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001 Bonus Lecture - Other Courses.html
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001 Classification Problems - Introduction.en.srt
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001 Classification Problems - Introduction.mp4
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10.1 MB
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001 Classification Trees - Problem Evaluation and Fitting a Logistic Regression.en.srt
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001 Classification Trees - Problem Evaluation and Fitting a Logistic Regression.mp4
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69.3 MB
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001 Data Science Project - Taxi Trip Duration Project - Introduction.en.srt
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001 Data Science Project - Taxi Trip Duration Project - Introduction.mp4
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001 Installing Libraries.en.srt
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001 Installing Libraries.mp4
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001 Installing R.en.srt
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001 Installing R.mp4
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001 Intro to Dplyr and Tibble Data Structure.en.srt
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001 Intro to Dplyr and Tibble Data Structure.mp4
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001 Linear Regression - Introduction.en.srt
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001 Linear Regression - Introduction.mp4
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12.8 MB
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001 Model Evaluation and Selection - Introduction.en.srt
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001 Model Evaluation and Selection - Introduction.mp4
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7.8 MB
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001 Welcome to the Course!.en.srt
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001 Welcome to the Course!.mp4
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128.5 MB
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001 Random Forest Intuition and Subsetting Data.en.srt
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001 Random Forest Intuition and Subsetting Data.mp4
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49.3 MB
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002 Classification Problems Intuition - Why Linear Regression is unfit.en.srt
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002 Classification Problems Intuition - Why Linear Regression is unfit.mp4
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81.8 MB
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002 Classification Trees - First Split and Gini Impurity Concept.en.srt
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002 Classification Trees - First Split and Gini Impurity Concept.mp4
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112.5 MB
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002 Course Materials.html
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002 Detailed Feedback.html
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002 Example of a High Bias Model.en.srt
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002 Example of a High Bias Model.mp4
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002 Exploratory Data Analysis - Loading Taxi Trip and Analyzing Outliers.en.srt
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12 KB
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002 Exploratory Data Analysis - Loading Taxi Trip and Analyzing Outliers.mp4
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002 Filter and Pipe Format.en.srt
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002 Filter and Pipe Format.mp4
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002 Fitting Different Decision Trees.en.srt
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002 Fitting Different Decision Trees.mp4
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002 Installing R Studio.en.srt
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002 Installing R Studio.mp4
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90 MB
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002 Loading Libraries.en.srt
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002 Loading Libraries.mp4
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002 Loading the Data into R.en.srt
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002 Loading the Data into R.mp4
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33 MB
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003 Building a Random Forest from Scratch with Three Estimators.en.srt
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003 Building a Random Forest from Scratch with Three Estimators.mp4
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73.8 MB
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003 Calculating Sigmoid Function and Fitting a Logistic Regression.en.srt
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10 KB
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003 Calculating Sigmoid Function and Fitting a Logistic Regression.mp4
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56.3 MB
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003 Classification Trees - Finding the Best Split with Minimum Gini Impurity.mp4
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82.8 MB
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003 Example of a High Variance Model.en.srt
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003 Classification Trees - Finding the Best Split with Minimum Gini Impurity.en.srt
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003 Example of a High Variance Model.mp4
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003 Exploratory Data Analysis - Removing Outliers.en.srt
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003 Exploratory Data Analysis - Removing Outliers.mp4
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003 Final Notes.en.srt
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003 Final Notes.mp4
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003 Glimpse and Lists as Columns.en.srt
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003 Glimpse and Lists as Columns.mp4
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003 Let's start!.en.srt
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003 Let's start!.mp4
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003 Plotting Feature (Age) and Target (Income) Variables.en.srt
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003 Plotting Feature (Age) and Target (Income) Variables.mp4
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004 Classification Trees - Fitting a Decision Tree using RPart.en.srt
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004 Classification Trees - Fitting a Decision Tree using RPart.mp4
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004 Evaluating the Model on Unseen Data.en.srt
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004 Evaluating the Model on Unseen Data.mp4
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134.3 MB
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004 Feature Engineering - Time Based Features.en.srt
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004 Feature Engineering - Time Based Features.mp4
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89.2 MB
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004 Fitting a Random Line.en.srt
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004 Fitting a Random Line.mp4
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39.6 MB
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004 Function Encapsulation and Multiple Arguments.en.srt
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004 Function Encapsulation and Multiple Arguments.mp4
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27.7 MB
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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
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35.6 MB
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004 Summary of Logistic Regression and Accuracy.en.srt
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11 KB
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004 Summary of Logistic Regression and Accuracy.mp4
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69.3 MB
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005 Adjusting the Weight of our Linear Model.en.srt
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005 Adjusting the Weight of our Linear Model.mp4
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005 Arrange and Mutate.en.srt
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10 KB
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005 Arrange and Mutate.mp4
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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
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005 Feature Engineering - Visualizing Trip Duration per Feature.en.srt
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005 Feature Engineering - Visualizing Trip Duration per Feature.mp4
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62.5 MB
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005 Log-Loss Function Intuition.en.srt
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005 Log-Loss Function Intuition.mp4
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93.9 MB
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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
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36.2 MB
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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
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89.1 MB
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006 Gradient Descent Intuition - Classification.en.srt
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006 Gradient Descent Intuition - Classification.mp4
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74.5 MB
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006 Performance across Training and Test Data.en.srt
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006 Performance across Training and Test Data.mp4
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127.7 MB
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006 Select and Distinct.en.srt
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006 Select and Distinct.mp4
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37 MB
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006 Training our First Linear Model.en.srt
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006 Training our First Linear Model.mp4
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007 Feature Engineering - Visualizing Correlation and Adding Features to our table.en.srt
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007 Feature Engineering - Visualizing Correlation and Adding Features to our table.mp4
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111.3 MB
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007 Linear Regression Evaluation.mp4
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108.6 MB
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007 Regression Metrics - Plotting the Residuals.en.srt
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007 Regression Metrics - Plotting the Residuals.mp4
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104.4 MB
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007 Linear Regression Evaluation.en.srt
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007 Regression Trees - Intuition.en.srt
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007 Regression Trees - Intuition.mp4
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007 Sample_N and Sample_Frac.en.srt
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007 Sample_N and Sample_Frac.mp4
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007 Visualizing Log-Loss in 3 Dimensions.en.srt
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007 Visualizing Log-Loss in 3 Dimensions.mp4
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008 Feature Engineering - Creating Weekday feature and Building Data Pipeline.en.srt
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008 Feature Engineering - Creating Weekday feature and Building Data Pipeline.mp4
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008 Linear Regression Closed Form Solution.en.srt
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008 Linear Regression Closed Form Solution.mp4
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82 MB
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008 Regression Metrics - MSE, MAE and RMSE.en.srt
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008 Regression Metrics - MSE, MAE and RMSE.mp4
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008 Regression Trees - Calculating Residual Sum of Squares.en.srt
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008 Regression Trees - Calculating Residual Sum of Squares.mp4
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008 Summarize and Group By.en.srt
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008 Summarize and Group By.mp4
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009 Gradient Descent Intuition - Part 1.en.srt
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009 Gradient Descent Intuition - Part 1.mp4
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009 Joining Dataframes.en.srt
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009 Joining Dataframes.mp4
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009 Modelling - Preparing Data for Modelling.en.srt
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009 Modelling - Preparing Data for Modelling.mp4
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009 Regression Metrics - R-Square Breakdown and MAPE.en.srt
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009 Regression Metrics - R-Square Breakdown and MAPE.mp4
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009 Regression Trees - Finding the Best Split with Residual Sum of Squares.en.srt
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009 Regression Trees - Finding the Best Split with Residual Sum of Squares.mp4
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55 MB
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010 Small Typo.html
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010 Classification Metrics - Fitting Logistic Regression and Confusion Matrix Intro.en.srt
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010 Classification Metrics - Fitting Logistic Regression and Confusion Matrix Intro.mp4
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010 Gradient Descent Intuition - Part 2.en.srt
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010 Gradient Descent Intuition - Part 2.mp4
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010 Modelling - Fitting Linear Regression.en.srt
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010 Modelling - Fitting Linear Regression.mp4
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010 Regression Trees - Fitting the Algorithm.en.srt
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010 Regression Trees - Fitting the Algorithm.mp4
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011 Classification Metrics - TP, FP, TN, FN.en.srt
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011 Classification Metrics - TP, FP, TN, FN.mp4
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011 Modelling - Training a Random Forest.en.srt
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011 Modelling - Training a Random Forest.mp4
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112.6 MB
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011 Regression Trees - Comparing between Tree and Linear Model.en.srt
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011 Regression Trees - Comparing between Tree and Linear Model.mp4
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119.7 MB
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011 Visualizing Gradient Descent.en.srt
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011 Visualizing Gradient Descent.mp4
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012 Classification Metrics - Precision, Recall and F-Score.en.srt
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012 Classification Metrics - Precision, Recall and F-Score.mp4
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012 Modelling - Caret Implementation and API.en.srt
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012 Modelling - Caret Implementation and API.mp4
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012 Multivariate Linear Regression.en.srt
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012 Multivariate Linear Regression.mp4
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013 Classification Metrics - Building ROC Curve.en.srt
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013 Classification Metrics - Building ROC Curve.mp4
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013 Modelling - Building Custom Experiments _ Hyperparameter Tuning.en.srt
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013 Modelling - Building Custom Experiments _ Hyperparameter Tuning.mp4
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014 Classification Metrics - ROCR Package and Area Under the Curve.en.srt
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014 Classification Metrics - ROCR Package and Area Under the Curve.mp4
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014 Modelling - Evaluating Best Model.mp4
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014 Modelling - Evaluating Best Model.en.srt
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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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TutsNode.com.txt
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102.4 B
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[TGx]Downloaded from torrentgalaxy.to .txt
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external-assets-links.txt
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58
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323.4 KB
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59
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907.9 KB
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60
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438.6 KB
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61
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188.9 KB
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62
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488.3 KB
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63
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41.2 KB
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863.2 KB
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444.6 KB
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66
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668.1 KB
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69
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580.1 KB
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70
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173.2 KB
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115.2 KB
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263.6 KB
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74
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75
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967.2 KB
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76
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201.5 KB
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77
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249.2 KB
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78
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912.3 KB
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79
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