Dude assembled an entire AI team of "data scientists" in Python and released it as open-source (completely free).

The library automates the typical DS pipeline with AI: data loading, cleaning/transformation, EDA, visualization, feature engineering. Plus, it logs every step so that everything can be assembled into a 100% reproducible pipeline (you can repeat the run exactly and understand what was done).

Timecodes from the demo:
00:00 project overview
01:32 workflow + data loading
02:10 wrangling and cleaning
03:33 insights from visualizations and chart building
04:08 feature engineering
05:00 1-hour live workshop
05:44 the Python library itself "AI Data Science Team"

GitHub: https://github.com/business-science/ai-data-science-team

👉 @Python53

Data scientists are in high demand right now: there's just too much data to analyze.

In this course, Tatev and Vae teach #Python for #DataScience.

You'll be doing projects and exploring EDA, A/B testing, BI, and more.

https://t.me/Python53 🌟

This channels is for Programmers, Coders, Software Engineers.

0️⃣ Python
1️⃣ Data Science
2️⃣ Machine Learning
3️⃣ Data Visualization
4️⃣ Artificial Intelligence
5️⃣ Data Analysis
6️⃣ Statistics
7️⃣ Deep Learning
8️⃣ programming Languages

✅ https://t.me/addlist/8_rRW2scgfRhOTc0

✅ https://t.me/Codeprogrammer

9 key concepts of artificial intelligence, explained in 7 minutes

- Tokenization
- #TextDecoding
- #PromptEngineering
- Multi Step #AI Agents
- #RAGs
- #RLHF
- #VAE
- #DiffusionModels
- #LoRA

👉 @Python53

🔅 Deep Learning: Image Recognition

📝 Learn how to design, build, and deploy a deep neural network to serve as an image recognition system.

📋 Topics: #ImageRecognition, @DeepLearning, #ComputerVision #python

https://t.me/Python53