34.4K
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
#What Is Try In Python
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250
_EMost people think Machine Learning models are magical black boxes.
They’re not.
A model is simply math that learns patterns from data.
Inputs → Math → Prediction.
Behind every model are four key components:
• Parameters
• Architecture
• Loss Function
• Hyperparameters
The real goal isn't memorizing data —
it's generalizing to new data.
Comment MODEL if you want the notes 📘
Follow the series for the next episode 🚀
#MachineLearning
#ArtificialIntelligence
#DataScience
#MLSeries
#LearnMachineLearning
#AIEngineer
#TechEducation
#CodingCommunity
#DeepLearning
#AIForStudents
#FutureOfAI
#EngineeringStudents
#PythonProgramming
#AIContent
@_explorewithshama
53.4K
VOComment “ML” and I’ll send you the links
🚀 3 Machine Learning Videos Every Beginner Should Watch
If you’re learning machine learning, AI, data science, or Python for ML, these videos will save you months of confusion and put you on the right path 👇
No fluff. No math overload. Just clear explanations of machine learning fundamentals, algorithms, and concepts that actually make sense.
1️⃣ Infinite Codes – Learn Machine Learning Like a GENIUS
A smart roadmap for learning machine learning, AI, and data science efficiently
2️⃣ Infinite Codes – All Machine Learning Concepts in 22 Minutes
Super-fast breakdown of supervised learning, unsupervised learning, neural networks, and ML basics ⚡
3️⃣ freeCodeCamp – Machine Learning for Everybody
A complete beginner-friendly machine learning course covering Python, models, and real-world concepts
If machine learning feels overwhelming, confusing, or too theoretical, this is the best place to start.
💾 Save this reel for later
👥 Share with someone learning AI, ML, or data science
@volkan.js
35.6K
WOI made these mistakes when I started with machine learning.
Sharing them so you don’t have to.
#machinelearning #datascience #mljourney #learningtech #ai studentsintech
@workiniterations
124.8K
WOThe details are provided in the pinned comment.
These projects aren't meant to be impressive portfolio pieces. They're designed to build transferable intuition that applies regardless of which specific tools or models you use later.
Libraries and frameworks change constantly. Fundamental understanding doesn't.
I break ML down from first principles and explain the why, not just the how.
Follow for real understanding :)
#MachineLearning
#LearnML
#DataScience
#MLProjects
#AIEngineering
@workiniterations
1.9K
THMachine Learning doesn’t have to be confusing
Here’s a simple cheatsheet to quickly understand the 3 main types of ML algorithms:
✅ Supervised → Learn with answers
✅ Unsupervised → Find hidden patterns
✅ Reinforcement → Learn using rewards
Master these basics and you’ve already won half the ML battle
Save this post for quick revision
Share with your friends learning Data Science
#MachineLearning
#DataScience
#AIlearning
#LearnAI
#TechSimplified
@thesravandev
424
_EThere are only 3 ways a machine can learn — and understanding this makes Machine Learning simple.
Supervised → Labelled data → Predict
Unsupervised → No labels → Discover patterns
Reinforcement → Rewards → Optimize decisions
If you’re serious about AI, you must understand this foundation.
Comment “ML02” and I’ll send the full notes 📘
Episode 03 coming next 🚀
#MachineLearning
#ArtificialIntelligence
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#LearnAI
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@_explorewithshama
0
ENStarting a 20-Day Machine Learning Sprint.
Not watching random videos.
Not jumping between tutorials.
Structured revision.
Clear concepts.
Real ML system design thinking.
20 days of discipline.
Let’s see the version of me on Day 20
#MachineLearning
#MLJourney
#ArtificialIntelligence
#MLRevision
#LearningInPublic
#EngineeringStudent
#PlacementPreparation
#FutureMLEngineer
#TechJourney
#AIEngineer
#StudyDiscipline
@engineer_ke_duniya
264
LEMaster these 8 ML algorithms and you’ll solve most real-world problems—even before Deep Learning.
#machinelearning #AI #mlbasic #datascience #support
@learn_with_sandeep
442
VIMost people learn Machine Learning…
but get stuck when it comes to practice.
DSA has LeetCode.
ML deserves the same.
If you’re serious about AI, ML, and real-world skills, this is for you.
💬 Comment ML or DM AI to get the website
📌 Save this for later
🚀 Follow for more ML & AI practice resources
#MachineLearning #AIPractice #DataScience #MLJourney #VidyaNex
@vidyanex_consulting
430
_EMost beginners fail in Machine Learning because they jump straight into coding.
But every real ML project follows the same 6-step workflow:
Define → Collect → Prepare → Build → Evaluate → Deploy
And here’s the truth nobody tells you:
Data Preparation alone takes 40% of the work.
Master the workflow first.
Algorithms come later.
Comment “ML03” for full notes 📘
Follow for Episode 04 🚀
#MachineLearning
#ArtificialIntelligence
#DataScience
#MLWorkflow
#AIEngineer
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@_explorewithshama
894
BEComment "LINK" to get links!
🚀 Want to learn Machine Learning in a way that actually sticks? This beginner friendly roadmap helps you go from zero knowledge to understanding real world machine learning, artificial intelligence, and data science concepts step by step.
🎓 Learn Machine Learning Like a Genius
Perfect starting point if you feel overwhelmed by AI and machine learning. You will learn how to study machine learning efficiently, what topics to focus on first, and how to avoid wasting time while building strong fundamentals in Python, math, and algorithms.
📘 The Complete Machine Learning Roadmap
Now deepen your knowledge. This resource explains supervised learning, unsupervised learning, neural networks, deep learning basics, model training, and evaluation. It gives you a clear path to become confident in data science and AI development.
💻 Machine Learning Explained in 100 Seconds
Time to simplify everything. This quick overview reinforces the core ideas behind machine learning and artificial intelligence so you clearly understand how models learn from data and make predictions.
💡 With these Machine Learning resources you will:
Understand core machine learning and AI concepts
Learn the roadmap to become a data scientist or ML engineer
Build strong foundations in algorithms and model training
Prepare for tech interviews in AI and data science roles
If you are serious about artificial intelligence, data science, or becoming a machine learning engineer, this roadmap will give you clarity and direction.
📌 Save this post so you do not lose the roadmap.
💬 Comment "LINK" and I will send you all the links.
👉 Follow for more content on AI, machine learning, and software engineering.
@berk.py
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