456.5K
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
#Moa Machine Learning Tool
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1.2M
CHMachine learning relies heavily on mathematical foundations.
#tech #ml #explore #fyp #ai
@chrisoh.zip
59.2K
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
53.5K
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
0
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
511.8K
ITthis is the software side of robotics of course there’s a whole other piece to make the robots work #ai #machinelearning #datascientist #machinelearningengineer #robotics #techcareer #careergrowthtips
@itsallykrinsky
147.7K
STYou can learn machine learning by building, not just reading books.
#machinelearning #coding #learning
@stephen_blum_code
41.6K
MXA friend got me into machine learning through robotics (LeRobot SE101) 🤖
Now I’m diving into things like neural networks, control, and building real systems…
and I started reading books like AI for Robotics.
But honestly, it still feels overwhelming. There are so many directions.
Everyone says “learn ML” — but what actually makes you GOOD at it?
If you’ve been through this:
how did you figure out what to focus on, and how did you gain real experience?
#machinelearning #ai #robotics #huggingface #learning
@mxjclabs
945.3K
WOSteve brunton is sooo GOATEDDD !!!
#machinelearning #datascience #stem #artificialintelligence
@workiniterations
41.7K
OLЧто умеет Имо
часть 1 Утренние процедуры
#роботимо
@olgakoyakhova
125.3K
BRRun AI models locally like a pro for FREE!
Run popular open source models like OpenAI GPT OSS, Google Gemma, Alibaba’s Qwen and Deepseek R1
@brendenvogt
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