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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 1.5 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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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

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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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New to Pandas? Here's a cheat sheet you can download (2025)

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👩‍💻 The best repository for learning artificial intelligence practically🎓! 📌 The official GitHub repository for the book Hands-On Machine Learning (2nd Edition) 📔 Contains Jupyter notebooks covering:

⚪️Machine Learning Principles (Linear Models, Decision Trees, SVM). ⚪️Unsupervised Algorithms (Clustering, PCA). ⚪️Advanced Neural Networks (ANN, CNN, RNN, LSTM). ⚪️How Transformers Work. ⚪️Reinforcement Learning. ⚪️Building and Training Models with TensorFlow 2 & Keras. ⚪️Deploying and Scaling Models to Production Environments. ⚪️Data Science Tools (Pandas, NumPy, Matplotlib, Scikit-Learn).

🖱️Free - Comprehensive - Updated 🖱️Suitable for students, developers 🖥 GitHub

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Have you ever opened a GitHub repository and wished someone would explain everything step by step? 🖥 Code2Tutorial is an amazing open-source tool that uses artificial intelligence to transform any software project on GitHub into an organized and clear interactive guide, as if you were reading documentation written specifically for you by the project developer📝. 🖥 Just paste the repository link and prepare for a comprehensive understanding journey: from the project structure, to the functions of each file, and the execution sequence🛸. Don't let a unique project pass you by... Understand it, learn from it, and go ahead and write your next lines with confidence🤝. 🖥 Source Code 🌐 Tool Link

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Found a cool tool for those who work with Pandas🐻. You can now automatically profile a DataFrame in Jupyter with Pandas AutoProfiler. The output includes column distribution, summary statistics, data on null values, code export, etc📊. Everything works automatically - no need to write additional code🤝. 🖥 GitHub

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🐍Looking to get started with Deep Learning using PyTorch? This well-structured GitHub repository is a goldmine for beginners who want to learn PyTorch with hands-on examples and clear explanations📖. 🗂 What’s Inside?

🈂 Jupyter Notebooks with interactive code. 🧠 Step-by-step tutorials on Tensors, Autograd, and Neural Networks. 🖼 Real-world mini-projects like image classification. ⌛ Practical guides on using GPU with PyTorch. ✅ Beginner-friendly but also great for revision.

💡If you're serious about learning AI, this is one of the best free resources to kick off your journey🤝. 🖥 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.me/addlist/8_rRW2scgfRhOTc0 ✅ https://t.me/Codeprogrammer

4️⃣In this repository… you don’t just understand — you experience it😱. 🔗Machine-Learning-Projects by Aswin S isn’t just a collection of code👨‍💻. It’s a hands-on learning environment powered by Jupyter Notebooks📔, designed to teach you how to apply machine learning algorithms to real-world data📱. 🛸 What’s inside?

A curated set of practical projects that turn theory into action: 🖼Predictive modeling for Black Friday and BigMart sales 🕯Time series forecasting for airline passenger trends 🧬Breast cancer detection from medical datasets. 💳Credit card fraud detection. 🏠Boston housing price prediction. 😳Face recognition using OpenCV. Each project is built with clarity and purpose👨‍💻. You can run everything directly in Google Colab — no setup required⚙.

 🖥 GitHub

Start with a notebook… and walk away with real skills🏹.

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