#Machine Learning Models

Machine learning relies heavily on mathematical foundations. #tech #ml #explore #fyp #ai

LLMs are AI models, but not all AI models are LLMs 👀 Here are 8 specialized architectures pushing AI beyond text: 1️⃣ LCMs – concept-level (Meta SONAR) 2️⃣ VLMs – vision + language 3️⃣ SLMs – small, fast edge models 4️⃣ MoE – efficient mixture of experts 5️⃣ MLMs – the OG masked models 6️⃣ LAMs – action-taking models (do tasks) 7️⃣ SAMs – pixel-level segmentation 8️⃣ LLMs – text + reasoning Each is built for a purpose: speed, size, or multimodality.

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

AI is more than LLM’s (large language models) 1️⃣ LLMs – Large Language Models 🧠 Token-by-token text processing for creative writing, coding, and deep reasoning. 2️⃣ LCMs – Large Concept Models 🌀 Meta’s approach: encode whole sentences as “concepts” in SONAR space, going beyond word-level. 3️⃣ VLMs – Vision-Language Models 🖼 Fuse images and text for visual understanding and captioning the core of multimodal AI. 4️⃣ SLMs – Small Language Models⚡️ Designed for edge devices. Compact, fast, and energy-efficient. 5️⃣ MoE Mixture of Experts 🧩 Activate only relevant subnetworks per query high efficiency, no quality loss. 6️⃣ MLMs – Masked Language Models 📚 The original bidirectional models understand context by seeing both sides of a sentence. 7️⃣ LAMs – Large Action Models 🔧 From understanding to action execute complex system-level operations. 8️⃣ SAMs – Segment Anything Models 🎯 Visual segmentation with pixel-level accuracy. Universal, foundational, powerful. Follow @aitoolhub.co for more Vid by LinkedIn / Francesco Massa #llm #ml #ai

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 If you don’t receive the link you either need to follow first then comment, or your instagram is outdated.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

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

Don’t know where to start on your AI development journey? These projects are the “Hello World” and basic intro into machine learning 😊☺️ #machinelearning #developer

Instance based VS Model based machine learning #ml #machinelearningengineer #machinelearning

Day 1 of our Machine Learning series 🚀 We started with the basics — what machine learning really is and how it works. This series is for anyone who wants to understand ML without confusion. Next up: AI vs Machine Learning. . . . . #MachineLearning #ArtificialIntelligence #CodeLoopa #LearnAI #TechExplained

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

If you want to learn AI in 2026, here's where to start: First, build a strong foundation in machine learning before moving into deep learning. Begin with supervised methods like linear and logistic regression to understand optimization and decision boundaries, then explore KNN, Naive Bayes, decision trees, random forests, gradient boosting, and SVMs to see different modeling assumptions and performance trade-offs. Next, study unsupervised techniques such as k-means and hierarchical clustering, Gaussian mixture models, and dimensionality reduction methods like PCA, t-SNE, and UMAP to learn how structure can be discovered without labels. With this in mind, transition to deep learning by learning neural networks and autoencoders, then more specialized architectures like CNNs for vision, RNNs for sequences, transformers and LLMs for language, and diffusion models for generative tasks. This progression builds intuition step by step, from classical algorithms to modern AI systems. If you want to commit to learning AI, Join 7000+ Others in our Visually Explained AI Newsletter. It's easy to understand, with math included—it's also completely free. The link is in our bio 🔗. Join our AI community for more posts like this @aibutsimple 🤖 #machinelearning #deeplearning #statistics #computerscience #coding #mathematics #math #physics #science #education

Let’s build a Machine Learning Model for Sentiment Analysis! 🤖💬 Using this dataset that I found online, I was able to experiment with building ML Models using Tensorflow and Python. 💻 This is the first time I’ve made a video about building an ML Model, so let me know if you’d like to see more! 🎥 After testing this, I was pretty impressed with the results. Would you like to see that video? 👀