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

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New to Pandas?

Here's a cheat sheet you can download (2025)

#Pandas #Python #DataAnalysis #PandasCheatSheet #PythonForDataScience #LearnPandas #DataScienceTools #PythonLibraries #FreeResources #DataManipulation

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Top 50 LLM Interview Questions!

A comprehensive resource that covers traditional ML basics, model architectures, real-world case studies, and theoretical foundations.

👇👇👇👇👇👇

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Top 50 LLM Interview Questions!

#LLM #AIInterviews #MachineLearning #DeepLearning #NLP #LLMInterviewPrep #ModelArchitectures #AITheory #TechInterviews #MLBasics #InterviewQuestions #LargeLanguageModels

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10 GitHub repos to build a career in AI engineering:

(100% free step-by-step roadmap)

1️⃣ ML for Beginners by Microsoft

A 12-week project-based curriculum that teaches classical ML using Scikit-learn on real-world datasets.

Includes quizzes, lessons, and hands-on projects, with some videos.

GitHub repo → https://lnkd.in/dCxStbYv

2️⃣ AI for Beginners by Microsoft

This repo covers neural networks, NLP, CV, transformers, ethics & more. There are hands-on labs in PyTorch & TensorFlow using Jupyter.

Beginner-friendly, project-based, and full of real-world apps.

GitHub repo → https://lnkd.in/dwS5Jk9E

3️⃣ Neural Networks: Zero to Hero

Now that you’ve grasped the foundations of AI/ML, it’s time to dive deeper.

This repo by Andrej Karpathy builds modern deep learning systems from scratch, including GPTs.

GitHub repo → https://lnkd.in/dXAQWucq

4️⃣ DL Paper Implementations

So far, you have learned the fundamentals of AI, ML, and DL. Now study how the best architectures work.

This repo covers well-documented PyTorch implementations of 60+ research papers on Transformers, GANs, Diffusion models, etc.

GitHub repo → https://lnkd.in/dTrtDrvs

5️⃣ Made With ML

Now it’s time to learn how to go from notebooks to production.

Made With ML teaches you how to design, develop, deploy, and iterate on real-world ML systems using MLOps, CI/CD, and best practices.

GitHub repo → https://lnkd.in/dYyjjBGb

6️⃣ Hands-on LLMs

- You've built neural nets.
- You've explored GPTs and LLMs.

Now apply them. This is a visually rich repo that covers everything about LLMs, like tokenization, fine-tuning, RAG, etc.

GitHub repo → https://lnkd.in/dh2FwYFe

7️⃣ Advanced RAG Techniques

Hands-on LLMs will give you a good grasp of RAG systems. Now learn advanced RAG techniques.

This repo covers 30+ methods to make RAG systems faster, smarter, and accurate, like HyDE, GraphRAG, etc.

GitHub repo → https://lnkd.in/dBKxtX-D

8️⃣ AI Agents for Beginners by Microsoft

After diving into LLMs and mastering RAG, learn how to build AI agents.

This hands-on course covers building AI agents using frameworks like AutoGen.

GitHub repo → https://lnkd.in/dbFeuznE

9️⃣ Agents Towards Production

The above course will teach what AI agents are. Next, learn how to ship them.

This is a practical playbook for building agents covering memory, orchestration, deployment, security & more.

GitHub repo → https://lnkd.in/dcwmamSb

🔟 AI Engg. Hub

To truly master LLMs, RAG, and AI agents, you need projects.

This covers 70+ real-world examples, tutorials, and agent app you can build, adapt, and ship.

GitHub repo → https://lnkd.in/geMYm3b6

#AIEngineering #MachineLearning #DeepLearning #LLMs #RAG #MLOps #Python #GitHubProjects #AIForBeginners #ArtificialIntelligence #NeuralNetworks #OpenSourceAI #DataScienceCareers

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.
Gemini CLI

Google has launched Gemini CLI, a new AI-powered tool that runs directly through the command-line interface (Terminal) 📃.

The tool is designed to be a powerful assistant for developers and represents a significant step forward in seamlessly integrating artificial intelligence into development environments🧠.

✅ Key Features of Gemini CLI:

Powered by the advanced Gemini 2.5 Pro model🛸.

Completely free during the current phase🆓.

Generous usage limits: up to 60 requests per minute and 1000 requests per day – more than enough for most personal uses🛸.


👨‍💻Advanced Technical Features:

Extended context window of up to 1 million tokens, enabling it to handle long texts and complex projects efficiently💻.

Multimodal support: understands and works with code, images, PDFs, and user interface (UI) graphics✨.

Built-in Google Search to enhance AI responses and provide accurate answers🔎.

Automated pull request (PR) handling in software projects, including detecting and executing PRs automatically✅.

While other AI tools require monthly subscriptions ranging from $10 to $20, Google is offering this tool completely free 🤝.

Google really is generous🎓.

🖥 GitHub

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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.iss.one/addlist/8_rRW2scgfRhOTc0

✅ https://t.iss.one/Codeprogrammer

Let's start at the top...

NumPy contains a broad array of functionality for fast numerical & mathematical operations in Python

The core data-structure within #NumPy is an ndArray (or n-dimensional array)

Behind the scenes - much of the NumPy functionality is written in the programming language C

NumPy functionality is used in other popular #Python packages including #Pandas, #Matplotlib, & #scikitlearn!

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