Repo that relates to the Medium blog 'Keeping your ML model in shape with Kafka, Airflow' and MLFlow'

Updated 2023-08-18 20:13:08 +08:00

Published by Packt, Hands-on Supervised Machine Learning with Python

Updated 2023-08-18 20:10:53 +08:00

Get power generation from ZeverSolar Inverter and present it to front end as a sensor for Hass.IO

Updated 2023-08-18 19:53:19 +08:00

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.

Updated 2023-09-15 11:15:00 +08:00

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

Updated 2023-08-15 18:51:26 +08:00

PyPortfolioOpt is a library that implements portfolio optimization methods, including classical mean-variance optimization techniques and Black-Litterman allocation

Updated 2023-09-15 11:15:00 +08:00

Build a simple methodology of Supply Chain Network Design that is considering the fluctuation of the demand.

Updated 2023-09-15 11:15:00 +08:00

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

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Python basic study with Jupyter Notebook From

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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:

Updated 2023-09-15 11:16:05 +08:00

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.

Updated 2023-08-18 19:45:59 +08:00

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

Updated 2023-08-18 19:52:40 +08:00

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.

Updated 2023-09-15 11:16:05 +08:00

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:

Updated 2023-09-15 11:16:05 +08:00