AjayK's starred repositories

handson-ml2

A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in Python using Scikit-Learn, Keras and TensorFlow 2.

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EMIT-Data-Resources

This repository provides guides, short how-tos, and tutorials to help users access and work with data from the Earth Surface Mineral Dust Source Investigation (EMIT) mission.

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machine-learning-book

Code Repository for Machine Learning with PyTorch and Scikit-Learn

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applied-llm

Everything about LLMs in production.

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LLM101n

LLM101n: Let's build a Storyteller

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path-to-senior-engineer-handbook

All the resources you need to get to Senior Engineer and beyond

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Landsat-Time-Series-Analysis-for-Multi-Temporal-Land-Cover-Classification

LANDSAT Time Series Analysis for Multi-temporal Land Cover Classification using Machine Learning techniques in Python and GUI development for automation of the process.

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sentinelsat

Search and download Copernicus Sentinel satellite images

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satellite-image-analysis

An introduction to script-based satellite image processing using Python

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Satellite_Imagery_Analysis

Implementation of Machine Learning and Deep Learning techniques to find insights from the satellite data.

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ThinkStats2

Text and supporting code for Think Stats, 2nd Edition

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earthaccess

Python Library for NASA Earthdata APIs

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algorithmic-trading-with-python

Source code for Algorithmic Trading with Python (2020) by Chris Conlan

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zipline-reloaded

Zipline, a Pythonic Algorithmic Trading Library

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Statistical-Time-Series-Analysis-on-Agricultural-Commodity-Prices

This project aims to understand trends in agricultural prices through quantity arrival data for different commodities in state of Maharashtra, India.

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pyfolio

Portfolio and risk analytics in Python

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Quantitative

Alpha Generation using Data Science and Quantitative Analysis with integrated Risk Model

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you-dont-need-a-bigger-boat

An end-to-end implementation of intent prediction with Metaflow and other cool tools

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from-pytorch-to-coreml

A simple guide that explains the steps from training a simple PyTorch image classifier to converting the generated neural network into a CoreML model ready for production

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sources-of-reinforcement-learning

All the source codes and lectures of reinforcement learning.

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