456.4K
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
#Machine Learning Supervised Learning
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1.2M
CHMachine learning relies heavily on mathematical foundations.
#tech #ml #explore #fyp #ai
@chrisoh.zip
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If you don’t receive the link you either need to follow first then comment, or your instagram is outdated.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
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
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
30.1K
VOComment “ML” for the links.
You will never feel confused about machine learning again.
📌 Learn machine learning the right way with these essential resources:
1️⃣ Infinite Codes – Learn Machine Learning Like a GENIUS and Not Waste Time
2️⃣ Shaw Talebi – ML Foundations for AI Engineers (in 34 Minutes)
3️⃣ AI For Beginners – All Machine Learning Models Clearly Explained
4️⃣ Infinite Codes – All Machine Learning Concepts Explained in 22 Minutes
5️⃣ Infinite Codes – 22 Machine Learning Projects That Will Make You a God at Data Science
This machine learning learning path covers core ML concepts like supervised learning, unsupervised learning, regression, classification, clustering, neural networks, model training, evaluation metrics, feature engineering, datasets, and real-world machine learning workflows.
These videos explain how machine learning actually works behind the scenes, how models learn from data, how different ML algorithms are used in practice, and how to apply machine learning concepts through real projects instead of just theory.
Whether you’re a complete beginner learning machine learning from scratch, a programmer transitioning into AI, a data science beginner, or preparing for machine learning or AI engineer interviews, this roadmap gives you a clear and structured understanding of modern machine learning.
Save this post, share it with anyone learning AI or ML, and start building real machine learning projects with confidence.
@volkan.js
144.3K
SUIf you were starting Machine Learning in 2026, what would your roadmap look like?
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#MachineLearning
#MLJourney
#LearnML
#AI2026
#DataScienceJourney
@sundaskhalidd
18.0K
AAMachine Learning Projects with implementation 👨💻💡
Get access to 150+ machine learning projects with step-by-step guides for all skill levels. Whether you’re a beginner or an expert, these projects cover everything from predictive analytics and image classification to sentiment analysis and anomaly detection.
Each project includes:
• Practical Implementation: Real-world applications with easy-to-follow code.
• Customizable Ideas: Modify projects to fit your learning goals.
• Diverse Domains: NLP, computer vision, recommendation systems, and more.
Comment Projects and I’ll share the link directly! Start building and leveling up your ML skills now!
[Machine Learning, ML Projects, Deep Learning, Data Science, AI Projects, Data Science Projects, Python , Data Analytics]
#MachineLearning #MLProjects #DataScience #AIProjects #DeepLearning #DataScienceProjects #ArtificialIntelligence #MachineLearningProjects #AI #TechSkills #LearnAI #projects #hiring #aasifcodes #jobs
@aasifcodes
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
24.3K
ITback with another industry with great applications for ml!! #ai #machinelearning #datascientist #machinelearningengineer #techcareer #careergrowthtips
@itsallykrinsky
147.6K
STYou can learn machine learning by building, not just reading books.
#machinelearning #coding #learning
@stephen_blum_code
125.4K
SAAI vs Machine Learning VS Deep Learning BREAKDOWN 😤 #ai #ml #tech #fyp
@sajjaad.khader
✨ #Machine Learning Supervised Learning Discovery Guide
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