Having trouble getting your machine learning models to make accurate predictions?
In this episode of PyPod Chronicles, we break down the biggest mistake most people make—failing to properly evaluate their models.
We’ll walk you through key metrics like MAE, RMSE, Precision, Recall, and F1-Score, so you can spot issues early and get better results.
Don’t miss this deep dive into what really makes or breaks machine learning accuracy.
👉 Thank you for allowing me to do work I find meaningful. This is my full time job so I hope you will support my work.
I’ve linked the latest episode of Code with Josh below, AI Voice Generator with Murf API & Flet.
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Along with getting access to my current Machine Learning Series, you also get exclusive access to a boat load more content such as monthly Python projects, weekly long form articles, 3 Randoms, & my complete archive!
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My New Skill Boosting eBooks
I’ve put in a ton of work to bring you two powerful eBooks that make learning data analytics simple and practical. This is for all you guys, my readers!
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Grab your copies today to boost your skills and your careers! Thank you for all your love and support in this journey.
🎬 AI Voice Generator with Murf API & Flet
Over on Code with Josh ~ HERE
Check out the video and drop a ❤️. This really helps out the channel!
📚 Other Books I’ve Found Helpful:
Python Crash Course - Here
Automate the Boring Stuff - Here
Data Structures and Algorithms in Python - Here
Python Pocket Reference - Here
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Thank you for taking the time to listen to this week’s episode!
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