Language
English
عربي
Tiếng Việt
русский
français
español
日本語
한글
Deutsch
हिन्दी
简体中文
繁體中文
API
Home
How To Use
Language
English
عربي
Tiếng Việt
русский
français
español
日本語
한글
Deutsch
हिन्दी
简体中文
繁體中文
Home
Detail
@unkown726627:
unknown🏴☠️
Open In TikTok:
Region: DE
Sunday 01 November 2026 09:22:28 GMT
2254
243
2
15
Music
Download
No Watermark .mp4 (
1.89MB
)
No Watermark(HD) .mp4 (
1.89MB
)
Watermark .mp4 (
1.98MB
)
Music .mp3
Comments
آبا عدي الدرعاوي :
جزاك الله خيرا اخي
2026-11-01 16:42:59
3
To see more videos from user @unkown726627, please go to the Tikwm homepage.
Other Videos
#نهائي_كأس_الخليج #السعودية #الإمارات #ملعب_الإنماء #خليجي27
🤖 BUILD YOUR FIRST MACHINE LEARNING MODEL: PREDICT STUDENT SCORES! 📚 Want to build your first Machine Learning project but don’t know where to start? Let’s build something simple and practical: A Student Score Prediction Model! 🚀 Imagine predicting how many marks a student might score based on the number of hours they study. 🎯 Step 1: Understand the Problem Our goal is to predict a student’s exam score using their study hours. * Input (X): Hours studied * Output (Y): Exam score This is a Supervised Learning problem. 📊 Step 2: Create a Dataset Hours Studied Exam Score 1 35 2 40 3 50 4 55 5 65 6 70 7 80 8 90 🧹 Step 3: Prepare Your Data Use Python libraries: * Pandas: Load and organize data. * NumPy: Work with numerical values. * Scikit-learn: Build your ML model. 📈 Step 4: Choose Your Algorithm Start with Simple Linear Regression. The model learns the relationship between study hours and exam scores. Mathematical formula: [ y = mx + b ] Where: * y = Predicted score * x = Study hours * m = Slope * b = Intercept 🧠 Step 5: Train Your Model Split your dataset into training and testing data. Then use Scikit-learn: * LinearRegression() * model.fit(X_train, y_train) * model.predict(X_test) 🔮 Step 6: Make Your First Prediction What if a student studies for 9 hours? Your trained model can estimate their expected score! Remember: This is a prediction, not a guarantee. Real exam performance depends on many other factors. 📏 Step 7: Evaluate Your Model Learn these evaluation metrics: * MAE: Mean Absolute Error * MSE: Mean Squared Error * RMSE: Root Mean Squared Error * R² Score: How well the model explains score variation 🛠️ Your Tech Stack Python | Pandas | NumPy | Matplotlib | Scikit-learn | Google Colab Your first Machine Learning model doesn’t need to be complicated. Understand the problem, prepare the data, train the model, and evaluate its predictions. That’s how your ML journey begins! 🔥 💬 Challenge: Can you build this model and predict a student’s score after 9 hours of study? #MachineLearning #Python #DataScience #creatorsearchinsights #datascience
FOLLOW ME LOGO TEMPLATE | #edit #capcut #viral #instagram #greenscreen #tutorial #videoediting #creativeedit #hook #text #logo #viralvideo #foryou #officialtiktok #adobe #freelogo
How was your Halloween 2026? 🎃
#MaybellineКонкурс #MaybellineKazakhstan Достарр тіркеліп лайк басып кетейык🙏
كيف غابت ملامحُ سنواتٍ من عمرِنا لم يعد لها تفاصيلُ ولا ذكرياتٌ!!؟#تصويري📸 #موسيقى #explore #اكسبلورexplore #طرب
About
Robot
API
Legal
Privacy Policy