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Reels en Tendance

(12)
#Machine Learning Reel by @sambhav_athreya - I've been asked many times where to start learning ML, so after talking to so many experts in this field, this is a good place to start. Comment dow
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@sambhav_athreya
I’ve been asked many times where to start learning ML, so after talking to so many experts in this field, this is a good place to start. Comment down below “TRAIN” and I’ll send you a more in-depth checklist along with the best GitHub links to help you start learning each concept. If you don’t receive the link you either need to follow first then comment, or your instagram is outdated. Either way, no worries. send me a dm and I’ll get it to you ASAP. #cs #ai #dev #university #softwareengineer #viral #advice #machinelearning
#Machine Learning Reel by @chrisoh.zip - Machine learning relies heavily on mathematical foundations. #tech #ml #explore #fyp #ai
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@chrisoh.zip
Machine learning relies heavily on mathematical foundations. #tech #ml #explore #fyp #ai
#Machine Learning Reel by @chrispathway (verified account) - Here's your full roadmap on how to get into machine learning. Comment "Roadmap" to get the pdf. Save and follow for more. #ai #machinelearning #codi
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@chrispathway
Here’s your full roadmap on how to get into machine learning. Comment “Roadmap” to get the pdf. Save and follow for more. #ai #machinelearning #coding #programming #cs
#Machine Learning Reel by @mar_antaya (verified account) - Hehhe hope you guys like these recs #machinelearning #coding #ai
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@mar_antaya
Hehhe hope you guys like these recs #machinelearning #coding #ai
#Machine Learning Reel by @pikacodes (verified account) - 2025 machine learning roadmap - it's time to start prepping for AI's takeover 💡🤖 resources mentioned: VIDEO: Full Applied AI Lectures by Cassie Ko
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@pikacodes
2025 machine learning roadmap - it’s time to start prepping for AI’s takeover 💡🤖 resources mentioned: VIDEO: Full Applied AI Lectures by Cassie Kozyrkov Neural Networks: Zero to Hero by Andrej Karpathy Machine Learning Specialization by Andrew Ng BOOKS: An Introduction to Statistical Learning Mathematics for Machine Learninf Artificial Intelligence: A Modern Approach FOR PRACTICE: Machine Learning with PyTorch and Scikit-Learn AIML.com . . #machinelearning #ai #resources #tech #programming #womenintech #coder #programacao #latinasintech #swe
#Machine Learning Reel by @sujar.tech (verified account) - Here are some machine learning courses that are actually worth it and will make you learn very quickly. These courses don't have the same fluff and ex
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@sujar.tech
Here are some machine learning courses that are actually worth it and will make you learn very quickly. These courses don’t have the same fluff and excessive theoretical jargon that doesn’t help you. So this list actually compiles the best ones that I have found. And if you want to check out the courses for yourself,make sure to follow @sujar.tech and comment “Courses” and I’ll send you the links to all of these #coding #computerscience #cs #machinelearning
#Machine Learning Reel by @the.datascience.gal (verified account) - Want to become a Machine Learning Engineer in 2025? Build real projects that reflect how ML is done in the industry: 1 → End-to-End ML Pipeline Predi
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@the.datascience.gal
Want to become a Machine Learning Engineer in 2025? Build real projects that reflect how ML is done in the industry: 1 → End-to-End ML Pipeline Predict something useful (like student dropout risk). Clean with Pandas, train with LightGBM, deploy with FastAPI + Docker + AWS. 2 → RAG Chatbot Build a chatbot that answers from your course notes. Use LlamaIndex + FAISS + Llama 3.1. This is how GenAI apps work today. 3 → Fine-Tune LLMs Take an open-source LLM and fine-tune it on your own dataset. Use QLoRA with PEFT. Example: medical Q&A bot. 4 → Model Monitoring Build a fraud detection model and track drift post-deployment using Evidently AI + Weights & Biases. Shows you think beyond training. 5 → Multimodal AI App Photo → nutrition info + recipe. Use CLIP or Florence-2 for vision-text, connect to LLaVA or Qwen-VL, deploy with Streamlit. This stack hits every part of the ML lifecycle—from classic ML to GenAI to production monitoring. [mlprojects, machinelearningengineer, genai, fine-tuning, ragchatbot, mlportfolio, endtoendpipeline, multimodalai, ai2025, llmengineer, mljobs, mlworkflow, productionai]
#Machine Learning Reel by @mar_antaya (verified account) - Do you think we can build a solid model at the end of this year? #formula1 #machinelearning #programming
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@mar_antaya
Do you think we can build a solid model at the end of this year? #formula1 #machinelearning #programming
#Machine Learning Reel by @chrisoh.zip - Courses take up too much time. I'd probably just start learning ML by doing it. Comment "link" and I'll send the resource. #tech #explore #fyp #ml #
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@chrisoh.zip
Courses take up too much time. I’d probably just start learning ML by doing it. Comment “link” and I’ll send the resource. #tech #explore #fyp #ml #ai
#Machine Learning Reel by @itsallykrinsky - this is the software side of robotics of course there's a whole other piece to make the robots work #ai #machinelearning #datascientist #machinelearni
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@itsallykrinsky
this is the software side of robotics of course there’s a whole other piece to make the robots work #ai #machinelearning #datascientist #machinelearningengineer #robotics #techcareer #careergrowthtips
#Machine Learning Reel by @emrcodes (verified account) - These are some of the best beginner-friendly resources I've found to actually understand machine learning. Nothing overly complicated, just what you
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@emrcodes
These are some of the best beginner-friendly resources I’ve found to actually understand machine learning. Nothing overly complicated, just what you need to get the concepts and start building. Comment ML and I’ll send you all the resources.
#Machine Learning Reel by @workiniterations - Links 1) Professor Leonard - Full Calculus Lectures https://youtube.com/playlist?list=PLF797E961509B4EB5&si=ArxpgybZXZ644KtM 2) MIT 18.01 (David Je
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@workiniterations
Links 1) Professor Leonard — Full Calculus Lectures https://youtube.com/playlist?list=PLF797E961509B4EB5&si=ArxpgybZXZ644KtM 2) MIT 18.01 (David Jerison) https://youtube.com/playlist?list=PL590CCC2BC5AF3BC1&si=2A-6mMZVTWwFrtu9 3) Steve Brunton — Calculus for Science & ML https://youtu.be/ebfOSDj4j3I?si=pz4mW7NBni27OIHM 4) Michael Penn — Calculus Explanations https://youtu.be/qiuEISlZefk?si=x5myhCBBtDR4mepg Full ML roadmap in my bio. #calculus #math #linearalgebra #machinelearning #ai

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