@cuidadosdalily:

Lily Silva

Open In TikTok:

Region: BR

Sunday 27 September 2026 21:15:38 GMT

561

3

0

0

Music

Download

No Watermark .mp4 (2.75MB) No Watermark(HD) .mp4 (1.78MB) Watermark .mp4 (2.91MB) Music .mp3

Comments

There are no more comments for this video.

To see more videos from user @cuidadosdalily, please go to the Tikwm homepage.

Other Videos

Machine Learning can look complicated at first. But the fundamentals are surprisingly structured. Here’s your quick reference. 👇 1️⃣ TYPES OF MACHINE LEARNING 🔵 Supervised Learning Learn from labeled data. Examples: 📈 Regression 🎯 Classification 🟣 Unsupervised Learning Find patterns in unlabeled data. Examples: 🔍 Clustering 📉 Dimensionality Reduction 🟢 Reinforcement Learning Learn through rewards and penalties. 2️⃣ COMMON ALGORITHMS 📈 REGRESSION 🔹 Linear Regression 🔹 Ridge / Lasso 🔹 Decision Tree Regression 🔹 Random Forest Regression 🔹 Gradient Boosting 🎯 CLASSIFICATION 🔹 Logistic Regression 🔹 KNN 🔹 Decision Tree 🔹 Random Forest 🔹 SVM 🔹 Gradient Boosting 🔍 CLUSTERING 🔹 K-Means 🔹 DBSCAN 🔹 Hierarchical Clustering 3️⃣ BASIC ML WORKFLOW 🔄 📥 Collect Data ⬇️ 🧹 Clean Data ⬇️ 🔎 EDA ⬇️ ⚙️ Feature Engineering ⬇️ ✂️ Train / Validation / Test Split ⬇️ 🤖 Train Model ⬇️ 📊 Evaluate ⬇️ 🎯 Tune ⬇️ 🚀 Deploy ⬇️ 📈 Monitor 4️⃣ OVERFITTING VS UNDERFITTING 🔴 Overfitting Model learns the training data too well. ➡️ Great training performance ➡️ Poor unseen-data performance 🟡 Underfitting Model is too simple to capture important patterns. ➡️ Poor training performance ➡️ Poor test performance 🟢 Good Fit Learns useful patterns and generalizes well. 5️⃣ BIAS VS VARIANCE 📉 High Bias → Model too simple 📈 High Variance → Model too sensitive to training data 🎯 Goal → Find a balance. 6️⃣ CLASSIFICATION METRICS 🎯 Accuracy Correct predictions / Total predictions 🔎 Precision Of predicted positives, how many were actually positive? 📡 Recall Of actual positives, how many did we identify? ⚖️ F1 Score Balances precision and recall. 📈 ROC-AUC Measures ranking/discrimination ability across classification thresholds. 7️⃣ REGRESSION METRICS 📏 MAE Average absolute error. 📐 MSE Average squared error. √ RMSE Square root of MSE. 📊 R² Measures how much variance in the target is explained by the model. 8️⃣ CROSS-VALIDATION 🔄 Instead of relying on one train/test split, divide the training data into multiple folds. Train on some folds → Validate on another → Repeat. Useful for: ✅ More reliable model evaluation ✅ Model comparison ✅ Hyperparameter tuning 9️⃣ FEATURE ENGINEERING ⚙️ Transform raw variables into useful features. Examples: 📅 Extract month from a date 🔢 Create ratios 🏷️ Encode categories 📏 Scale numerical features 🧩 Create interaction features 🔟 REGULARIZATION 🛡️ Helps reduce overfitting. L1 → Lasso L2 → Ridge Regularization adds a penalty that discourages overly complex model parameters. 🧠 QUICK ALGORITHM GUIDE 📈 Predict a number → Regression 🎯 Predict a class → Classification 👥 Find groups → Clustering 📉 Reduce dimensions → PCA 🌳 Strong tabular baseline → Random Forest / Gradient Boosting 🖼️ Images → CNN / Vision models 💬 Language → Transformers 🚀 THE GOLDEN RULE Don’t memorize algorithms. Understand: ❓ What problem does it solve? ❓ What assumptions does it make? ❓ When should I use it? ❓ How do I evaluate it? ❓ How does it fail? That’s what turns ML knowledge into practical ML skills. 🔥 📌 Save this cheat sheet for your next Machine Learning project. #MachineLearning #ML #DataScience #creatorsearchinsights #machinelearningengineer

Machine Learning can look complicated at first. But the fundamentals are surprisingly structured. Here’s your quick reference. 👇 1️⃣ TYPES OF MACHINE LEARNING 🔵 Supervised Learning Learn from labeled data. Examples: 📈 Regression 🎯 Classification 🟣 Unsupervised Learning Find patterns in unlabeled data. Examples: 🔍 Clustering 📉 Dimensionality Reduction 🟢 Reinforcement Learning Learn through rewards and penalties. 2️⃣ COMMON ALGORITHMS 📈 REGRESSION 🔹 Linear Regression 🔹 Ridge / Lasso 🔹 Decision Tree Regression 🔹 Random Forest Regression 🔹 Gradient Boosting 🎯 CLASSIFICATION 🔹 Logistic Regression 🔹 KNN 🔹 Decision Tree 🔹 Random Forest 🔹 SVM 🔹 Gradient Boosting 🔍 CLUSTERING 🔹 K-Means 🔹 DBSCAN 🔹 Hierarchical Clustering 3️⃣ BASIC ML WORKFLOW 🔄 📥 Collect Data ⬇️ 🧹 Clean Data ⬇️ 🔎 EDA ⬇️ ⚙️ Feature Engineering ⬇️ ✂️ Train / Validation / Test Split ⬇️ 🤖 Train Model ⬇️ 📊 Evaluate ⬇️ 🎯 Tune ⬇️ 🚀 Deploy ⬇️ 📈 Monitor 4️⃣ OVERFITTING VS UNDERFITTING 🔴 Overfitting Model learns the training data too well. ➡️ Great training performance ➡️ Poor unseen-data performance 🟡 Underfitting Model is too simple to capture important patterns. ➡️ Poor training performance ➡️ Poor test performance 🟢 Good Fit Learns useful patterns and generalizes well. 5️⃣ BIAS VS VARIANCE 📉 High Bias → Model too simple 📈 High Variance → Model too sensitive to training data 🎯 Goal → Find a balance. 6️⃣ CLASSIFICATION METRICS 🎯 Accuracy Correct predictions / Total predictions 🔎 Precision Of predicted positives, how many were actually positive? 📡 Recall Of actual positives, how many did we identify? ⚖️ F1 Score Balances precision and recall. 📈 ROC-AUC Measures ranking/discrimination ability across classification thresholds. 7️⃣ REGRESSION METRICS 📏 MAE Average absolute error. 📐 MSE Average squared error. √ RMSE Square root of MSE. 📊 R² Measures how much variance in the target is explained by the model. 8️⃣ CROSS-VALIDATION 🔄 Instead of relying on one train/test split, divide the training data into multiple folds. Train on some folds → Validate on another → Repeat. Useful for: ✅ More reliable model evaluation ✅ Model comparison ✅ Hyperparameter tuning 9️⃣ FEATURE ENGINEERING ⚙️ Transform raw variables into useful features. Examples: 📅 Extract month from a date 🔢 Create ratios 🏷️ Encode categories 📏 Scale numerical features 🧩 Create interaction features 🔟 REGULARIZATION 🛡️ Helps reduce overfitting. L1 → Lasso L2 → Ridge Regularization adds a penalty that discourages overly complex model parameters. 🧠 QUICK ALGORITHM GUIDE 📈 Predict a number → Regression 🎯 Predict a class → Classification 👥 Find groups → Clustering 📉 Reduce dimensions → PCA 🌳 Strong tabular baseline → Random Forest / Gradient Boosting 🖼️ Images → CNN / Vision models 💬 Language → Transformers 🚀 THE GOLDEN RULE Don’t memorize algorithms. Understand: ❓ What problem does it solve? ❓ What assumptions does it make? ❓ When should I use it? ❓ How do I evaluate it? ❓ How does it fail? That’s what turns ML knowledge into practical ML skills. 🔥 📌 Save this cheat sheet for your next Machine Learning project. #MachineLearning #ML #DataScience #creatorsearchinsights #machinelearningengineer

Лютый Занос с Апгрейда @Closetape #cs2 #cs #кс #кейсы #case

Лютый Занос с Апгрейда @Closetape #cs2 #cs #кс #кейсы #case

Punjabi beauty 🔥 hazir hojaen punjabi #kohatking #islamabad #poetry #tiktoklive @👑 KOHAT KING 👑 @TikTok Pakistan

Punjabi beauty 🔥 hazir hojaen punjabi #kohatking #islamabad #poetry #tiktoklive @👑 KOHAT KING 👑 @TikTok Pakistan

girls hide face poses😉🎀 . . . #viral #Fyp #poseideas #picture

girls hide face poses😉🎀 . . . #viral #Fyp #poseideas #picture

The original family and and their heads #theoriginals #thevampirediaries #tvd #themikaelsons #themikaelsonfamily

The original family and and their heads #theoriginals #thevampirediaries #tvd #themikaelsons #themikaelsonfamily

@Túi Xách - Vali 2T Cần Thơ - TÚI XÁCH VALI 2T Cần Thơ #TuiXachVaLi2TCanTho #NguyenHuuPhuocReview #CeoHongHotCanTho

@Túi Xách - Vali 2T Cần Thơ - TÚI XÁCH VALI 2T Cần Thơ #TuiXachVaLi2TCanTho #NguyenHuuPhuocReview #CeoHongHotCanTho

About

Robot
API

Legal

Privacy Policy