tahirdmello's repositories
LIDAR-Height-Extractor
Implementation of an algorithm using Python to remotely extract building heights by combining LIDAR (Light Detection and Ranging) datasets and building shapefiles.
Single-Cell-RNA-Sequence-Data-Analysis
Created an R pipeline using Bioconductor and Seurat to preprocess and clean scRNA sequence datasets, perform Principal Component Analysis and identify anomalous differential gene expression. [ONGOING]
Bike-Sharing-Data-Analysis
This project is an exploratory data analysis of the Bay Area Bike Share using basic data analysis and data visualization tools and packages in Python.
Convoluted-Neural-Network-Malaria-Detection
Built a deep learning Convoluted Neural Network to differentiate between images of healthy and malaria infected cells.
deep-learning-coursera
Deep Learning Specialization by Andrew Ng on Coursera.
higher-order-organization-matlab
Experimental Matlab code for methods and some examples in "Higher-order organization of complex networks"
KNN-Categorization-Iris-Flower
Built a supervised machine learning model (KNN model) from scratch and implemented it on data of different types of iris flowers to categorize them by sepal and petal form and colour.
LSTM-Stock-Price-Predictor
EE626 Course Project on LSTM based Stock Price Prediction for Closing Price Data
mctools
Motif Clustering Tools for Complex Networks
ML-From-Scratch
Machine Learning From Scratch. Bare bones NumPy implementations of machine learning models and algorithms with a focus on accessibility. Aims to cover everything from linear regression to deep learning.
models
Models and examples built with TensorFlow
opencv
Open Source Computer Vision Library
Principal-Component-Analysis-Gene-Cancer-Markers
Categorize acute myeloid leukemia (AML) and acute lymphoblastic leukemia (ALL) using Principal Component Analysis and sorting algorithms on datasets of gene sequencing. [ONGOING]
pydicom
Read, modify and write DICOM files with python code
pyica
python code for Independent Component Analysis
pytorch
Tensors and Dynamic neural networks in Python with strong GPU acceleration
Random-Forest-Heart-Disease-Classification
The basic goal of this Machine Learning project is to predict the absence (0) or presence (1) of heart disease being suffered by the patients using a Random Tree algorithm on 14 physiological attributes. Dataset obtained from: https://www.kaggle.com/ronitf/heart-disease-uci
scikit-image
Image processing in Python
scikit-learn
scikit-learn: machine learning in Python
signnet
R package for signed networks
Summer-Analytics-2019
Assignments for Summer Analytics 2019, Consulting and Analytics Club, IIT Guwahati
tahirdme.github.io
Github Pages template for academic personal websites, forked from mmistakes/minimal-mistakes