🥇 This repo is like gold for every data scientist!

✅ Just open your browser; a ton of interactive exercises and real experiences await you. Any question about statistics, probability, Python, or machine learning, you'll get the answer right there! With code, charts, even animations. This way, you don't waste time, and what you learn really sticks in your mind!

⬅️ Data science statistics and probability topics
⬅️ Clustering
⬅️ Principal Component Analysis (PCA)
⬅️ Bagging and Boosting techniques
⬅️ Linear regression
⬅️ Neural networks and more...


┌ 📂 Int Data Science Python Dash
└ 🐱 GitHub-Repos

👉 @codeprogrammer

#Python #OpenCV #Automation #ML #AI #DEEPLEARNING #MACHINELEARNING #ComputerVision

Want to learn Python quickly and from scratch? Then here’s what you need — CodeEasy: Python Essentials

🔹Explains complex things in simple words
🔹Based on a real story with tasks throughout the plot
🔹Free start

Ready to begin? Click https://codeeasy.io/course/python-essentials 🌟

👉 @DataScience4

Must watch "AI Engineer YouTube Playlist"

1. Neural Networks Zero to Hero (Karpathy) - https://lnkd.in/gBVSQqFf

2. Language Modelling from Scratch (Stanford CS336 2025) - https://lnkd.in/guuhQ8gA

3. Introduction to Deep Learning (MIT 6.S191 2025) - https://lnkd.in/ggBB_aCm

4. Introduction to Transformers (Talk - Andrej Karpathy) - https://lnkd.in/gYMTVVmH

5. Building LLMs (Stanford CS229 Guest Lecture) - https://lnkd.in/gP9xqXxi

6. Deep Dive into LLMs like ChatGPT - https://lnkd.in/gtZ9BAdA

7. Let’s Build GPT from Scratch - https://lnkd.in/gdNj7_Tw

8. Agentic AI by Stanford - https://lnkd.in/gknxmPQG

9. Transformers and Self-Attention - https://lnkd.in/gvZZtciU

https://t.me/CodeProgrammer ✈️

800+ Data Science Interview Questions – A Must-Have Resource for Every Aspirant

Breaking into the data science field is challenging—not because of a lack of opportunities, but because of how thoroughly you need to prepare.

This document, curated by Steve Nouri, is a goldmine of 800+ real-world interview questions covering:

-Statistics
-Data Science Fundamentals
-Data Analysis
-Machine Learning
-Deep Learning
-Python & R
-Model Evaluation & Optimization
-Deployment Strategies
…and much more!

https://t.me/CodeProgrammer 🔰

Top 140 PyTorch Interview Questions and Answers

This comprehensive guide covers essential PyTorch interview questions across multiple categories, with detailed explanations for each.these 140 carefully curated questions represent the most important concepts you'll encounter in #PyTorch interviews.

🧠 Part 1: https://hackmd.io/@husseinsheikho/pytorch-interview

https://t.me/CodeProgrammer

“Learn AI” is everywhere. But where do the builders actually start?
Here’s the real path, the courses, papers and repos that matter.

✅ Videos:

Everything here ⇒ https://lnkd.in/ePfB8_rk

➡️ LLM Introduction → https://lnkd.in/ernZFpvB
➡️ LLMs from Scratch - Stanford CS229 → https://lnkd.in/etUh6_mn
➡️ Agentic AI Overview →https://lnkd.in/ecpmzAyq
➡️ Building and Evaluating Agents → https://lnkd.in/e5KFeZGW
➡️ Building Effective Agents → https://lnkd.in/eqxvBg79
➡️ Building Agents with MCP → https://lnkd.in/eZd2ym2K
➡️ Building an Agent from Scratch → https://lnkd.in/eiZahJGn

✅ Courses:

All Courses here ⇒ https://lnkd.in/eKKs9ves

➡️ HuggingFace's Agent Course → https://lnkd.in/e7dUTYuE
➡️ MCP with Anthropic → https://lnkd.in/eMEnkCPP
➡️ Building Vector DB with Pinecone → https://lnkd.in/eP2tMGVs
➡️ Vector DB from Embeddings to Apps → https://lnkd.in/eP2tMGVs
➡️ Agent Memory → https://lnkd.in/egC8h9_Z
➡️ Building and Evaluating RAG apps → https://lnkd.in/ewy3sApa
➡️ Building Browser Agents → https://lnkd.in/ewy3sApa
➡️ LLMOps → https://lnkd.in/ex4xnE8t
➡️ Evaluating AI Agents → https://lnkd.in/eBkTNTGW
➡️ Computer Use with Anthropic → https://lnkd.in/ebHUc-ZU
➡️ Multi-Agent Use → https://lnkd.in/e4f4HtkR
➡️ Improving LLM Accuracy → https://lnkd.in/eVUXGT4M
➡️ Agent Design Patterns → https://lnkd.in/euhUq3W9
➡️ Multi Agent Systems → https://lnkd.in/evBnavk9

✅ Guides:

Access all ⇒ https://lnkd.in/e-GA-HRh

➡️ Google's Agent → https://lnkd.in/encAzwKf
➡️ Google's Agent Companion → https://lnkd.in/e3-XtYKg
➡️ Building Effective Agents by Anthropic → https://lnkd.in/egifJ_wJ
➡️ Claude Code Best practices → https://lnkd.in/eJnqfQju
➡️ OpenAI's Practical Guide to Building Agents → https://lnkd.in/e-GA-HRh

✅ Repos:
➡️ GenAI Agents → https://lnkd.in/eAscvs_i
➡️ Microsoft's AI Agents for Beginners → https://lnkd.in/d59MVgic
➡️ Prompt Engineering Guide → https://lnkd.in/ewsbFwrP
➡️ AI Agent Papers → https://lnkd.in/esMHrxJX

✅ Papers:
🟡 ReAct → https://lnkd.in/eZ-Z-WFb
🟡 Generative Agents → https://lnkd.in/eDAeSEAq
🟡 Toolformer → https://lnkd.in/e_Vcz5K9
🟡 Chain-of-Thought Prompting → https://lnkd.in/eRCT_Xwq
🟡 Tree of Thoughts → https://lnkd.in/eiadYm8S
🟡 Reflexion → https://lnkd.in/eggND2rZ
🟡 Retrieval-Augmented Generation Survey → https://lnkd.in/eARbqdYE

Access all ⇒ https://lnkd.in/e-GA-HRh

By: https://t.me/CodeProgrammer 🟡