1.1M
CHMachine learning relies heavily on mathematical foundations.
#tech #ml #explore #fyp #ai
@chrisoh.zip
#Algorithm In Machine Learning
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282.0K
COMachine Learning, explained by cats. #cat #code #ai #machinelearning #algorithm
@coding.kitty
86.1K
THPractice machine learning from today!!!
#themetrixofsatya
@themetrixofsatya
141.2K
ARIf you’re just getting into machine learning, this is the best place to start.
R2D3 is a free, interactive website that teaches you how machine learning works through animated visualizations. No equations upfront. No wall of theory. You scroll, and the model builds itself in front of you.
What the video couldn’t cover:
The site walks you through a decision tree, one of the most foundational algorithms in ML, using a real dataset of homes in San Francisco and New York. You watch the model draw boundaries on the data, test them, and adjust when they are wrong.
The concept it ends on is overfitting, what happens when a model learns the training data too well and fails on anything new. Seeing it visually is the moment a lot of things in ML suddenly click.
Built by Stephanie Yee and Tony Chu. Completely free, no sign-up required.
Link in the pinned comment.
#ai #artificialintelligence #technews #algorithm #fyp
@arnitly
40.7K
VOComment “ML” and I’ll send you the links👇
Machine learning doesn’t have to feel overwhelming. With the right guidance, complex topics like models, training, and prediction start making real sense 🧠
📌 Check out these beginner-friendly ML videos:
1️⃣ Learn Machine Learning Like a Genius – by InfiniteCodes
2️⃣ All ML Concepts Explained in 22 Minutes – by InfiniteCodes
3️⃣ ML for Everybody (Full Course) – by FreeCodeCamp
If terms like neural networks, supervised learning, or algorithms have ever confused you, these tutorials simplify everything into clear, practical explanations you can actually follow.
Instead of getting stuck in heavy math or abstract theory, you’ll build strong intuition around how machine learning works — from foundational concepts to real-world AI applications.
Whether you're interested in artificial intelligence, data science, Python projects, or future-proof tech skills, this is a powerful place to begin.
⭐ Save this so you don’t lose it, share it with someone learning AI, and start making machine learning finally click.
@volkan.js
210.2K
CHCourses 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
@chrisoh.zip
49.1K
CAK Means clustering algorithm in Machine Learning
#machinelearning #datascience #mlengineer #deeplearning #ai #artificialintelligence #learnai #cactusai #techreels #university #exam #exams
@cactuss.ai
380.8K
CHHere’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
@chrispathway
1.3M
SAI’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
@sambhav_athreya
25.5K
SAWhat is an algorithm? 🤯
It’s not magic. It’s just step-by-step logic.
Search, sort, recommend… this is what runs everything.
Wether you are CS student, a junior or a senior engineer, learning algorithms + DSA is how you learn to actually solve real-world problems. 🚀
@sayed.developer
117.2K
EQThe Secret Behind Machine Learning Predictions! Ever wondered how machines make binary decisions? This video breaks down Logistic Regression using the Sigmoid Function. We visualize how the weight (w) controls the steepness of the curve and how the bias (b) shifts it along the x-axis. See how Cross-Entropy (CE) Loss is minimized to find the optimal fit for your data points. Finally, we explore the decision boundary at P=0.5, which separates predictions into Class 0 and Class 1. Perfect for data science students and machine learning enthusiasts looking for a quick, intuitive visualization of classification algorithms and mathematical optimization. #LogisticRegression #MachineLearning #SigmoidFunction #Math #Manim
@equationsinmotion
35.0K
DAThe exact framework I’d use to learn ML from scratch in 2026. Save this if you’re actually trying to build - not just collect tutorials.
#machinelearning #artificalintelligence #datascience #learntocode #coding
@dandoesdata.ai
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