udemy-complete-machine-learning-data-science-bootcamp-2022-2022-5-1

Creating Machine Learning Models\ 16:30 NEW Choosing The Right Model For Your Data 2 (Regression) 27:51 Quick Tip How ML Algorithms Work 29:17 Choosing The Right Model For Your Data 3 (Classification) 42:02 Fitting A Model To The Data 48:48 Making Predictions With Our Model 57:13 predict() vs predict_proba() 1:05:46 NEW Making Predictions With Our Model (Regression) 1:14:34 NEW Evaluating A Machine Learning Model (Score) Part 1 1:24:15 NEW Evaluating A Machine Learning Model (Score) Part 2 1:31:03 Evaluating A Machine Learning Model 2 (Cross Validation) 1:44:19 Evaluating A Classification Model 1 (Accuracy) 1:49:05 Evaluating A Classification Model 2 (ROC Curve) 1:58:10 Evaluating A Classification Model 3 (ROC Curve) 2:05:55 Evaluating A Classification Model 4 (Confusion Matrix) 2:16:56 NEW Evaluating A Classification Model 5 (Confusion Matrix) 2:31:19 Evaluating A Classification Model 6 (Classification Report) 2:41:36 NEW Evaluating A Regression Model 1 (R2 Score) 2:51:35 NEW Evaluating A Regression Model 2 (MAE) 2:58:58 NEW Evaluating A Regression Model 3 (MSE) 3:08:47 NEW Evaluating A Model With Cross Validation and Scoring Parameter 3:34:06 NEW Evaluating A Model With Scikit-learn Functions 3:48:08 Improving A Machine Learning Model 3:59:25 Tuning Hyperparameters 4:22:41 Tuning Hyperparameters 2 4:37:04 Tuning Hyperparameters 3 4:52:04 Quick Tip Correlation Analysis 4:54:32 Saving And Loading A Model 5:02:01 Saving And Loading A Model 2 5:08:21 Putting It All Together 5:28:41 Putting It All Together 2 Project 1 Supervised Learning (Classification)\ 5:40:15 Section Overview 5:42:25 Project Overview 5:48:34 Project Environment Setup 5:59:34 Optional Windows Project Environment Setup 6:04:26 Step 1~4 Framework Setup 6:16:32 Getting Our Tools Ready 6:25:37 Exploring Our Data 6:34:10 Finding Patterns 6:44:13 Finding Patterns 2 7:01:01 Finding Patterns 3 7:14:38 Preparing Our Data For Machine Learning 7:23:30 Choosing The Right Models 7:33:45 Experimenting With Machine Learning Models 7:40:17 TuningImproving Our Model 7:54:06 Tuning Hyperparameters 8:05:34 Tuning Hyperparameters 2 8:17:24 Tuning Hyperparameters 3 8:24:31 Evaluating Our Model 8:35:31 Evaluating Our Model 2 8:41:26 Evaluating Our Model 3 8:50:16 Finding The Most Important Features 9:06:23 Reviewing The Project Project 2 Supervised Learning (Time Series Data)\ 9:15:37 Section Overview 9:16:44 Project Overview 9:21:09 Project Environment Setup 9:32:01 Step 1~4 Framework Setup 9:40:38 Exploring Our Data 9:54:54 Exploring Our Data 2 10:01:11 Feature Engineering 10:16:36 Turning Data Into Numbers 10:32:14 Filling Missing Numerical Values 10:45:03 Filling Missing Categorical Values 10:53:31 Fitting A Machine Learning Model 11:00:47 Splitting Data 11:10:48 Custom Evaluation Function 11:22:01 Reducing Data 11:32:38 RandomizedSearchCV 11:42:10 Improving Hyperparameters 11:50:21 Preproccessing Our Data