1.0M
CHMachine learning relies heavily on mathematical foundations.
#tech #ml #explore #fyp #ai
@chrisoh.zip
#Content Machine Learning Models
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Трендовые Reels
(12)
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If you don’t receive the link you either need to follow first then comment, or your instagram is outdated.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
43.1K
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
134.0K
MABuilding an xgboost model! This is the type of model that we use for the f1 and the premier league model as well #machinelearning
@mar_antaya
1.0M
MAMaking building your own ML model a little less intimidating if it’s your first time :) #ai #machinelearning
@mar_antaya
335.3K
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
140.6K
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
40.0K
HE📌 “Confused about how to start your Machine Learning & AI journey? Here’s your complete roadmap from zero to job-ready! 💻✨”
No more scrolling through 100 videos — this 30 sec guide has everything you need to start & grow in ML!
Save 🔖 | Share 🤝 | Follow @helloworld_avani for more!
#machinelearning #artificialintelligence #pythonforbeginners #datasciencelearning #mlroadmap #techreels #codingjourney #learnwithme #careerinttech #reelsforstudents #studygramindia #trending #explorepage
@helloworld_avani
358.0K
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
40.6K
VOComment “ML” and I’ll send you the links👇
Machine learning doesn’t have to feel overwhelming. With the right guidance, complex topics like models, training, and prediction start making real sense 🧠
📌 Check out these beginner-friendly ML videos:
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 FreeCodeCamp
If terms like neural networks, supervised learning, or algorithms have ever confused you, these tutorials simplify everything into clear, practical explanations you can actually follow.
Instead of getting stuck in heavy math or abstract theory, you’ll build strong intuition around how machine learning works — from foundational concepts to real-world AI applications.
Whether you're interested in artificial intelligence, data science, Python projects, or future-proof tech skills, this is a powerful place to begin.
⭐ Save this so you don’t lose it, share it with someone learning AI, and start making machine learning finally click.
@volkan.js
23.2K
CO🚀 Machine Learning Roadmap (2025 Edition)
Unlock your journey into AI, Machine Learning & Deep Learning with this step-by-step guide designed for beginners to advanced learners.
📌 What You’ll Learn in This Video:
⚙️ Phase 1 – Core Foundation
📐 Math Basics | 🐍 Python Programming
🧹 Phase 2 – Data Preparation
🧽 Data Cleaning | 🎛 Feature Engineering | 📊 Visualization
🤖 Phase 3 – Machine Learning Concepts
🎯 Supervised & Unsupervised Learning | 🔍 Key Algorithms
🧪 Phase 4 – Model Optimization
📈 Cross-Validation | 🛠 Hyperparameter Tuning | 📍 Metrics
🧠 Phase 5 – Advanced ML
🌀 Neural Networks | 👁 Computer Vision | 💬 NLP
🚀 Phase 6 – Deployment & Real-World Use
🗃 Model Serialization | 🌐 APIs | ☁ Cloud | 🧩 MLOps
---
💡 Whether you're a beginner, student, or career switcher, this roadmap will help you become job-ready in AI and ML.
📚 Save this video and start learning step by step.
👇 Comment "ROADMAP" if you want a downloadable PDF version.
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🔍 Keywords:
Machine Learning Roadmap 2025, AI learning path, Deep Learning, Data Science Roadmap, Python for ML, Best way to learn AI, MLOps, Cloud AI skills.
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🔥 Hashtags:
#MachineLearning #AI #ArtificialIntelligence #DeepLearning #DataScience #Python #MLRoadmap #LearnML #TechCareers #Programming #NLP #ComputerVision #MLOps #DataEngineer #FutureSkills #Roadmap2025 #AIEducation #AIRevolution #CodingJourney
@codewithprashantt
946.8K
DAChoosing a Machine Learning Model Based on Inductive Biases
Inductive bias = the assumptions a model makes to learn patterns from data.
Linear Regression assumes linear relationships
SVMs assume linear boundaries (unless using kernels)
Decision Trees split orthogonally (axis-aligned)
MLPs (Multi-layer Perceptrons) can model complex functions but learn from data without strong built-in structure
CNNs use locality and translation invariance (good for images)
Transformers have lower inductive bias, they learn patterns from data, not from built-in assumptions like locality or hierarchy
Lower inductive bias = more flexibility, but more data needed to learn effectively.
Use the right model for your data structure!
#MachineLearning #AI #DeepLearning #Transformers #CNN #MLTips #DataScience #largelanguagemodels
@dairobotica
✨ Руководство по #Content Machine Learning Models
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