Abdur Rasheed's starred repositories
thunderbird-android
K-9 Mail – Open Source Email App for Android
CtCI-6th-Edition-Python
Cracking the Coding Interview 6th Ed. Python Solutions
python_for_microscopists
https://www.youtube.com/channel/UC34rW-HtPJulxr5wp2Xa04w?sub_confirmation=1
matplotlib_for_papers
Handout for the tutorial "Creating publication-quality figures with matplotlib"
Python-for-Probability-Statistics-and-Machine-Learning
Jupyter Notebooks for Springer book "Python for Probability, Statistics, and Machine Learning"
Machine-Learning-Projects
Machine Learning Experiments and Work
Machine-Learning-Projects
This repository consists of all my Machine Learning Projects.
house-price-prediction
Predicting house prices using Linear Regression and GBR
Machine-Learning-Projects
Various projects in Linear Regression, Logistic Regression, k Nearest Neighbors, Decision Trees, Random Forests, SVM
dsa-using-python
codes related to my dsa course on python
machineintelligence
An Introduction to Machine Intelligence for Architects and other Nonengineers
machineLearning
A repo for all the relevant code notebooks and datasets used in my Machine Learning tutorial videos on YouTube
WildFireDetection
Using U-Net Model to Detect Wildfire from Satellite Imagery
simple-Linear-Regression
Simple Linear implementation with python
ipl-win-probability-predictor
A machine learning project to find out the win probability of an IPL match
Computer-Vision
Computer Vision, often abbreviated as CV, is defined as a field of study that seeks to develop techniques to help computers “see” and understand the content of digital images such as photographs and videos.Here i am making some code for you to easily learn computer vision
Reliability
A Python class for Reliability analysis including Monte Carlo and FORM methods
HCM_Nat-Cardiovasc-Res
Predicting the survival outcomes of HCM patient using Random Survival forests（RSF）
Image-Denoising-_-with-Jupyter-notebook
This repository contains my favourite examples of Image Denoising for microscopy/biology images by using Python and in particular Jupyter notebook. Full credit goes to Dr. Sreenivas Bhattiprolu, who created this examples in the first place; you can find his .py files at https://github.com/bnsreenu/python_for_microscopists. This repository is just a selection of my favourite denoising algorithms illustrated by Dr. Bhattiprolu. I provide Jupyter notebook files, original images to upload and PDF previews so that any person can have fun using these algorithms. Installation guidelines of Jupyter notebook (via Anaconda installation) are also provided.
Machine-Learning-Model-Comparasion-for-Wilfire-Burn-Area
Compares SVR, DNN, Decision Tree, and Random Forest Regressor using dataset from UCI. Sees which machine learning method would be most effective for firefighters to utilize in order to correctly predict burn area of wildfires.