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NFL Season is coming soon so it seems apt to create a machine learning model that can predict winning Daily Fantasy Sports players. In this AI tutorial, i'll explain how to use Python and Machine Learning to predict the winning NFL players. We'll also use this opportunity to learn about model training, deployment, and continuous learning. What does it look like when a model gets new data daily, retrains itself, and makes new predictions?

Betting Bot Code for this video:
https://github.com/llSourcell/Fantasy_Football_Predictions

Deployment code:
https://github.com/testdrivenio/fastapi-ml

Airflow code:
https://medium.datadriveninvestor.com/machine-learning-orchestration-using-apache-airflow-beginner-level-e4939492568c

ML Ops Builder Tool:
https://mymlops.com/builder

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I Built a Trading bot with Bing and ChatGPT:
https://youtu.be/5G1_zRME_pY

I Built a Sports Betting Bot with ChatGPT:
https://www.youtube.com/watch?v=IDthta5sUGQ

I Built a Trading Bot with ChatGPT:
https://www.youtube.com/watch?v=fhBw3j_O9LE

Watch ChatGPT Build an AI Startup:
https://www.youtube.com/watch?v=hL2hLFUwuqQ Sign up for my AI Sports betting Bot, WagerGPT! (500 spots available):
https://www.wagergpt.co