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141б |
| 001 Bonus Lecture - Other Courses.html |
1.72Кб |
| 001 Classification Problems - Introduction.en.srt |
2.73Кб |
| 001 Classification Problems - Introduction.mp4 |
10.11Мб |
| 001 Classification Trees - Problem Evaluation and Fitting a Logistic Regression.en.srt |
12.54Кб |
| 001 Classification Trees - Problem Evaluation and Fitting a Logistic Regression.mp4 |
69.27Мб |
| 001 Data Science Project - Taxi Trip Duration Project - Introduction.en.srt |
5.64Кб |
| 001 Data Science Project - Taxi Trip Duration Project - Introduction.mp4 |
21.06Мб |
| 001 Installing Libraries.en.srt |
14.02Кб |
| 001 Installing Libraries.mp4 |
140.74Мб |
| 001 Installing R.en.srt |
8.89Кб |
| 001 Installing R.mp4 |
74.23Мб |
| 001 Intro to Dplyr and Tibble Data Structure.en.srt |
7.82Кб |
| 001 Intro to Dplyr and Tibble Data Structure.mp4 |
38.82Мб |
| 001 Linear Regression - Introduction.en.srt |
1.76Кб |
| 001 Linear Regression - Introduction.mp4 |
12.76Мб |
| 001 Model Evaluation and Selection - Introduction.en.srt |
3.13Кб |
| 001 Model Evaluation and Selection - Introduction.mp4 |
7.84Мб |
| 001 Random Forest Intuition and Subsetting Data.en.srt |
10.41Кб |
| 001 Random Forest Intuition and Subsetting Data.mp4 |
49.32Мб |
| 001 Welcome to the Course!.en.srt |
17.62Кб |
| 001 Welcome to the Course!.mp4 |
128.49Мб |
| 002 Classification Problems Intuition - Why Linear Regression is unfit.en.srt |
15.63Кб |
| 002 Classification Problems Intuition - Why Linear Regression is unfit.mp4 |
81.78Мб |
| 002 Classification Trees - First Split and Gini Impurity Concept.en.srt |
18.15Кб |
| 002 Classification Trees - First Split and Gini Impurity Concept.mp4 |
112.50Мб |
| 002 Course Materials.html |
1.32Кб |
| 002 Detailed Feedback.html |
1.19Кб |
| 002 Example of a High Bias Model.en.srt |
15.18Кб |
| 002 Example of a High Bias Model.mp4 |
88.85Мб |
| 002 Exploratory Data Analysis - Loading Taxi Trip and Analyzing Outliers.en.srt |
12.02Кб |
| 002 Exploratory Data Analysis - Loading Taxi Trip and Analyzing Outliers.mp4 |
68.78Мб |
| 002 Filter and Pipe Format.en.srt |
9.00Кб |
| 002 Filter and Pipe Format.mp4 |
51.64Мб |
| 002 Fitting Different Decision Trees.en.srt |
12.81Кб |
| 002 Fitting Different Decision Trees.mp4 |
85.88Мб |
| 002 Installing R Studio.en.srt |
10.84Кб |
| 002 Installing R Studio.mp4 |
90.03Мб |
| 002 Loading Libraries.en.srt |
2.77Кб |
| 002 Loading Libraries.mp4 |
27.06Мб |
| 002 Loading the Data into R.en.srt |
5.67Кб |
| 002 Loading the Data into R.mp4 |
33.02Мб |
| 003 Building a Random Forest from Scratch with Three Estimators.en.srt |
10.88Кб |
| 003 Building a Random Forest from Scratch with Three Estimators.mp4 |
73.81Мб |
| 003 Calculating Sigmoid Function and Fitting a Logistic Regression.en.srt |
10.00Кб |
| 003 Calculating Sigmoid Function and Fitting a Logistic Regression.mp4 |
56.35Мб |
| 003 Classification Trees - Finding the Best Split with Minimum Gini Impurity.en.srt |
11.64Кб |
| 003 Classification Trees - Finding the Best Split with Minimum Gini Impurity.mp4 |
82.77Мб |
| 003 Example of a High Variance Model.en.srt |
18.86Кб |
| 003 Example of a High Variance Model.mp4 |
132.19Мб |
| 003 Exploratory Data Analysis - Removing Outliers.en.srt |
15.46Кб |
| 003 Exploratory Data Analysis - Removing Outliers.mp4 |
106.40Мб |
| 003 Final Notes.en.srt |
1.86Кб |
| 003 Final Notes.mp4 |
13.80Мб |
| 003 Glimpse and Lists as Columns.en.srt |
4.64Кб |
| 003 Glimpse and Lists as Columns.mp4 |
32.98Мб |
| 003 Let's start!.en.srt |
995б |
| 003 Let's start!.mp4 |
6.89Мб |
| 003 Plotting Feature (Age) and Target (Income) Variables.en.srt |
5.64Кб |
| 003 Plotting Feature (Age) and Target (Income) Variables.mp4 |
34.35Мб |
| 004 Classification Trees - Fitting a Decision Tree using RPart.en.srt |
7.49Кб |
| 004 Classification Trees - Fitting a Decision Tree using RPart.mp4 |
43.42Мб |
| 004 Evaluating the Model on Unseen Data.en.srt |
19.55Кб |
| 004 Evaluating the Model on Unseen Data.mp4 |
134.27Мб |
| 004 Feature Engineering - Time Based Features.en.srt |
15.69Кб |
| 004 Feature Engineering - Time Based Features.mp4 |
89.19Мб |
| 004 Fitting a Random Line.en.srt |
6.72Кб |
| 004 Fitting a Random Line.mp4 |
39.57Мб |
| 004 Function Encapsulation and Multiple Arguments.en.srt |
4.42Кб |
| 004 Function Encapsulation and Multiple Arguments.mp4 |
27.74Мб |
| 004 Measuring the Accuracy of Each Trees and of the Ensemble Average.en.srt |
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| 004 Measuring the Accuracy of Each Trees and of the Ensemble Average.mp4 |
35.57Мб |
| 004 Summary of Logistic Regression and Accuracy.en.srt |
10.96Кб |
| 004 Summary of Logistic Regression and Accuracy.mp4 |
69.32Мб |
| 005 Adjusting the Weight of our Linear Model.en.srt |
4.85Кб |
| 005 Adjusting the Weight of our Linear Model.mp4 |
29.83Мб |
| 005 Arrange and Mutate.en.srt |
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| 005 Arrange and Mutate.mp4 |
74.83Мб |
| 005 Classification Trees - Adding more Thresholds and Visualizing Classification.en.srt |
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| 005 Classification Trees - Adding more Thresholds and Visualizing Classification.mp4 |
45.41Мб |
| 005 Feature Engineering - Visualizing Trip Duration per Feature.en.srt |
8.67Кб |
| 005 Feature Engineering - Visualizing Trip Duration per Feature.mp4 |
62.52Мб |
| 005 Log-Loss Function Intuition.en.srt |
19.41Кб |
| 005 Log-Loss Function Intuition.mp4 |
93.90Мб |
| 005 Random Forest - R Package Implementation.en.srt |
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| 005 Random Forest - R Package Implementation.mp4 |
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| 005 Randomized Train and Test Split.en.srt |
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| 005 Randomized Train and Test Split.mp4 |
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| 006 Classification Trees - Tweaking Hyperparameters and Checking Accuracy.en.srt |
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| 006 Classification Trees - Tweaking Hyperparameters and Checking Accuracy.mp4 |
36.16Мб |
| 006 Feature Engineering - Building Location Based Features (Manhattan and Euclidean).en.srt |
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| 006 Feature Engineering - Building Location Based Features (Manhattan and Euclidean).mp4 |
89.06Мб |
| 006 Gradient Descent Intuition - Classification.en.srt |
12.48Кб |
| 006 Gradient Descent Intuition - Classification.mp4 |
74.48Мб |
| 006 Performance across Training and Test Data.en.srt |
20.75Кб |
| 006 Performance across Training and Test Data.mp4 |
127.72Мб |
| 006 Select and Distinct.en.srt |
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| 006 Select and Distinct.mp4 |
36.96Мб |
| 006 Training our First Linear Model.en.srt |
6.84Кб |
| 006 Training our First Linear Model.mp4 |
40.11Мб |
| 007 Feature Engineering - Visualizing Correlation and Adding Features to our table.en.srt |
15.50Кб |
| 007 Feature Engineering - Visualizing Correlation and Adding Features to our table.mp4 |
111.25Мб |
| 007 Linear Regression Evaluation.en.srt |
18.01Кб |
| 007 Linear Regression Evaluation.mp4 |
108.62Мб |
| 007 Regression Metrics - Plotting the Residuals.en.srt |
17.91Кб |
| 007 Regression Metrics - Plotting the Residuals.mp4 |
104.40Мб |
| 007 Regression Trees - Intuition.en.srt |
15.47Кб |
| 007 Regression Trees - Intuition.mp4 |
84.81Мб |
| 007 Sample_N and Sample_Frac.en.srt |
4.23Кб |
| 007 Sample_N and Sample_Frac.mp4 |
30.43Мб |
| 007 Visualizing Log-Loss in 3 Dimensions.en.srt |
13.30Кб |
| 007 Visualizing Log-Loss in 3 Dimensions.mp4 |
79.69Мб |
| 008 Feature Engineering - Creating Weekday feature and Building Data Pipeline.en.srt |
16.74Кб |
| 008 Feature Engineering - Creating Weekday feature and Building Data Pipeline.mp4 |
108.24Мб |
| 008 Linear Regression Closed Form Solution.en.srt |
17.38Кб |
| 008 Linear Regression Closed Form Solution.mp4 |
82.00Мб |
| 008 Regression Metrics - MSE, MAE and RMSE.en.srt |
10.11Кб |
| 008 Regression Metrics - MSE, MAE and RMSE.mp4 |
61.29Мб |
| 008 Regression Trees - Calculating Residual Sum of Squares.en.srt |
6.28Кб |
| 008 Regression Trees - Calculating Residual Sum of Squares.mp4 |
38.52Мб |
| 008 Summarize and Group By.en.srt |
4.45Кб |
| 008 Summarize and Group By.mp4 |
29.82Мб |
| 009 Gradient Descent Intuition - Part 1.en.srt |
20.74Кб |
| 009 Gradient Descent Intuition - Part 1.mp4 |
130.75Мб |
| 009 Joining Dataframes.en.srt |
8.82Кб |
| 009 Joining Dataframes.mp4 |
61.68Мб |
| 009 Modelling - Preparing Data for Modelling.en.srt |
14.20Кб |
| 009 Modelling - Preparing Data for Modelling.mp4 |
89.19Мб |
| 009 Regression Metrics - R-Square Breakdown and MAPE.en.srt |
10.64Кб |
| 009 Regression Metrics - R-Square Breakdown and MAPE.mp4 |
61.94Мб |
| 009 Regression Trees - Finding the Best Split with Residual Sum of Squares.en.srt |
7.86Кб |
| 009 Regression Trees - Finding the Best Split with Residual Sum of Squares.mp4 |
54.97Мб |
| 010 Classification Metrics - Fitting Logistic Regression and Confusion Matrix Intro.en.srt |
16.64Кб |
| 010 Classification Metrics - Fitting Logistic Regression and Confusion Matrix Intro.mp4 |
90.31Мб |
| 010 Gradient Descent Intuition - Part 2.en.srt |
12.66Кб |
| 010 Gradient Descent Intuition - Part 2.mp4 |
84.22Мб |
| 010 Modelling - Fitting Linear Regression.en.srt |
10.31Кб |
| 010 Modelling - Fitting Linear Regression.mp4 |
69.39Мб |
| 010 Regression Trees - Fitting the Algorithm.en.srt |
8.53Кб |
| 010 Regression Trees - Fitting the Algorithm.mp4 |
52.05Мб |
| 010 Small Typo.html |
1.07Кб |
| 011 Classification Metrics - TP, FP, TN, FN.en.srt |
4.80Кб |
| 011 Classification Metrics - TP, FP, TN, FN.mp4 |
27.89Мб |
| 011 Modelling - Training a Random Forest.en.srt |
18.44Кб |
| 011 Modelling - Training a Random Forest.mp4 |
112.62Мб |
| 011 Regression Trees - Comparing between Tree and Linear Model.en.srt |
17.57Кб |
| 011 Regression Trees - Comparing between Tree and Linear Model.mp4 |
119.73Мб |
| 011 Visualizing Gradient Descent.en.srt |
12.58Кб |
| 011 Visualizing Gradient Descent.mp4 |
70.95Мб |
| 012 Classification Metrics - Precision, Recall and F-Score.en.srt |
8.20Кб |
| 012 Classification Metrics - Precision, Recall and F-Score.mp4 |
40.68Мб |
| 012 Modelling - Caret Implementation and API.en.srt |
9.23Кб |
| 012 Modelling - Caret Implementation and API.mp4 |
60.13Мб |
| 012 Multivariate Linear Regression.en.srt |
19.41Кб |
| 012 Multivariate Linear Regression.mp4 |
109.49Мб |
| 013 Classification Metrics - Building ROC Curve.en.srt |
14.30Кб |
| 013 Classification Metrics - Building ROC Curve.mp4 |
83.00Мб |
| 013 Modelling - Building Custom Experiments _ Hyperparameter Tuning.en.srt |
7.96Кб |
| 013 Modelling - Building Custom Experiments _ Hyperparameter Tuning.mp4 |
56.93Мб |
| 014 Classification Metrics - ROCR Package and Area Under the Curve.en.srt |
9.15Кб |
| 014 Classification Metrics - ROCR Package and Area Under the Curve.mp4 |
45.65Мб |
| 014 Modelling - Evaluating Best Model.en.srt |
6.75Кб |
| 014 Modelling - Evaluating Best Model.mp4 |
49.22Мб |
| 015 Evaluating - Preparing New Data for Scoring.en.srt |
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| 015 Evaluating - Preparing New Data for Scoring.mp4 |
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| 016 Evaluating - Scoring New Data and Submitting do Kaggle.en.srt |
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| 016 Evaluating - Scoring New Data and Submitting do Kaggle.mp4 |
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