison to other models
Programs
Curated list of project-based tutorials
An open-source, low-code machine learning library in Python
Getting start with PySpark and MLlib
All Algorithms implemented in Python