Pinned Repositories
2018
Course materials for the 2018 version of the Automating GIS processes -course at the University of Helsinki, Finland
3D-Point-Cloud-Processing
Master MVA, ENS Cachan, France: 3D Point Cloud Processing. Implementation of the research article "Segmentation Based Classification of 3D Urban Point Clouds". Very large data processing techniques using kdtree (scikit-learn API), feature computations on 3D points cloud. (Python, Scikit-Learrn))
geosocial_analysis
List of python-based tools that can be used for working with and analyzing geosocial data (e.g. Twitter)
Getting_to_know_Python_Final
Training material for working with ArcPy with ArcGIS Desktop 10x
Remote_sensing
Remote sensing related lectures and tutorials
useful_commands
Contains everyday useful python commands
rsmahabir's Repositories
rsmahabir/AI-RemoteSensing
rsmahabir/alltheplaces
A set of spiders and scrapers to extract location information from places that post their location on the internet.
rsmahabir/awesome-python-chemistry
A curated list of Python packages related to chemistry
rsmahabir/carto_sdsc23
workshop on spatial data science with PySAL @ CARTO SDSC23
rsmahabir/constellate-notebooks
Example notebooks and tutorials from Constellate, the text analysis service from ITHAKA.
rsmahabir/Deep-Learning
This repository is a related to all about Deep Learning - an A-Z guide to the world of Data Science. This supplement contains the implementation of algorithms, statistical methods and techniques (in Python)
rsmahabir/DiachronicEmb-BigHistData
Tools to train and explore diachronic word embeddings from Big Historical Data
rsmahabir/energy
Predicting energy demand in major US cities based on historical weather data
rsmahabir/feature-engineering-tutorials
Data Science Feature Engineering and Selection Tutorials
rsmahabir/God-Level-Data-Science-ML-Full-Stack
A collection of scientific methods, processes, algorithms, and systems to build stories & models. This roadmap contains 16 Chapters, whether you are a fresher in the field or an experienced professional who wants to transition into Data Science & AI
rsmahabir/GPy
Gaussian processes framework in python
rsmahabir/hallucination-leaderboard
Leaderboard Comparing LLM Performance at Producing Hallucinations when Summarizing Short Documents
rsmahabir/Hands-On-Graph-Neural-Networks-Using-Python
Hands-On Graph Neural Networks Using Python, published by Packt
rsmahabir/HyperCoast
A Python package for visualizing and analyzing hyperspectral data in coastal regions
rsmahabir/MachineLearning-DeepLearning-Code-for-my-YouTube-Channel
The full collection of all codes for my Youtube Channel segregated as per topic.
rsmahabir/ML_DL_Course
rsmahabir/OpenForest
:evergreen_tree: :deciduous_tree: :palm_tree: A catalogue of open access forest datasets
rsmahabir/OpenWPM
A web privacy measurement framework
rsmahabir/osrm-backend
Open Source Routing Machine - C++ backend
rsmahabir/pandas-ai
Pandas AI is a Python library that integrates generative artificial intelligence capabilities into Pandas, making dataframes conversational
rsmahabir/py3plex
Py3plex - A multilayer complex network visualization and analysis library in python3
rsmahabir/python_for_microscopists
https://www.youtube.com/channel/UC34rW-HtPJulxr5wp2Xa04w?sub_confirmation=1
rsmahabir/region2vec-GAT
GeoAI-Enhanced Community Detection on Spatial Networks with Graph Deep Learning
rsmahabir/ridge_map
Ridge plots of ridges
rsmahabir/segment-geospatial
A Python package for segmenting geospatial data with the Segment Anything Model (SAM)
rsmahabir/sktime
A unified framework for machine learning with time series
rsmahabir/TDA-tutorial
A set of jupyter notebooks for the practice of TDA with the python Gudhi library together with popular machine learning and data sciences libraries.
rsmahabir/TweetFeed
TweetFeed collects Indicators of Compromise (IOCs) shared by the infosec community at Twitter. Here you will find malicious URLs, domains, IPs, and SHA256/MD5 hashes.
rsmahabir/UK-Electricity-Demand-Forecasting
This notebook is centered around the analysis of historical electricity demand data, with a primary focus on uncovering trends and patterns in electricity consumption. Additionally, the notebook delves into the training and testing of deep neural network algorithms to develop robust models for forecasting future electricity demand.
rsmahabir/xicorpy
Python implementation of Chatterjee's Rank Correlation, its modifications, and other offshoots