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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6 216
When I started learning machine learning, I thought the hardest part would be choosing the right algorithm.
Random Forest?
SVM?
Neural Networks?
But very quickly I realized something unexpected.
My biggest challenges were not the models.
They were the data.
Here are some problems I kept running into:
• Missing values — Many datasets had empty fields that required careful handling.
• Messy formats — Numbers stored as text, inconsistent units, and poorly structured tables.
• Duplicate records — The same observations appearing multiple times and skewing results.
• Noisy or incorrect data — Wrong entries that could mislead the model during training.
• Unbalanced datasets — One class dominating the data and biasing predictions.
What surprised me most was this:
I spent far more time preparing data than training models.
Cleaning data
Normalizing formats
Handling missing values
Validating datasets
That experience changed how I see machine learning.
Better models help.
But better data helps even more.
Machine learning is not only about algorithms.
It is about building reliable data pipelines and high-quality datasets.
If you want a deeper explanation about this topic, this video explains the hidden cost of data quality issues in machine learning:
https://youtu.be/TdMu-0TEppM?si=YcJCIREbHabMqjxj
#MachineLearning #DataScience #AI #DataEngineering #MLOps
6 216
Python Moving Average Solved | Smooth Noisy Sensor Data (Machine Learning Preprocessing)
https://www.youtube.com/watch?v=JxF7DAaTHAA
The Problem: https://github.com/epythonlab2/AI-ML-Interview-Preparation/blob/main/problems/02-moving_average.md
6 216
Python Min-Max Normalization: Health Data Preprocessing for AI & ML (Interview Problem Solved
https://www.youtube.com/watch?v=TpGY2U6OlCQ
6 216
Go Variables and Data Types Deep Dive | Zero Values & Type Inference vs Python
https://www.youtube.com/watch?v=gCr28avlsnk
6 216
Repost from N/a
In golang, we declare variables like x := 3. Does this kind of declaration make Go dynamic typed? Why?
6 216
In the last 24 hours, there have been 422 downloads of scaffml(Professional ML Project Structure Generator) on PyPi.
PyPi: https://pypi.org/project/scaffml/
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+2
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
6 216
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
6 216
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.
