Repo that relates to the Medium blog 'Keeping your ML model in shape with Kafka, Airflow' and MLFlow'
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Published by Packt, Hands-on Supervised Machine Learning with Python
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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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IBM Decision Optimization Tutorials for Python
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These samples demonstrate how to use the DOcplex library to model and solve optimization problems.
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Download market data from Yahoo! Finance's API
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Implementing a Neural Network from Scratch
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Microsoft offer a 10-week, 20-lesson curriculum all about Data Science
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PyPortfolioOpt is a library that implements portfolio optimization methods, including classical mean-variance optimization techniques and Black-Litterman allocation
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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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Find the optimal locations of your manufacturing facilities to meet your customers’ demand and reduce production costs. From Samir Saci
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Inventory Management using Python. From https://github.com/wiredtoserve
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Python basic study with Jupyter Notebook From https://github.com/wiredtoserve/datascience/tree/master/Python
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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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This handbook extensively covers time series analysis and forecasting, delving from the most fundamental methods to the state-of-the-art. The handbook was made in Python and is designed such that readers can both learn the theory and apply them to real-world problems.
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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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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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Jupyter notebooks from the scikit-learn video series Youtube link: https://www.youtube.com/playlist?list=PL5-da3qGB5ICeMbQuqbbCOQWcS6OYBr5A
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It is a Technical Analysis library useful to do feature engineering from financial time series datasets (Open, Close, High, Low, Volume). It is built on Pandas and Numpy. Project url: https://technical-analysis-library-in-python.readthedocs.io/en/latest/
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An Ultimate Compilation of AI Resources for Mathematics, Machine Learning and Deep Learning. Highly recommended!
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