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Epython Lab
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Welcome to Epython Lab, where you can get resources to learn, one-on-one trainings on machine learning, business analytics, and Python, and solutions for business problems. Buy ads: https://telega.io/c/epythonlab
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Every time I started a new machine learning project, I faced the same frustration.
Create folders.
Set up configs.
Prepare data directories.
Add logging.
Structure modules properly.
And before even writing the first model… I was already tired.
So I built a solution.
I created ScaffML — an automated ML project structure generator that sets up clean, scalable, production-ready machine learning architecture in seconds.
No messy folders.
No inconsistent structure.
No wasted setup time.
Just install: pip install scaffml
Generate your project, and focus on building models — not folders.
If you're working in ML, AI, or data-driven systems, this might save you more time than you think.
I’d love your feedback and suggestions to make it even better.
PyPi: https://lnkd.in/djVY4fsq
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Why Go Beats Python for Scalable Machine Learning in Production
After years of building and deploying ML-powered applications, I have reached a clear conclusion.
https://medium.com/@epythonlab/why-go-beats-python-for-scalable-machine-learning-in-production-c5f91618be97
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The core philosophy's of Golang vs Python
https://youtu.be/GiUCX5kDtc8
Join Go Dev Community @godevcommunity
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If you want to learn 𝐏𝐲𝐭𝐡𝐨𝐧 𝐟𝐨𝐫 𝐀𝐈 𝐢𝐧 𝐡𝐞𝐚𝐥𝐭𝐡𝐜𝐚𝐫𝐞 𝐟𝐫𝐨𝐦 𝐳𝐞𝐫𝐨, with real medical examples and clear thinking, now is the right time.
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𝐁𝐮𝐢𝐥𝐝𝐢𝐧𝐠 𝐀𝐈 𝐟𝐨𝐫 𝐡𝐞𝐚𝐥𝐭𝐡𝐜𝐚𝐫𝐞 𝐢𝐬𝐧’𝐭 𝐣𝐮𝐬𝐭 𝐚𝐛𝐨𝐮𝐭 𝐦𝐨𝐝𝐞𝐥𝐬. https://youtu.be/SPlCXMcUvCg
It starts with how you structure patient data.
In this video, I explain Python classes and objects using a patient-based example — the same design thinking used in real healthcare AI systems.
What I cover:
➡️ How classes act as blueprints for patient records
➡️ Why self matters when working with multiple patients
➡️ How objects store validated medical data safely
➡️ Adding behavior like feature extraction inside a class
➡️ How patient objects flow into an ML pipeline
This is the same foundation behind libraries like pandas, scikit-learn, and PyTorch.
If you’re learning Python for AI in healthcare, this concept matters more than most people realize.
🎥 Watch here: https://youtu.be/SPlCXMcUvCg
#HealthcareAI #Python #MachineLearning #DataScience #OOP #AIEngineering
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🩺 No Coding Background? You Can Still Build AI for Healthcare https://youtube.com/playlist?list=PL0nX4ZoMtjYGSy-rn7-JKt0XMwKBpxyoE&si=N8rHxnIYnZvF-WBz
Many people think AI in healthcare is only for programmers.
That’s not true.
If you can understand patient data, charts, or clinical reports, you can learn Python for Healthcare AI — even with zero coding experience.
We start from the basics:
Python from scratch (no assumptions)
Working with real healthcare datasets
Turning medical data into AI models step by step
No computer science degree required.
Just curiosity and the desire to solve real healthcare problems.
#PythonForBeginners #HealthcareAI #AIinMedicine #MedicalAI #HealthTech #DataScience #LearnPython
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Real healthcare data is never fixed.
That’s why Python developers working in healthcare rely on
*args and **kwargs.
In this tutorial, I show how they’re used in:
✔️ patient symptoms
✔️ vitals collection
✔️ ML feature preparation
🎥 Watch here 👉 https://www.youtube.com/watch?v=01GK69j4Cx8
#HealthcareAI #Python #AI #DataScience #HealthTech
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Are you struggling how to build and import your own custom module? https://www.youtube.com/watch?v=rawqnBBZb5E
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Start your Python Journey in 2026: Share to your friends
https://www.youtube.com/playlist?list=PL0nX4ZoMtjYGSy-rn7-JKt0XMwKBpxyoE
