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CHMachine learning relies heavily on mathematical foundations.
#tech #ml #explore #fyp #ai
@chrisoh.zip
#Intuitive Machine Learning
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AIK-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
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#machinelearning #statistics #mathematics #math #physics #computerscience #coding #science #education #datascience #knn
@aibutsimple
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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If you don’t receive the link you either need to follow first then comment, or your instagram is outdated.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
456.1K
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
571.3K
THNeural networks and machine learning.
@theartificialintelligence
217.1K
IThow to learn ml with no experience - been getting asked a ton about this #techcareer #ai #machinelearning #careergrowthtips #careerdevelopment #datascience
@itsallykrinsky
140.8K
TOLet’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? 👀
@tom.developer
1.0M
MAMaking building your own ML model a little less intimidating if it’s your first time :) #ai #machinelearning
@mar_antaya
102.6K
VOComment "ML" to get the links!
🧠 You Will Never Struggle With Machine Learning Again
📌 Watch these beginner-friendly ML tutorials:
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 FreeCodeCap
Stop getting lost in complex formulas and confusing jargon. These videos break down Machine Learning step by step — from basic intuition to real-world model building.
Whether you’re learning for AI projects, data science, or just starting your tech career, this is the fastest way to finally understand ML for real.
✨ Save this, share it, and turn confusion into clarity with hands-on Machine Learning skills.
@volkan.js
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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
336.1K
PI2025 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
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#machinelearning #ai #resources #tech #programming #womenintech #coder #programacao #latinasintech #swe
@pikacodes
706.6K
AILarge Language Models (LLMs) such as ChatGPT are based on neural networks called transformers, an architecture built using multiple attention mechanisms and multilayer perceptrons (MLPs).
These models process input text by learning context through self-attention mechanisms, which weighs the importance of each pair of words. This way, long sequences are no longer an issue.
This contextual understanding is passed through MLPs, which learn the representations and patterns of the sequence.
To generate text, the model generates a probability distribution of the next word; we choose the highest-probability word and keep predicting the next word, iterating to create a sentence or paragraph.
C: 3blue1brown
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@aibutsimple
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