#Supervised Learning Algorithms

📍Day 4: Difference between Supervised vs Unsupervised Learning cheatsheet. ⬇️ Save it for Later👇 1. Supervised and unsupervised learning are two key approaches in machine learning. 2. In supervised learning, the model is trained with labeled data where each input is paired with a corresponding output. 3. On the other hand, unsupervised learning involves training the model with unlabeled data where the task is to uncover patterns, structures or relationships within the data without predefined outputs. ✅ Type ‘supervised’ in the comment section and we will DM the PDF version for FREE ✨ ⏰ Like this post? Go to our bio click subscribe button and subscribe to our page. Join our exclusive subscribers channel ✨ Hashtags (ignore): #datascience #python #python3ofcode #programmers #coder #programming #developerlife #programminglanguage #womenwhocode #codinggirl #entrepreneurial #softwareengineer #100daysofcode #programmingisfun #developer #coding #software #programminglife #codinglife #code

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 If you don’t receive the link you either need to follow first then comment, or your instagram is outdated.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

🚀 Your roadmap to mastering ml algorithms in 2025! 💡 Save this for your next project! � Supervised 📊 Unsupervised 🔍 Reinforcement 🤖� This cheat sheet shows when to use classification, regression, clustering, association, dimensionality reduction & rl. Which algorithm have you used the most? 👇 ⚠️NOTICE Special Benefits for Our Instagram Subscribers 🔻 ➡️ Free Resume Reviews & ATS-Compatible Resume Template ➡️ Quick Responses and Support ➡️ Exclusive Q&A Sessions ➡️ Data Science Job Postings ➡️ Access to MIT + Stanford Notes ➡️ Full Data Science Masterclass PDFs ⭐️ All this for just Rs.45/month! . . . . . . #machinelearning #datascience #artificialintelligence #mlalgorithms #bigdata #deeplearning #ai #datasciencelife #mlengineer #datascientist

Google replaced the traditional education system with AI. Google’s new “Learn Your Way” uses AI to personalize learning from any book, document, or research paper based on how you understand best. Instead of one-size-fits-all lessons, it rewrites concepts using examples, analogies, visuals, and formats that actually make sense to you. This goes beyond education. Personalized learning powered by AI can transform how teams onboard, how founders train employees, and how professionals upskill. Internal documents, training material, and research can now be turned into content people retain. The real advantage in the AI era isn’t access to information. It’s how fast you can learn and apply it.

Proving simulation theory (the idea that our entire reality is a computer simulation) with 5th grade math, go cry about it #interesting #facts #theory #mindblown #videogames

Contrastive learning is a type of self-supervised learning where the goal is to learn representations by comparing pairs of data. Instead of predicting missing parts of data like other self-supervised algorithms, it teaches a model to bring similar examples (called positive pairs) closer in the embedding space while spacing different ones (called negative pairs) farther apart. For instance, two different versions of the same image (rotated and cropped) should be encoded into similar vectors, while two images of different objects should be encoded into distant vectors. There is a special loss function used called the contrastive loss, which minimizes the distance between positives and maximizes it against negatives by using a parameter called the margin. This loss function is fairly simple and depends on the squared distances between points. The result of contrastive learning is a semantic space where similar concepts are related, making it very effective for downstream tasks like clustering, retrieval, or classification with minimal labeled data. Struggling with ML/AI? Accelerate Your Learning With our Weekly AI Newsletter—educational, easy to understand, mathematically explained, and completely free (link in bio 🔗). C: Deepia Join our AI community for more posts like this @aibutsimple 🤖

Supervised Learning Explained #machinelearning #ml #computerscience #engineering #programming #coding

Learning HOW to learn, is one of the most important skillsets of the next decade. Well, Google has a product called NotebookLM, that they released a few years ago, and have been steadily improving and iterating not he product since. It is one of THE very best AI learning tools, but it seems it flies heavily under the radar. I’ve talked about it in my content before, but I thought a dedicated video would be helpful to so many people out there. Make sure to share it with all of the students in your life, but I think everyone could make use of it. If you like this video, follow @rpn to stay two steps ahead on the incredibly disruption happening in tech and how to leverage it in content and business.

Practice machine learning from today!!! #themetrixofsatya

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

Steve brunton is sooo GOATEDDD !!! #machinelearning #datascience #stem #artificialintelligence

If I had to implement AI into my study workflow from scratch, here’s exactly how I would do it. Most students use AI completely wrong. They ask it to summarize chapters, explain concepts, and provide answers. This feels productive but prevents real learning because you’re outsourcing the thinking instead of strengthening your own understanding. The golden rule is to use AI to challenge your thinking, not replace it. AI should make you work harder mentally, not easier. Step One: Socratic Tutoring. Don’t ask AI to explain concepts. Ask it to guide you through questions that expose what you don’t understand. This forces you to identify your own gaps instead of passively consuming explanations. Step Two: Multi-Level Explanations. Ask for explanations at different levels, then close the chat and explain it back in your own words without looking. This tests whether you actually understand or just recognized the information. Step Three: Active Recall Testing. Never ask AI for notes. Ask it to test you and answer from memory. Research by Henry Roediger proves that retrieval practice builds stronger retention than passive review. Step Four: Bloom’s Taxonomy Layers. Move through all six cognitive levels: remember, understand, apply, analyze, evaluate, create. Exams test all six, not just memorization, so your study sessions should too. Step Five: Reflective Reading. Skim the chapter, write your own summary, then ask AI to compare it to the original and show what you missed. This reveals whether you identified the core concepts or just surface details. AI isn’t for making studying easy. It’s for making learning effective. If retrieving information feels uncomfortable, that’s proof you’re learning. Comment AI for my full video on how to use AI properly 🚀