1.2M
CHMachine learning relies heavily on mathematical foundations.
#tech #ml #explore #fyp #ai
@chrisoh.zip
#How Does Machine Learning Work
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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If you don’t receive the link you either need to follow first then comment, or your instagram is outdated.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
456.3K
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
59.1K
EMThese are some of the best beginner-friendly resources I’ve found to actually understand machine learning.
Nothing overly complicated, just what you need to get the concepts and start building.
Comment ML and I’ll send you all the resources.
@emrcodes
265.6K
MAWhat is Machine leaning part 1.
#machinelearning #ai
@machine_learning_samane
116.4K
VOComment “ML” and I’ll send you the links!
You don’t need expensive AI or machine learning bootcamps to understand how ML models and large language models actually work. Some of the best machine learning tutorials, deep learning resources, and AI courses online are completely free — and often better than paid programs.
📌 3 High-Impact Resources to Actually Learn Machine Learning & AI:
1️⃣ All Machine Learning Concepts Explained in 22 Minutes – Infinite Codes
A fast-paced breakdown of core machine learning concepts including supervised vs unsupervised learning, regression, classification, neural networks, and deep learning. Perfect for quickly understanding how ML models work without getting lost in theory.
2️⃣ Stanford CS229: Machine Learning – Building Large Language Models (LLMs)
A more advanced lecture covering how modern AI systems and LLMs are built. It explains key concepts like training data, model architecture, optimization, and how large-scale machine learning systems power tools like ChatGPT.
3️⃣ Machine Learning for Beginners (GitHub Repository)
A structured, hands-on resource that walks through machine learning step by step. Includes real projects, explanations, and practical implementations so you can actually apply ML concepts and build your own models.
These resources cover essential machine learning concepts like supervised learning, unsupervised learning, neural networks, deep learning, large language models (LLMs), training data, model optimization, and real-world AI applications.
Whether you’re a developer getting into AI, preparing for machine learning interviews, or building intelligent systems, understanding machine learning is a must-have skill.
Save this, share it, and start learning how AI actually works. 🤖
@volkan.js
125.4K
SAAI vs Machine Learning VS Deep Learning BREAKDOWN 😤 #ai #ml #tech #fyp
@sajjaad.khader
144.1K
SUIf you were starting Machine Learning in 2026, what would your roadmap look like?
ㅤ
#MachineLearning
#MLJourney
#LearnML
#AI2026
#DataScienceJourney
@sundaskhalidd
53.4K
LILet’s see if I can cover the ML pipeline in 60 seconds ⏰😅
Machine learning isn’t just training a model. A production ML lifecycle typically looks like this:
1️⃣ Define the problem & objective
2️⃣ Collect and (if needed) label data
3️⃣ Split into train / validation / test sets
4️⃣ Data preprocessing & feature engineering
5️⃣ Train the model (forward pass + backpropagation in deep learning)
6️⃣ Evaluate on held-out data to measure generalization
7️⃣ Hyperparameter tuning (learning rate, architecture, etc.)
8️⃣ Final testing before release
9️⃣ Deploy (batch inference or real-time serving behind an API)
🔟 Monitor for data drift, concept drift, latency, cost, and reliability
1️⃣1️⃣ Retrain when performance degrades
Training updates weights.
Evaluation measures performance.
Deployment serves predictions.
Monitoring keeps the system healthy.
It’s not linear. It’s a loop.
And once you move beyond a single experiment, that loop becomes a systems problem.
At scale, the challenge isn’t just modeling … it’s building reliable, scalable infrastructure that supports the entire lifecycle.
Curious if this type of content is helpful! Lmk in the comments & as always Happy Building! 🤍
@lindavivah
36.0K
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
348.2K
PRhere’s a full roadmap for anyone who wants to get into machine learning but doesn’t know where to start. covers the math, tools, courses, and projects that actually matter— no fluff, just what’ll get you from zero to real-world skills.
if you want the actual roadmap doc itself written up, either comment below or shoot me a DM, i’ll send it ASAP.
hope that helps. 🤝
#study #viral #education #math #advice #university #studyhelp #cs #exam #leetcode #research #machinelearning #deeplearning
@pranavpatnaik_
1.6K
CODay 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.
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#MachineLearning
#ArtificialIntelligence
#CodeLoopa
#LearnAI
#TechExplained
@codeloopaa
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