Machine Learning with Python

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Searched 35 free courses, so you don't have to! 🔍✨ Here are the 35 best free courses: 🎓 1. Data Science: Machine Learning 🤖 Link: https://lnkd.in/gUNVYgGB 2. Introduction to computer science 💻 Link: https://lnkd.in/gR66-htH 3. Introduction to programming with scratch 🧩 Link: https://lnkd.in/gBDUf_Wx 4. Computer science for business professionals 💼 Link: https://lnkd.in/g8gQ6N-H 5. How to conduct and write a literature review 📝 Link: https://lnkd.in/gsh63GET 6. Software Construction 🛠 Link: https://lnkd.in/ghtwpNFJ 7. Machine Learning with Python: from linear models to deep learning 🐍🧠 Link: https://lnkd.in/g_T7tAdm 8. Startup Success: How to launch a technology company in 6 steps 🚀 Link: https://lnkd.in/gN3-_Utz 9. Data analysis: statistical modeling and computation in applications 📊 Link: https://lnkd.in/gCeihcZN 10. The art and science of searching in systematic reviews 🔎 Link: https://lnkd.in/giFW5q4y 11. Introduction to conducting systematic review 📋 Link: https://lnkd.in/g6EEgCkW 12. Introduction to computer science and programming using python 🖥 Link: https://lnkd.in/gwhMpWck 13. Introduction to computational thinking and data science 💡 Link: https://lnkd.in/gfjuDp5y 14. Becoming an Entrepreneur 💸 Link: https://lnkd.in/gqkYmVAW 15. High-dimensional data analysis 📈 Link: https://lnkd.in/gv9RV9Zc 16. Statistics and R 📉 Link: https://lnkd.in/gUY3jd8v 17. Conduct a literature review 📚 Link: https://lnkd.in/g4au3w2j 18. Systematic Literature Review: An Introduction 🧐 Link: https://lnkd.in/gVwGAzzY 19. Introduction to systematic review and meta-analysis 🧮 Link: https://lnkd.in/gnpN9ivf 20. Creating a systematic literature review ✍️ Link: https://lnkd.in/gbevCuy6 21. Systematic reviews and meta-analysis 📊 Link: https://lnkd.in/ggnNeX5j 22. Research methodologies 🕵️‍♂️ Link: https://lnkd.in/gqh3VKCC 23. Quantitative and Qualitative research for beginners 📊💬 Link: https://shorturl.at/uNT58 24. Writing case studies: science of delivery 📑 Link: https://shorturl.at/ejnMY 25. research methodology: complete research project blueprint 🗺 Link: https://lnkd.in/gFU8Nbrv 26. How to write a successful research paper 📜 Link: https://lnkd.in/g-ni3u5q 27. Research proposal bootcamp: how to write a research proposal 🏃‍♂️ Link: https://lnkd.in/gNRitBwX 28. Understanding technology 📱 Link: https://lnkd.in/gfjUnHfd 29. Introduction to artificial intelligence with Python 🤖🐍 Link: https://lnkd.in/gygaeAcY 30. Introduction to programming with Python 💻 Link: https://lnkd.in/gAdyf6xR 31. Web programming with Python and JavaScript 🌐 Link: https://lnkd.in/g_i5-SeG 32. Understanding Research methods 🔬 Link: https://lnkd.in/g-xBFj4v 33. How to write and publish a scientific paper 📢 Link: https://lnkd.in/giwTe2is 34. Introduction to systematic review and meta-analysis 📊 Link: https://lnkd.in/gnpN9ivf 35. Research for impact 🌍 Link: https://lnkd.in/gRsWsUsq

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LLM Engineering Roadmap (2026 Practical Guide) 🗺✨ If your goal is to build real LLM apps (not just prompts), follow this order. 🚀 1️⃣ Python + APIs 🐍🔌 You’ll spend most of your time wiring systems. Learn: → functions, classes → working with APIs (requests, JSON) → async basics → environment variables Resources → Python for Everybody https://lnkd.in/gUqkvnGG → Introduction to Python https://lnkd.in/g7xfYJVZ → MLTUT Python Basics Course https://lnkd.in/gCqfyCGZ 2️⃣ Text Basics (NLP) 📝🧠 You don’t need heavy theory, just the essentials. Learn: → tokenization → text cleaning → similarity (cosine) → basic embeddings idea Resources → Natural Language Processing Specialization https://lnkd.in/gz_xmqD9 → NLP in Python https://lnkd.in/gnpcJxhz 3️⃣ Transformers (What’s happening behind the API) 🤖🔍 Enough to not treat it like a black box. Learn: → tokens, context window → attention (high level) → why embeddings work → limits of LLMs Resources → Generative AI with Large Language Models https://lnkd.in/gk3PPtyf → Hugging Face Transformers Course https://lnkd.in/ggSR5JNb 4️⃣ Prompting (Make outputs reliable) 💬🎯 Treat prompts like code. Learn: → few-shot examples → structured outputs (JSON) → system vs user instructions → simple evals (does it break?) Resources → Prompt Engineering for ChatGPT https://lnkd.in/gyg4EiJS → Prompt Engineering with LLMs https://lnkd.in/gn67Mxga 5️⃣ Embeddings + Vector DBs 📊🗄 This is how you add your data. Learn: → embedding generation → similarity search → indexing Tools: → FAISS → Pinecone → Chroma Resources → Working with Embeddings https://lnkd.in/gnngPW4E → Vector Databases & Semantic Search https://lnkd.in/gP2HdMmD 6️⃣ RAG Pipelines 🔗🔄 Most useful apps use this pattern. Learn: → chunking documents → retrieval + ranking → prompt + context design → basic evaluation Resources → Generative AI for Software Development https://lnkd.in/g3uduecv → Build RAG Apps with LangChain https://lnkd.in/ggXJjgDN 7️⃣ Build Real Applications 🛠💻 Keep them small and usable. Build: → document Q&A (PDF → answers) → internal knowledge bot → code assistant (repo Q&A) → support chatbot Tools: → LangChain → LlamaIndex → OpenAI APIs Resources → Build LLM Apps with LangChain & Python https://lnkd.in/g6xXVX_8 → LLM Applications https://lnkd.in/gzs8_SRk 8️⃣ Deployment 🚢☁️ Make it usable by others. Learn: → FastAPI endpoints → streaming responses → caching (reduce cost) → logging + monitoring Tools: → FastAPI → Docker → AWS / GCP Resources →Machine Learning Engineering for Production (MLOps) https://lnkd.in/gCMtYSk5 → MLOps Fundamentals https://lnkd.in/g8TGrUzT https://t.me/DataAnalyticsX ✅

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Today, the public mint for Lobsters on TON goes live on Getgems 🦞 This is not just another NFT drop. In my view, Lobsters is one of the first truly cohesive products at the intersection of blockchain, NFTs, and AI. Here, the NFT is not just an image and not just a collectible. Each Lobster is an NFT with a built-in AI agent inside: a digital character with its own soul, on-chain biography, persistent memory, and a unified identity across Telegram, Mini App, Claude, and API. So you are not just getting an asset in your wallet. You are getting an AI-native digital character that can interact, remember, and stay consistent across different interfaces. What makes this especially interesting is the timing. In the recent video Pavel Durov shared in his post about agentic bots in Telegram, the lobster imagery was right there. Against that backdrop, Lobsters does not feel like a random mint — it feels like a very precise fit for the new narrative: Telegram-native agents + TON infrastructure + NFT ownership layer + AI utility Put simply, this is one of the first real attempts to turn an NFT from “just an image” into a digital agent. Public mint: today, 16:00 Price: 50 TON 👉 Mint your Lobster on Getgems 🦞🦞🦞

This FREE AI engineering roadmap Will teach you more in 2026 than a 4-year college degree... Here's the exact 6-step blueprint 👇 1️⃣STEP 1: Python Programming Foundations Harvard CS50's Python Programming Course : https://lnkd.in/ePCvXwXP → Build unshakeable coding fundamentals → 6-8 weeks to Python mastery 2️⃣ STEP 2: Machine Learning Foundations Stanford CS229: Machine Learning : https://lnkd.in/eEsdZbVc → Learn from the legends at Stanford → Master ML algorithms and math foundations → 10-12 weeks of pure gold 3️⃣ STEP 3: Deep Learning Mastery Fast.ai Practical Deep Learning : https://course.fast.ai/ → Jeremy Howard's legendary course → Build real AI applications from day 1 → 8-10 weeks of hands-on projects 4️⃣ STEP 4: Natural Language Processing Stanford CS224N/Ling284 : https://lnkd.in/ebQZ5_T3 → Master transformers and language models → The foundation of ChatGPT and GPT-4 → 10-12 weeks of cutting-edge NLP 5️⃣ STEP 5: Generative AI Introduction Microsoft Generative AI for Beginners : https://lnkd.in/ewsH8gMT → 21 Lessons teaching everything you need to know to start building Generative AI applications → 6-8 weeks of creative AI 6️⃣ STEP 6: Large Language Models LLM University by Cohere : https://cohere.com/llmu → Fine-tune and deploy production LLMs → Build and deploy LLM models → 6-8 weeks of enterprise-level skills https://t.me/CodeProgrammer ✅

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