1. Home
  2. Detail

@homedecoration90: Making wooden shelf Like Follow and Share. #homedecoration #goviral #fyp #satisfyingvideos

Home Decor Hub
Home Decor Hub
Open In TikTok:
Region: PK
Thursday 28 May 2026 10:26:28 GMT
1236
28
0
2

Music

Download

No Watermark .mp4 (0.92MB) No Watermark(HD) .mp4 (0.92MB) Watermark .mp4 (0MB) Music .mp3

Comments

There are no more comments for this video.
To see more videos from user @homedecoration90, please go to the Tikwm homepage.

Other Videos

Don’t Just Learn Algorithms. Understand How They Work. 🧠 If you’re starting Machine Learning, don’t jump straight into LLMs. Build strong fundamentals first. 👇 📚 1️⃣ MACHINE LEARNING COURSE Learn the core concepts: 📊 Regression 🎯 Classification 🔍 Clustering 🌳 Decision Trees 🌲 Random Forest ⚡ Gradient Boosting 📈 Model Evaluation 📖 2️⃣ HANDS-ON MACHINE LEARNING A great resource for connecting theory with practical implementation. Focus on: 🐍 Python 📊 Scikit-learn 🧠 Neural Networks 🤖 ML workflows 🎓 3️⃣ ANDREW NG’S MACHINE LEARNING COURSES Excellent for understanding the intuition behind ML algorithms. Learn: 📈 Supervised Learning 🔍 Unsupervised Learning ⚙️ Model Training 📊 Evaluation 🧠 Neural Networks 🧮 4️⃣ MATH FOR MACHINE LEARNING Build the mathematical foundation: 📊 Probability 📈 Statistics 🔢 Linear Algebra 📉 Calculus 🎯 Optimization Don’t memorize formulas. Understand why they matter in ML. 🐍 5️⃣ SCIKIT-LEARN DOCUMENTATION Don’t only watch tutorials. Learn directly from documentation and examples. Study: 🔹 Preprocessing 🔹 Pipelines 🔹 Cross-validation 🔹 Model selection 🔹 Metrics 🧪 6️⃣ KAGGLE Use Kaggle to practice with real datasets. Start with: 📊 Titanic 🏠 House Prices ❤️ Classification datasets Then move to more challenging problems. 🧠 7️⃣ STATQUEST Perfect when a Machine Learning concept feels confusing. Use it to build intuition for: 📊 Statistics 🌳 Decision Trees 🎯 Classification 🧠 Neural Networks 📈 Model Evaluation 🗺️ HOW TO USE THESE RESOURCES Don’t consume everything at once. Follow: Theory 📚 ⬇️ Math intuition 🧮 ⬇️ Implementation 🐍 ⬇️ Real Dataset 📊 ⬇️ Project 🚀 ⬇️ Explain what you learned 🧠 🔥 THE REAL SECRET You don’t become good at Machine Learning by watching 100 hours of courses. You become good by repeatedly doing: Learn → Implement → Experiment → Fail → Debug → Explain → Build 📌 Save these resources for your ML journey. #MachineLearning #DataScience #AI #creatorsearchinsights #machinelearningengineer
Don’t Just Learn Algorithms. Understand How They Work. 🧠 If you’re starting Machine Learning, don’t jump straight into LLMs. Build strong fundamentals first. 👇 📚 1️⃣ MACHINE LEARNING COURSE Learn the core concepts: 📊 Regression 🎯 Classification 🔍 Clustering 🌳 Decision Trees 🌲 Random Forest ⚡ Gradient Boosting 📈 Model Evaluation 📖 2️⃣ HANDS-ON MACHINE LEARNING A great resource for connecting theory with practical implementation. Focus on: 🐍 Python 📊 Scikit-learn 🧠 Neural Networks 🤖 ML workflows 🎓 3️⃣ ANDREW NG’S MACHINE LEARNING COURSES Excellent for understanding the intuition behind ML algorithms. Learn: 📈 Supervised Learning 🔍 Unsupervised Learning ⚙️ Model Training 📊 Evaluation 🧠 Neural Networks 🧮 4️⃣ MATH FOR MACHINE LEARNING Build the mathematical foundation: 📊 Probability 📈 Statistics 🔢 Linear Algebra 📉 Calculus 🎯 Optimization Don’t memorize formulas. Understand why they matter in ML. 🐍 5️⃣ SCIKIT-LEARN DOCUMENTATION Don’t only watch tutorials. Learn directly from documentation and examples. Study: 🔹 Preprocessing 🔹 Pipelines 🔹 Cross-validation 🔹 Model selection 🔹 Metrics 🧪 6️⃣ KAGGLE Use Kaggle to practice with real datasets. Start with: 📊 Titanic 🏠 House Prices ❤️ Classification datasets Then move to more challenging problems. 🧠 7️⃣ STATQUEST Perfect when a Machine Learning concept feels confusing. Use it to build intuition for: 📊 Statistics 🌳 Decision Trees 🎯 Classification 🧠 Neural Networks 📈 Model Evaluation 🗺️ HOW TO USE THESE RESOURCES Don’t consume everything at once. Follow: Theory 📚 ⬇️ Math intuition 🧮 ⬇️ Implementation 🐍 ⬇️ Real Dataset 📊 ⬇️ Project 🚀 ⬇️ Explain what you learned 🧠 🔥 THE REAL SECRET You don’t become good at Machine Learning by watching 100 hours of courses. You become good by repeatedly doing: Learn → Implement → Experiment → Fail → Debug → Explain → Build 📌 Save these resources for your ML journey. #MachineLearning #DataScience #AI #creatorsearchinsights #machinelearningengineer
For AI / ML Engineering Jobs in 2026 🤖 Training a model in a notebook is only the beginning. The real engineering challenge is: Model → API → Deployment → Monitoring → Production Save these 7 topics. 👇 1️⃣ ML PROJECT LIFECYCLE Learn how a real ML project moves from problem definition → data → model → deployment → monitoring. 🎯 Focus on: • Problem scoping • Data requirements • Baselines • Deployment patterns • Monitoring 2️⃣ DEPLOYING ML MODELS Learn the different ways to serve predictions: 📦 Batch prediction ⚡ Real-time inference 🌐 REST APIs 🚀 Model-as-a-service 📈 Scaling Full Stack Deep Learning’s deployment lecture covers these production patterns in detail. (Class Central⁠) 3️⃣ FASTAPI + ML MODEL SERVING Turn your trained model into an actual API. Learn: 🐍 FastAPI 📡 REST endpoints 📥 Request validation 📤 Prediction responses ⚡ Latency 4️⃣ DOCKERIZE YOUR ML APPLICATION 🐳 Your model works on your laptop. Now make it work consistently elsewhere. Learn: 📦 Dockerfile 🧱 Images 🚢 Containers 🔧 Dependencies ⚙️ Environment configuration 5️⃣ TESTING & CI/CD 🧪 Production ML needs more than model accuracy. Learn: ✅ Unit testing ✅ API testing ✅ Data validation ✅ Regression testing 🔄 CI/CD Full Stack Deep Learning specifically includes ML testing, continuous integration, and deployment. (Full Stack Deep Learning⁠) 6️⃣ MONITORING & MODEL DRIFT 📊 A model can work perfectly today and degrade tomorrow. Learn: 📈 Performance monitoring 🔍 Data drift 🎯 Model drift ⚡ Latency 🚨 Alerts 📊 Logs & metrics Production ML courses commonly treat monitoring and continual improvement as core parts of the lifecycle. (Coursera⁠) 7️⃣ END-TO-END ML SYSTEM 🚀 Now combine everything: Data ⬇️ Training ⬇️ Experiment Tracking ⬇️ Model Registry ⬇️ FastAPI ⬇️ Docker ⬇️ Cloud Deployment ⬇️ Monitoring ⬇️ 🔄 Retraining For a more advanced 2026 perspective, CMU’s Spring 2026 Machine Learning in Production / AI Engineering course covers the lifecycle from prototype models through deployed systems, including ML, LLMs, agents, MLOps, and responsible AI. (GitHub⁠) 🧠 THE SKILL STACK 🐍 Python 📊 Scikit-learn / PyTorch 🌐 FastAPI 🐳 Docker 🔄 CI/CD 📦 MLflow ☁️ Cloud 📊 Monitoring 🗄️ Data Pipelines THE GOAL: ❌ “I trained a model.” ⬇️ ✅ “I built, deployed, tested, monitored, and maintained an ML system.” That’s the difference between a notebook ML project and production ML engineering. 🚀 #Mac#MachineLearningO#MLOpsE#AIEngineering #cre#creatorsearchinsightst#datascience
For AI / ML Engineering Jobs in 2026 🤖 Training a model in a notebook is only the beginning. The real engineering challenge is: Model → API → Deployment → Monitoring → Production Save these 7 topics. 👇 1️⃣ ML PROJECT LIFECYCLE Learn how a real ML project moves from problem definition → data → model → deployment → monitoring. 🎯 Focus on: • Problem scoping • Data requirements • Baselines • Deployment patterns • Monitoring 2️⃣ DEPLOYING ML MODELS Learn the different ways to serve predictions: 📦 Batch prediction ⚡ Real-time inference 🌐 REST APIs 🚀 Model-as-a-service 📈 Scaling Full Stack Deep Learning’s deployment lecture covers these production patterns in detail. (Class Central⁠) 3️⃣ FASTAPI + ML MODEL SERVING Turn your trained model into an actual API. Learn: 🐍 FastAPI 📡 REST endpoints 📥 Request validation 📤 Prediction responses ⚡ Latency 4️⃣ DOCKERIZE YOUR ML APPLICATION 🐳 Your model works on your laptop. Now make it work consistently elsewhere. Learn: 📦 Dockerfile 🧱 Images 🚢 Containers 🔧 Dependencies ⚙️ Environment configuration 5️⃣ TESTING & CI/CD 🧪 Production ML needs more than model accuracy. Learn: ✅ Unit testing ✅ API testing ✅ Data validation ✅ Regression testing 🔄 CI/CD Full Stack Deep Learning specifically includes ML testing, continuous integration, and deployment. (Full Stack Deep Learning⁠) 6️⃣ MONITORING & MODEL DRIFT 📊 A model can work perfectly today and degrade tomorrow. Learn: 📈 Performance monitoring 🔍 Data drift 🎯 Model drift ⚡ Latency 🚨 Alerts 📊 Logs & metrics Production ML courses commonly treat monitoring and continual improvement as core parts of the lifecycle. (Coursera⁠) 7️⃣ END-TO-END ML SYSTEM 🚀 Now combine everything: Data ⬇️ Training ⬇️ Experiment Tracking ⬇️ Model Registry ⬇️ FastAPI ⬇️ Docker ⬇️ Cloud Deployment ⬇️ Monitoring ⬇️ 🔄 Retraining For a more advanced 2026 perspective, CMU’s Spring 2026 Machine Learning in Production / AI Engineering course covers the lifecycle from prototype models through deployed systems, including ML, LLMs, agents, MLOps, and responsible AI. (GitHub⁠) 🧠 THE SKILL STACK 🐍 Python 📊 Scikit-learn / PyTorch 🌐 FastAPI 🐳 Docker 🔄 CI/CD 📦 MLflow ☁️ Cloud 📊 Monitoring 🗄️ Data Pipelines THE GOAL: ❌ “I trained a model.” ⬇️ ✅ “I built, deployed, tested, monitored, and maintained an ML system.” That’s the difference between a notebook ML project and production ML engineering. 🚀 #Mac#MachineLearningO#MLOpsE#AIEngineering #cre#creatorsearchinsightst#datascience
Nhớ ngôi nhà đó đi từ giờ khó mà nhìn thấy nữa #xh#phd#phc #xuhuong
Nhớ ngôi nhà đó đi từ giờ khó mà nhìn thấy nữa #xh#phd#phc #xuhuong
Raaliyo 👸🏽🫶🏼❤️‍🩹#muqdisho_somalia #viralvideo #foryoupage #capcut_edit #funnyvideos
Raaliyo 👸🏽🫶🏼❤️‍🩹#muqdisho_somalia #viralvideo #foryoupage #capcut_edit #funnyvideos
#путькистине #истинажизни
#путькистине #истинажизни
Bomba Submersa De Óleo Diesel #tiktokshop1010
Bomba Submersa De Óleo Diesel #tiktokshop1010

About

Legal