Dhiraj Mahato's repositories
AI-ML-cheatsheets
Cheatsheets for AI and Machine Learning
Cheatsheet
Cheatsheet
Data_Engineering_module
ETL pipelines and other implementations
Foundation_of_Data_Science_IIMB
"Learn the fundamental concepts in probability, statistics, optimization and linear algebra which form the foundations for data science"
Predictive_Analytics_IIMB
Predictive analytics is emerging as a competitive strategy across many business sectors and can set apart high performing companies. It aims to predict the probability of the occurrence of a future event such as customer churn, loan defaults, and stock market fluctuations – leading to effective business management.
Cloud_Platforms
Cloud infra setup code
Dashboard_Panel
Interactive visualization dashboard in Python with Panel
DataScience_HandBook
A quick guide to Data Science, DBMS, and AWS concepts
dm_streamlit
Personal Project Portfolio
DS
Data Science Usecases
generative-ai-for-beginners
12 Lessons, Get Started Building with Generative AI 🔗 https://microsoft.github.io/generative-ai-for-beginners/
indian-district-boundaries
Districts of India
INDIAN-SHAPEFILES
Shape files and Geojson files for various categories of India.
Machine-Learning-course-notebook
The full collection of Jupyter Notebook labs from Andrew Ng's new Machine Learning Specialization.
Machine-Learning-Notebooks
Machine Learning notebooks for refreshing concepts.
machine_learning_complete
A comprehensive machine learning repository containing 30+ notebooks on different concepts, algorithms and techniques.
ML-Cheatsheet
These are my notes for machine learning
playnext
recomendation for the next track
PracticalDataScience-ENCA
The goal of this project is to recommend songs for a given playlist from Spotify. This project starts from data collection all the way to model deployment to ensure you have a working model to showcase.
pyspark
A quick reference guide to the most commonly used patterns and functions in PySpark SQL
Spatial_data_science
Spatial data science (SDS) is a subset of Data Science that focuses on the unique characteristics of spatial data SDS treats location, distance & spatial interactions as core aspects of the data using specialized methods & software to analyze, visualize & apply learnings to spatial use cases.
webscrapping
Different jupyter notebook code to scrap different websites