This repository contains all the deep learning projects done as tutorial

Benchmarking computational single cell ATAC-seq methods

Data Engineering, Data Warehouse, Data Mart, Cloud Data, AWS, SAS, Redshift, S3

Python tutorial: start here if you don't know Programming

"Heart Attack Analysis" - A data science project for predicting heart attacks using machine learning on health-related data.

An interactive tool for fitting Butler-Volmer equations to potentiodynamic scan data in Jupyter Notebook. Provides corrosion parameters and user-friendly plotting with features like IR compensation and axis switching. Requires Python 3.8 (Anaconda).

We Learn and Grow 🔝

Quantity Takeoff with Jupyter Notebook

This repository contains different projects and deep learning concept notebooks. I mostly used PyTorch to develop ANN, RNN, CNN, GAN/DCGAN algorithms. I used AWS services such as Sagemaker, lambda, Restful API, EC2 and EMR during learning phase. 'Orca is deep diver dolphin, shows my honest approach to deep dive in the field of AI.

The Jupyter Notebook Programming language IDE submodule for SNU Programming Tools (2D Mode)

🚗💥 Accident damage estimation for insurance companies using the CatBoost algorithm. 📊📈 This advanced technique utilizes machine learning to provide accurate estimates for assessing damages caused by accidents. 💯💼 Enhance efficiency and accuracy in insurance claim processes with the power of CatBoost! 🤖📉

A repository for showcasing my knowledge of the JupyterNotebook programming language, and continuing to learn the language.

You can find projects about Predictive Analytics for Business. There are 6 projects in this repo.

CS 168: AI-Powered Quantitative Digital Pathology for Personalized Cancer Immunotherapy

🧠️🖥️2️⃣️0️⃣️0️⃣️1️⃣️💾️📜️ The sourceCode:JupyterNotebook category for AI2001, containing JupyterNotebook programming language datasets

Material of the master-level course "Observational Astrophysics II" at the Department of Astronomy of Stockholm University.

To Predict the Rice quality like good or bad by using different types of machine learning like logistic regression, decision tree, support vector machine classifier, random forest classifier, K-nearest neighbor's classifier, and Gaussian Naïve Bayes classifier are used.

A end to end project with with EDA done on PowerBI deployed on Heroku which predicts if the Customer would Churn or not by using Random Forests Classifier as its model

A repo that helps you to create certificates in bulk easily