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2.3 GB | 11min 14s | mp4 | 1280X720 | 16:9
Genre:eLearning |Language:English
Files Included :
001 Course Introduction.mp4 (29.33 MB)
002 About This Series.mp4 (3.64 MB)
003 Course Structure & Outline.mp4 (11.83 MB)
006 Introducing the Course Project.mp4 (4.47 MB)
007 Setting Expectations.mp4 (10.09 MB)
008 Jupyter Installation & Launch.mp4 (23.83 MB)
001 What is Data Science.mp4 (5.22 MB)
002 The Data Science Skillset.mp4 (3.36 MB)
003 What is Machine Learning.mp4 (6.07 MB)
004 Common Machine Learning Algorithms.mp4 (4.31 MB)
005 Data Science Workflow.mp4 (2.35 MB)
006 Data Prep & EDA Steps.mp4 (8.43 MB)
007 Modeling Steps.mp4 (7.12 MB)
008 Classification Modeling.mp4 (3.56 MB)
009 Key Takeaways.mp4 (2.91 MB)
001 Classification 101.mp4 (12.31 MB)
002 Goals of Classification.mp4 (4.26 MB)
003 Types of Classification.mp4 (10.53 MB)
004 Classification Modeling Workflow.mp4 (6.49 MB)
005 Key Takeaways.mp4 (3.02 MB)
001 EDA For Classification.mp4 (7.59 MB)
002 Defining a Target.mp4 (9.65 MB)
003 DEMO Defining a Target.mp4 (36.24 MB)
004 Exploring the Target.mp4 (12.02 MB)
005 Exploring the Features.mp4 (4.74 MB)
006 DEMO Exploring the Features.mp4 (10.96 MB)
007 ASSIGNMENT Exploring the Target & Features.mp4 (7.98 MB)
008 SOLUTION Exploring the Target & Features.mp4 (22.53 MB)
009 Correlation.mp4 (10.33 MB)
010 PRO TIP Correlation Matrix.mp4 (5.62 MB)
011 DEMO Correlation Matrix.mp4 (31.34 MB)
012 Feature-Target Relationships.mp4 (21.22 MB)
013 Feature-Feature Relationships.mp4 (5.89 MB)
014 PRO TIP Pair Plots.mp4 (37.14 MB)
015 ASSIGNMENT Exploring Relationships.mp4 (5.11 MB)
016 SOLUTION Exploring Relationships.mp4 (26.47 MB)
017 Feature Engineering Overview.mp4 (12.29 MB)
018 Numeric Feature Engineering.mp4 (9.8 MB)
019 Dummy Variables.mp4 (10.72 MB)
020 Binning Categories.mp4 (8.1 MB)
021 DEMO Feature Engineering.mp4 (24.41 MB)
022 Data Splitting.mp4 (31.68 MB)
023 Preparing Data for Modeling.mp4 (4.29 MB)
024 ASSIGNMENT Preparing the Data for Modeling.mp4 (4.13 MB)
025 SOLUTION Prepare the Data for Modeling.mp4 (49.24 MB)
026 Key Takeaways.mp4 (2.9 MB)
001 K-Nearest Neighbors.mp4 (30.63 MB)
002 The KNN Workflow.mp4 (20.61 MB)
003 KNN in Python.mp4 (9.37 MB)
004 Model Accuracy.mp4 (16.14 MB)
005 Confusion Matrix.mp4 (20.09 MB)
006 DEMO Confusion Matrix.mp4 (23.27 MB)
007 ASSIGNMENT Fitting a Simple KNN Model.mp4 (10.83 MB)
008 SOLUTION Fitting a Simple KNN Model.mp4 (24.49 MB)
009 Hyperparameter Tuning.mp4 (7.31 MB)
010 Overfitting & Validation.mp4 (19.28 MB)
011 DEMO Hyperparameter Tuning.mp4 (15.7 MB)
012 Hard vs Soft Classification.mp4 (14.1 MB)
013 DEMO Probability vs Event Rate.mp4 (33.56 MB)
014 ASSIGNMENT Tuning a KNN Model.mp4 (3.86 MB)
015 SOLUTION Tuning a KNN Model.mp4 (18.39 MB)
016 Pros & Cons of KNN.mp4 (10.83 MB)
017 Key Takeaways.mp4 (2.42 MB)
001 Logistic Regression.mp4 (15.26 MB)
002 Logistic vs Linear Regression.mp4 (11.99 MB)
003 The Logistic Function.mp4 (6.63 MB)
004 Likelihood.mp4 (10.63 MB)
005 Multiple Logistic Regression.mp4 (6.47 MB)
006 The Logistic Regression Workflow.mp4 (1.86 MB)
007 Logistic Regression in Python.mp4 (15.19 MB)
008 Interpreting Coefficients.mp4 (8.99 MB)
009 ASSIGNMENT Logistic Regression.mp4 (7.49 MB)
010 SOLUTION Logistic Regression.mp4 (9.44 MB)
011 Feature Engineering & Selection.mp4 (10.91 MB)
012 Regularization.mp4 (11.31 MB)
013 Tuning a Regularized Model.mp4 (8.89 MB)
014 DEMO Regularized Logistic Regression.mp4 (22.87 MB)
015 ASSIGNMENT Regularized Logistic Regression.mp4 (5.14 MB)
016 SOLUTION Regularized Logistic Regression.mp4 (43.56 MB)
017 Multi-class Logistic Regression.mp4 (15.47 MB)
018 ASSIGNMENT Multi-class Logistic Regression.mp4 (4.15 MB)
019 SOLUTION Multi-class Logistic Regression.mp4 (9.24 MB)
020 Pros & Cons of Logistic Regression.mp4 (6.57 MB)
021 Key Takeaways.mp4 (3.22 MB)
001 Classification Metrics.mp4 (5.52 MB)
002 Accuracy, Precision & Recall.mp4 (13.27 MB)
003 DEMO Accuracy, Precision & Recall.mp4 (18.95 MB)
004 PRO TIP F1 Score.mp4 (9.66 MB)
005 ASSIGNMENT Model Metrics.mp4 (2.96 MB)
006 SOLUTION Model Metrics.mp4 (10.92 MB)
007 Soft Classification.mp4 (14.21 MB)
008 DEMO Leveraging Soft Classification.mp4 (8.83 MB)
009 PRO TIP Precision-Recall & F1 Curves.mp4 (8.8 MB)
010 DEMO Plotting Precision-Recall & F1 Curves.mp4 (23.48 MB)
011 The ROC Curve & AUC.mp4 (7.84 MB)
012 DEMO The ROC Curve & AUC.mp4 (10.12 MB)
013 Classification Metrics Recap.mp4 (8.3 MB)
014 ASSIGNMENT Threshold Shifting.mp4 (4.08 MB)
015 SOLUTION Threshold Shifting.mp4 (16.64 MB)
016 Multi-class Metrics.mp4 (12.62 MB)
017 Multi-class Metrics in Python.mp4 (3.64 MB)
018 ASSIGNMENT Multi-class Metrics.mp4 (2.89 MB)
019 SOLUTION Multi-class Metrics.mp4 (9.58 MB)
020 Key Takeaways.mp4 (3.71 MB)
001 Imbalanced Data.mp4 (9.49 MB)
002 Managing Imbalanced Data.mp4 (21.25 MB)
003 Threshold Shifting.mp4 (5.3 MB)
004 Sampling Strategies.mp4 (4.33 MB)
005 Oversampling.mp4 (3.34 MB)
006 Oversampling in Python.mp4 (6.47 MB)
007 DEMO Oversampling.mp4 (11.59 MB)
008 SMOTE.mp4 (2.52 MB)
009 SMOTE in Python.mp4 (6.12 MB)
010 Undersampling.mp4 (4.69 MB)
011 Undersampling in Python.mp4 (15.47 MB)
012 ASSIGNMENT Sampling Methods.mp4 (12.58 MB)
013 SOLUTION Sampling Methods.mp4 (18.16 MB)
014 Changing Class Weights.mp4 (6.9 MB)
015 DEMO Changing Class Weights.mp4 (9.17 MB)
016 ASSIGNMENT Changing Class Weights.mp4 (2.27 MB)
017 SOLUTION Changing Class Weights.mp4 (10.48 MB)
018 Imbalanced Data Recap.mp4 (3.45 MB)
019 Key Takeaways.mp4 (2.2 MB)
001 Project Brief.mp4 (14.23 MB)
002 Solution Walkthrough.mp4 (53.77 MB)
001 Decision Trees.mp4 (7.51 MB)
002 Entropy.mp4 (11.52 MB)
003 Decision Tree Predictions.mp4 (9.37 MB)
004 Decision Trees in Python.mp4 (6.75 MB)
005 DEMO Decision Trees.mp4 (32.33 MB)
006 Feature Importance.mp4 (15.02 MB)
007 ASSIGNMENT Decision Trees.mp4 (3.37 MB)
008 SOLUTION Decision Trees.mp4 (29.9 MB)
009 Hyperparameter Tuning for Decision Trees.mp4 (26.21 MB)
010 DEMO Hyperparameter Tuning.mp4 (14.94 MB)
011 ASSIGNMENT Tuned Decision Tree.mp4 (2.04 MB)
012 SOLUTION Tuned Decision Tree.mp4 (14.71 MB)
013 Pros & Cons of Decision Trees.mp4 (6.57 MB)
014 Key Takeaways.mp4 (2.42 MB)
001 Ensemble Models.mp4 (8.74 MB)
002 Simple Ensemble Models.mp4 (5.2 MB)
003 DEMO Simple Ensemble Models.mp4 (13.45 MB)
004 ASSIGNMENT Simple Ensemble Models.mp4 (4.45 MB)
005 SOLUTION Simple Ensemble Models.mp4 (11.16 MB)
006 Random Forests.mp4 (6.3 MB)
007 Fitting Random Forests in Python.mp4 (11.16 MB)
008 Hyperparameter Tuning for Random Forests.mp4 (13.54 MB)
009 PRO TIP Random Search.mp4 (26.55 MB)
010 Pros & Cons of Random Forests.mp4 (4.14 MB)
011 ASSIGNMENT Random Forests.mp4 (2.34 MB)
012 SOLUTION Random Forests.mp4 (43.62 MB)
013 Gradient Boosting.mp4 (9.61 MB)
014 Gradient Boosting in Python.mp4 (6.21 MB)
015 Hyperparameter Tuning for Gradient Boosting.mp4 (11.61 MB)
016 DEMO Hyperparameter Tuning for Gradient Boosting.mp4 (22.91 MB)
017 Pros & Cons of Gradient Boosting.mp4 (4.21 MB)
018 ASSIGNMENT Gradient Boosting.mp4 (2.27 MB)
019 SOLUTION Gradient Boosting.mp4 (14.94 MB)
020 PRO TIP SHAP Values.mp4 (16.56 MB)
021 DEMO SHAP Values.mp4 (14.73 MB)
022 Key Takeaways.mp4 (2.44 MB)
001 Recap Classification Models & Workflow.mp4 (7.27 MB)
002 Pros & Cons of Classification Models.mp4 (8.38 MB)
003 DEMO Production Pipeline & Deployment.mp4 (55.65 MB)
004 Looking Ahead Unsupervised Learning.mp4 (5.52 MB)
001 Project Brief.mp4 (14.06 MB)
002 Solution Walkthrough.mp4 (38.65 MB)]
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