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Machine Learning with Python: post #5423 — TG.ME

Machine Learning with Python@CodeProgrammer · 68.2K subscribers Open in TelegramOpen

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t.me/CodeProgrammer/5423

August 13, 2026

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Follow the Machine Learning with Python channel on WhatsApp: https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A 3K·Aug 22 Don't forget to try it; it's free and includes most AI models. 877·Aug 22 Machine Learning with Python pinned a photoAug 22

🔥 More models. Lower cost. One API key. Access GPT, Claude, Grok, Gemini, DeepSeek, Kimi, Qwen and more through one gateway. 💰 Better value Access leading models at prices below official API list rates. 🔌 One unified gateway Connect apps, agents and coding tools with one Smart API key. 📈 Clear costs Track every request, token and cost in one place. 🛡️ Reliable access Choose model groups with ordered fallback options. ⚡️ Mode Website: https://modelflare.dev/ 👉 Models & pricing: https://modelflare.dev/pricing 💬 Join the ModelFlare community: https://t.me/+GxEEPAsQ0ERiOGUx 1.4K·Aug 22 🤖Still Scared AI Is Coming For Your Job? Here's 20 Repos To Actually Do Something About It 1. Prompt-Engineering-Guide Explains how to actually get useful, reliable output from AI models instead of guessing and hoping. 2. AI-For-Beginners Covers the broader AI landscape, not just LLMs, so you're not caught off guard by the next big shift either. 3. generative-ai-for-beginners A calm, lesson-by-lesson intro to building with generative AI, no ML background assumed. 4. anthropic-cookbook Real code examples for building things with Claude, straight from the people who make it. 5. openai-cookbook The same idea for OpenAI's models, practical patterns instead of theory. 6. ML-For-Beginners Twelve weeks of classic machine learning basics, useful groundwork before jumping into AI specifically. 7. llama_index Handles connecting AI models to your own data and documents, a skill that comes up constantly in real jobs now. 8. ollama Lets you run AI models on your own machine, good for understanding them without relying on an API key. 9. llama.cpp Shows what's actually happening when a model runs, right down to the hardware level. 10. transformers The library behind most modern AI models, worth exploring even just to see how they're built. 11. awesome-ai-agents A running list of AI agent projects and frameworks, handy for keeping track of a space that moves fast. 12. nanoGPT A small, readable implementation of GPT, built by Andrej Karpathy so you can see the whole thing without getting lost. 13. AutoGPT One of the earliest autonomous AI agent projects, still useful for understanding how agents plan and act. 14. gpt-engineer An AI agent that writes entire codebases from a prompt, worth trying just to see what it's actually capable of. 15. OpenHands An open source AI coding agent, a good look at where AI-assisted development is heading. 16. awesome-machine-learning A broad, well-organized list of ML resources and libraries across every language, a solid reference to keep bookmarked. 17. langchain The most widely used framework for wiring AI models into actual applications, worth knowing even if you end up not using it. 18. Awesome-LLM A curated list of papers, models, and tools, good for going deeper once the basics feel comfortable. 19. applied-ml Real-world write-ups from companies on how they actually use ML and AI in production, not just in demos. 20. llm-course A structured path from "what is an LLM" to actually fine-tuning and deploying one yourself. https://t.me/CodeProgrammer ✈️ 2.6K·Aug 19

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