In this episode of PyPod Chronicles, we’re breaking down Decision Trees, Random Forests, and XGBoost in a way that actually makes sense.
These machine learning models help solve real-world problems, like figuring out if someone might default on a loan. We’ll talk about when to use each one, how they make predictions better, and why they’re so important in data science and AI.
Whether you're just getting started or already working with data, this episode will give you a clear understanding of how these powerful tools work.
Give it a listen and level up your machine learning skills.
👉 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, Python Flet App – Build Desktop & Mobile Apps with One Codebase
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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!
📘 The Data Analytics Playbook – A step-by-step guide that takes you from beginner to confident data analyst. No fluff, just real-world examples, hands-on Python code, and everything you need to actually use data analytics.
📗 SQL Meets Python – Learn how to combine SQL and Python to handle real-world data projects with ease. Whether you’re querying databases or automating analysis, this book makes it easy to connect the two and get results fast.
Both books are packed with practical lessons and real examples—so you can skip the frustration and start building real skills.
Grab your copies today to boost your skills and your careers! Thank you for all your love and support in this journey.
🎬 Python Flet App – Build Desktop & Mobile Apps with One Codebase
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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