570.6K
THNeural networks and machine learning.
@theartificialintelligence
#Machine Learning Algorithm Examples
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1.2M
CHMachine learning relies heavily on mathematical foundations.
#tech #ml #explore #fyp #ai
@chrisoh.zip
316.4K
COMachine Learning, explained by cats. #cat #code #ai #machinelearning #algorithm
@coding.kitty
158.2K
EQThe Secret Behind Machine Learning Predictions! Ever wondered how machines make binary decisions? This video breaks down Logistic Regression using the Sigmoid Function. We visualize how the weight (w) controls the steepness of the curve and how the bias (b) shifts it along the x-axis. See how Cross-Entropy (CE) Loss is minimized to find the optimal fit for your data points. Finally, we explore the decision boundary at P=0.5, which separates predictions into Class 0 and Class 1. Perfect for data science students and machine learning enthusiasts looking for a quick, intuitive visualization of classification algorithms and mathematical optimization. #LogisticRegression #MachineLearning #SigmoidFunction #Math #Manim
@equationsinmotion
49.4K
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
1.4M
INGraham scan algorithm animated!
Full video in the YouTube channel
#algorithms #computerscience #programming
@inside.code
72.9K
CLFollow @cloud_x_berry for more info
#MachineLearning #MLAlgorithms #DataScience #AI #LearnML
machine learning algorithms explained, linear regression model, logistic regression classification, decision tree algorithm, support vector machine svm, knn algorithm explained, dimensionality reduction techniques, random forest algorithm, k means clustering algorithm, naive bayes classifier, supervised learning algorithms, unsupervised learning algorithms, classification vs regression, ml basics for beginners, data science concepts, ai model types, feature engineering basics, model selection techniques, ml interview preparation, machine learning fundamentals
@cloud_x_berry
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
3.0M
ITcomment ‘AI’ and I’ll send you the link in your DMs
this is such a great resource to guide you on your AI/ML journey!
#techcareer #ai #machinelearning #careergrowthtips #datascience #coding
@itsallykrinsky
315.6K
DAThe Kernel Trick explained in 75 seconds ✨
Ever wondered how machine learning separates data that seems impossible to separate?
Here's the secret:
→ In 2D, no line can separate this data
→ But lift it into 3D...
→ A simple plane does the job perfectly
This is why Support Vector Machines are so powerful 🧠
Save this for later 🔖
—
Follow @dailymathvisuals for daily ML & math visualizations
#machinelearning #artificialintelligence #datascience #python #coding #svm #kerneltrick #ai #tech #programming #learnwithreels #educationalreels #mathvisualization #deeplearning #engineering
@dailymathvisuals
23.8K
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
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💡 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
447.7K
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
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