An Ultimate Compilation of AI Resources for Mathematics, Machine Learning and Deep Learning. Highly recommended!
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HTTP(S) benchmark tools, testing/debugging, & restAPI (RESTful)
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A collection of resources for pandas (Python) and related subjects.
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Sample and simple code sharing for Capital Budgeting with Monte Carlo Simulation
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Set of Jupyter notebook supporting articles on https://medium.com/@vdekanovsky
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IPython notebooks with demo code intended as a companion to the book: Data Driven Science & Engineering: Machine Learning, Dynamical Systems, and Control by S. L. Brunton and J. N. Kutz http://databookuw.com/
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Microsoft offer a 10-week, 20-lesson curriculum all about Data Science
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These samples demonstrate how to use the DOcplex library to model and solve optimization problems.
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A summary from a Chinese author "bitcarmanlee". Chinese contents. Please go to the author's link https://github.com/bitcarmanlee/easy-algorithm-interview-and-practice
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Get power generation from ZeverSolar Inverter and present it to front end as a sensor for Hass.IO
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Published by Packt, Hands-on Supervised Machine Learning with Python
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IBM Decision Optimization Tutorials for Python
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Repo that relates to the Medium blog 'Keeping your ML model in shape with Kafka, Airflow' and MLFlow'
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Inventory Management using Python. From https://github.com/wiredtoserve
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sql magic for IPython, hopefully evolving into full SQL client. http://catherinedevlin.blogspot.com/
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Jupylet is a Python library for programming 2D and 3D games, graphics, music and sound synthesizers, interactively in a Jupyter notebook. It is intended for three types of audiences: Computer scientists, researchers, and students of deep reinforcement learning. Musicians interested in sound synthesis and live music coding. Kids and their parents interested in learning to program.
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Lean Six Sigma (LSS) is a method based on a stepwise approach to process improvements.This approach usually follows 5 steps (Define, Measure, Analyze, Improve and Control) for improving existing process problems with unknown causes. source: https://github.com/samirsaci/lss-logistic-regression
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ML for Trading - 2nd Edition This book aims to show how ML can add value to algorithmic trading strategies in a practical yet comprehensive way. It covers a broad range of ML techniques from linear regression to deep reinforcement learning and demonstrates how to build, backtest, and evaluate a trading strategy driven by model predictions.
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MachineLearningStocks is designed to be an intuitive and highly extensible template project applying machine learning to making stock predictions. Source from: https://github.com/robertmartin8/MachineLearningStocks Caution!!! As of Feb 2021: MachineLearningStocks is no longer actively maintained
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Build a simple methodology of Supply Chain Network Design that is considering the fluctuation of the demand.
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