Pinned Repositories
beergame
Simulations and Jupyter Notebooks that provide an in depth analysis of the Beer Distribution Game. Advanced illustration of using BPTK to train autonomous agents to play the game, using a reinforcement learning approach.
bptk-model-library
The BPTK Model Library is a collection of System Dynamics and Agent-based models built using the Business Prototyping Toolkit for Python.
bptk_intro
Introduction to the business prototyping toolkit
bptk_py_tutorial
Tutorial for BPTK-Py, the Python simulation engine for System Dynamics & Agent based models.
machine-learning-yearning
Machine Learning Yearning book by 🅰️𝓷𝓭𝓻𝓮𝔀 🆖
mit-15-003-data-science-tools
Study guides for MIT's 15.003 Data Science Tools
stanford-cs-221-artificial-intelligence
VIP cheatsheets for Stanford's CS 221 Artificial Intelligence
stanford-cs-229-machine-learning
VIP cheatsheets for Stanford's CS 229 Machine Learning
stanford-cs-230-deep-learning
VIP cheatsheets for Stanford's CS 230 Deep Learning
theMLbook
The Python code to reproduce the illustrations from The Hundred-Page Machine Learning Book.
melkharbili's Repositories
melkharbili/beergame
Simulations and Jupyter Notebooks that provide an in depth analysis of the Beer Distribution Game. Advanced illustration of using BPTK to train autonomous agents to play the game, using a reinforcement learning approach.
melkharbili/bptk-model-library
The BPTK Model Library is a collection of System Dynamics and Agent-based models built using the Business Prototyping Toolkit for Python.
melkharbili/bptk_intro
Introduction to the business prototyping toolkit
melkharbili/bptk_py_tutorial
Tutorial for BPTK-Py, the Python simulation engine for System Dynamics & Agent based models.
melkharbili/mit-15-003-data-science-tools
Study guides for MIT's 15.003 Data Science Tools
melkharbili/stanford-cs-221-artificial-intelligence
VIP cheatsheets for Stanford's CS 221 Artificial Intelligence
melkharbili/stanford-cs-229-machine-learning
VIP cheatsheets for Stanford's CS 229 Machine Learning
melkharbili/stanford-cs-230-deep-learning
VIP cheatsheets for Stanford's CS 230 Deep Learning
melkharbili/theMLbook
The Python code to reproduce the illustrations from The Hundred-Page Machine Learning Book.
melkharbili/aistrategy
Artificial Intelligence is a core capability of the modern enterprise. It must have a sustainable and laser-like precision and efficiency.
melkharbili/Aspect-Based-Sentiment-Mining
Keywords: NLP, Text-Classification, Drug Reviews, Flask
melkharbili/awesome-datascience
:memo: An awesome Data Science repository to learn and apply for real world problems.
melkharbili/best-of-ml-python
🏆 A ranked list of awesome machine learning Python libraries. Updated weekly.
melkharbili/bpmn-js
A BPMN 2.0 rendering toolkit and web modeler.
melkharbili/coursera_How-to-Win-a-Data-Science-Competition-Learn-from-Top-Kagglers
Labs and Project from the course "How to Win a Data Science Competition: Learn from Top Kagglers"
melkharbili/cs4220-s23
Course materials for Cornell CS 4220 / Math 4260 / CS 5223 for Spring 2023.
melkharbili/CS50-AI-Python
Harvard University CS50’s Introduction to Artificial Intelligence with Python
melkharbili/data-science
:bar_chart: Path to a free self-taught education in Data Science!
melkharbili/Data-Science--All-Cheat-Sheet
melkharbili/Data-Science-Interview-Resources
A repository listing out the potential sources which will help you in preparing for a Data Science/Machine Learning interview. New resources added frequently.
melkharbili/ethereumbook
Mastering Ethereum, by Andreas M. Antonopoulos, Gavin Wood
melkharbili/FlightSurety
Udacity Blockchain Nanodegree Course 6 "Flight Surety" Project
melkharbili/linkedin_scraper
A library that scrapes Linkedin for user data
melkharbili/machine-learning
Content for Udacity's Machine Learning curriculum
melkharbili/ml-design-patterns
Source code accompanying O'Reilly book: Machine Learning Design Patterns
melkharbili/ML-Study-Guide
Minimal Machine Learning Study Plan
melkharbili/product-api
Making data science available for the user: An API-centric data science product
melkharbili/Quality_Assurance_in_poduction_process
Enable continuous learning of a quality measurement system through a holistic process and thus to automatically improve the precision of the measurement system. The solution is based on a Raspberry Pi and a camera to which I deployed a deep learning functionality that allows the object classification to be predicted directly on the edge device.
melkharbili/tuning_playbook
A playbook for systematically maximizing the performance of deep learning models.
melkharbili/udacity-deep-learning
Repo for the Deep Learning Nanodegree Foundations program.