#Machine Learnings

Where does AI help you the most? #ai #aiart #neuralnetworks #math #machinelearning #fyp #fypシ

Making building your own ML model a little less intimidating if it’s your first time :) #ai #machinelearning

Week 6 of AI updates is here 🔥 . . . . . . . . . . . . . . . . [AI updates, artificial intelligence, tech news, ChatGPT, OpenAI, Google AI, Meta AI, weekly roundup, AI trends, machine learning, generative AI, AI tools, AI news, future of work, tech trends, automation, AI features, AI week 6, trending AI, AI developments, AI innovation]

Beginner or pro🙃 #solidworks #engineer #mecánica #machinelearning #machinist

Built an Al feature at Google and lowkey still don’t know how LLM’s work What should I should research first?

All-in-One AI Tools | Create, Write, Design & More in One Place #aitools #allinone #aiproductivity . . Comment “ChatGPT” for free 1000 Advanced ChatGPT prompts

Anddd Number 10 is Bestfreeaiwebsites.com, the biggest library of FREE ai tools updated daily. Whats your favorite one?🫢 #aitools #bestwebsites #tech #digitalmarketing

K-Nearest Neighbours (KNN) is a simple and intuitive supervised machine learning algorithm that makes predictions based on how similar things are to each other. They can be used for classification and regression. Imagine you have a scatter plot with red and blue points, where red points represent one class and blue points represent another class. Now, let’s say you get a new data point you haven’t seen before, and want to know if it should be red or blue. KNN looks at the “K” closest points (a hyperparameter that you set) to this new one — say, the 3 nearest points. If 2 out of those 3 are red and 1 is blue, the new point is classified as red. It’s like asking your closest neighbors what they are and choosing the majority answer. Although simple, KNN performs surprisingly well based on the principle of proximity. Want to get better at machine learning? Accelerate your ML learning with our Weekly AI Newsletter—educational, easy to understand, mathematically explained, and completely free (link in bio 🔗). C: visually explained Join our AI community for more posts like this @aibutsimple 🤖 #machinelearning #statistics #mathematics #math #physics #computerscience #coding #science #education #datascience #knn

Le AIceberg #chatgpt #gpt5 #ai #llm

Comment “Statistics” and I’ll share the link. This website is a complete guide to learning statistics for machine learning. You’ll find everything in one place, from basic probability to regression analysis. It covers topics like probability distribution, compound probability, and statistical inference in a clean, visual way. The best part is its interactive UI. You can experiment with real examples, like simulating a coin toss 100 times, to see how probabilities actually work. It helps you move from memorizing formulas to understanding how data behaves. If you’ve been struggling with statistics, this website will make it simple and engaging to learn. 💡 Comment “Statistics” and I’ll share the link.

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]

Do you think we can build a solid model at the end of this year? #formula1 #machinelearning #programming