| 0 | 102.4 B | ||
| 001 Bonus Lecture - Other Courses.html | 1.7 KB | ||
| 001 Classification Problems - Introduction.en.srt | 2.7 KB | ||
| 001 Classification Problems - Introduction.mp4 | 10.1 MB | ||
| 001 Classification Trees - Problem Evaluation and Fitting a Logistic Regression.en.srt | 12.5 KB | ||
| 001 Classification Trees - Problem Evaluation and Fitting a Logistic Regression.mp4 | 69.3 MB | ||
| 001 Data Science Project - Taxi Trip Duration Project - Introduction.en.srt | 5.6 KB | ||
| 001 Data Science Project - Taxi Trip Duration Project - Introduction.mp4 | 21.1 MB | ||
| 001 Installing Libraries.en.srt | 14 KB | ||
| 001 Installing Libraries.mp4 | 140.7 MB | ||
| 001 Installing R.en.srt | 8.9 KB | ||
| 001 Installing R.mp4 | 74.2 MB | ||
| 001 Intro to Dplyr and Tibble Data Structure.en.srt | 7.8 KB | ||
| 001 Intro to Dplyr and Tibble Data Structure.mp4 | 38.8 MB | ||
| 001 Linear Regression - Introduction.en.srt | 1.8 KB | ||
| 001 Linear Regression - Introduction.mp4 | 12.8 MB | ||
| 001 Model Evaluation and Selection - Introduction.en.srt | 3.1 KB | ||
| 001 Model Evaluation and Selection - Introduction.mp4 | 7.8 MB | ||
| 001 Welcome to the Course!.en.srt | 17.6 KB | ||
| 001 Welcome to the Course!.mp4 | 128.5 MB | ||
| 1 | 614.4 B | ||
| 001 Random Forest Intuition and Subsetting Data.en.srt | 10.4 KB | ||
| 001 Random Forest Intuition and Subsetting Data.mp4 | 49.3 MB | ||
| 002 Classification Problems Intuition - Why Linear Regression is unfit.en.srt | 15.6 KB | ||
| 2 | 630.7 KB | ||
| 002 Classification Problems Intuition - Why Linear Regression is unfit.mp4 | 81.8 MB | ||
| 002 Classification Trees - First Split and Gini Impurity Concept.en.srt | 18.2 KB | ||
| 002 Classification Trees - First Split and Gini Impurity Concept.mp4 | 112.5 MB | ||
| 002 Course Materials.html | 1.3 KB | ||
| 002 Detailed Feedback.html | 1.2 KB | ||
| 002 Example of a High Bias Model.en.srt | 15.2 KB | ||
| 002 Example of a High Bias Model.mp4 | 88.8 MB | ||
| 002 Exploratory Data Analysis - Loading Taxi Trip and Analyzing Outliers.en.srt | 12 KB | ||
| 002 Exploratory Data Analysis - Loading Taxi Trip and Analyzing Outliers.mp4 | 68.8 MB | ||
| 002 Filter and Pipe Format.en.srt | 9 KB | ||
| 002 Filter and Pipe Format.mp4 | 51.6 MB | ||
| 002 Fitting Different Decision Trees.en.srt | 12.8 KB | ||
| 002 Fitting Different Decision Trees.mp4 | 85.9 MB | ||
| 002 Installing R Studio.en.srt | 10.8 KB | ||
| 002 Installing R Studio.mp4 | 90 MB | ||
| 002 Loading Libraries.en.srt | 2.8 KB | ||
| 002 Loading Libraries.mp4 | 27.1 MB | ||
| 002 Loading the Data into R.en.srt | 5.7 KB | ||
| 002 Loading the Data into R.mp4 | 33 MB | ||
| 003 Building a Random Forest from Scratch with Three Estimators.en.srt | 10.9 KB | ||
| 003 Building a Random Forest from Scratch with Three Estimators.mp4 | 73.8 MB | ||
| 003 Calculating Sigmoid Function and Fitting a Logistic Regression.en.srt | 10 KB | ||
| 003 Calculating Sigmoid Function and Fitting a Logistic Regression.mp4 | 56.3 MB | ||
| 003 Classification Trees - Finding the Best Split with Minimum Gini Impurity.mp4 | 82.8 MB | ||
| 003 Example of a High Variance Model.en.srt | 18.9 KB | ||
| 3 | 832.3 KB | ||
| 003 Classification Trees - Finding the Best Split with Minimum Gini Impurity.en.srt | 11.6 KB | ||
| 003 Example of a High Variance Model.mp4 | 132.2 MB | ||
| 003 Exploratory Data Analysis - Removing Outliers.en.srt | 15.5 KB | ||
| 003 Exploratory Data Analysis - Removing Outliers.mp4 | 106.4 MB | ||
| 003 Final Notes.en.srt | 1.9 KB | ||
| 003 Final Notes.mp4 | 13.8 MB | ||
| 003 Glimpse and Lists as Columns.en.srt | 4.6 KB | ||
| 003 Glimpse and Lists as Columns.mp4 | 33 MB | ||
| 003 Let's start!.en.srt | 1 KB | ||
| 003 Let's start!.mp4 | 6.9 MB | ||
| 003 Plotting Feature (Age) and Target (Income) Variables.en.srt | 5.6 KB | ||
| 003 Plotting Feature (Age) and Target (Income) Variables.mp4 | 34.3 MB | ||
| 004 Classification Trees - Fitting a Decision Tree using RPart.en.srt | 7.5 KB | ||
| 004 Classification Trees - Fitting a Decision Tree using RPart.mp4 | 43.4 MB | ||
| 004 Evaluating the Model on Unseen Data.en.srt | 19.6 KB | ||
| 004 Evaluating the Model on Unseen Data.mp4 | 134.3 MB | ||
| 004 Feature Engineering - Time Based Features.en.srt | 15.7 KB | ||
| 004 Feature Engineering - Time Based Features.mp4 | 89.2 MB | ||
| 004 Fitting a Random Line.en.srt | 6.7 KB | ||
| 004 Fitting a Random Line.mp4 | 39.6 MB | ||
| 4 | 251.4 KB | ||
| 004 Function Encapsulation and Multiple Arguments.en.srt | 4.4 KB | ||
| 004 Function Encapsulation and Multiple Arguments.mp4 | 27.7 MB | ||
| 004 Measuring the Accuracy of Each Trees and of the Ensemble Average.en.srt | 4.7 KB | ||
| 004 Measuring the Accuracy of Each Trees and of the Ensemble Average.mp4 | 35.6 MB | ||
| 004 Summary of Logistic Regression and Accuracy.en.srt | 11 KB | ||
| 004 Summary of Logistic Regression and Accuracy.mp4 | 69.3 MB | ||
| 005 Adjusting the Weight of our Linear Model.en.srt | 4.9 KB | ||
| 005 Adjusting the Weight of our Linear Model.mp4 | 29.8 MB | ||
| 005 Arrange and Mutate.en.srt | 10 KB | ||
| 005 Arrange and Mutate.mp4 | 74.8 MB | ||
| 005 Classification Trees - Adding more Thresholds and Visualizing Classification.en.srt | 8 KB | ||
| 005 Classification Trees - Adding more Thresholds and Visualizing Classification.mp4 | 45.4 MB | ||
| 005 Feature Engineering - Visualizing Trip Duration per Feature.en.srt | 8.7 KB | ||
| 005 Feature Engineering - Visualizing Trip Duration per Feature.mp4 | 62.5 MB | ||
| 005 Log-Loss Function Intuition.en.srt | 19.4 KB | ||
| 005 Log-Loss Function Intuition.mp4 | 93.9 MB | ||
| 005 Random Forest - R Package Implementation.en.srt | 8.4 KB | ||
| 005 Random Forest - R Package Implementation.mp4 | 48.2 MB | ||
| 005 Randomized Train and Test Split.en.srt | 16.8 KB | ||
| 5 | 527.1 KB | ||
| 005 Randomized Train and Test Split.mp4 | 73.2 MB | ||
| 006 Classification Trees - Tweaking Hyperparameters and Checking Accuracy.en.srt | 6.1 KB | ||
| 006 Classification Trees - Tweaking Hyperparameters and Checking Accuracy.mp4 | 36.2 MB | ||
| 006 Feature Engineering - Building Location Based Features (Manhattan and Euclidean).en.srt | 12.7 KB | ||
| 006 Feature Engineering - Building Location Based Features (Manhattan and Euclidean).mp4 | 89.1 MB | ||
| 006 Gradient Descent Intuition - Classification.en.srt | 12.5 KB | ||
| 006 Gradient Descent Intuition - Classification.mp4 | 74.5 MB | ||
| 006 Performance across Training and Test Data.en.srt | 20.8 KB | ||
| 006 Performance across Training and Test Data.mp4 | 127.7 MB | ||
| 006 Select and Distinct.en.srt | 6.3 KB | ||
| 006 Select and Distinct.mp4 | 37 MB | ||
| 006 Training our First Linear Model.en.srt | 6.8 KB | ||
| 6 | 283 KB | ||
| 006 Training our First Linear Model.mp4 | 40.1 MB | ||
| 007 Feature Engineering - Visualizing Correlation and Adding Features to our table.en.srt | 15.5 KB | ||
| 007 Feature Engineering - Visualizing Correlation and Adding Features to our table.mp4 | 111.3 MB | ||
| 007 Linear Regression Evaluation.mp4 | 108.6 MB | ||
| 007 Regression Metrics - Plotting the Residuals.en.srt | 17.9 KB | ||
| 007 Regression Metrics - Plotting the Residuals.mp4 | 104.4 MB | ||
| 7 | 272.3 KB | ||
| 007 Linear Regression Evaluation.en.srt | 18 KB | ||
| 007 Regression Trees - Intuition.en.srt | 15.5 KB | ||
| 007 Regression Trees - Intuition.mp4 | 84.8 MB | ||
| 007 Sample_N and Sample_Frac.en.srt | 4.2 KB | ||
| 007 Sample_N and Sample_Frac.mp4 | 30.4 MB | ||
| 007 Visualizing Log-Loss in 3 Dimensions.en.srt | 13.3 KB | ||
| 007 Visualizing Log-Loss in 3 Dimensions.mp4 | 79.7 MB | ||
| 008 Feature Engineering - Creating Weekday feature and Building Data Pipeline.en.srt | 16.7 KB | ||
| 008 Feature Engineering - Creating Weekday feature and Building Data Pipeline.mp4 | 108.2 MB | ||
| 008 Linear Regression Closed Form Solution.en.srt | 17.4 KB | ||
| 008 Linear Regression Closed Form Solution.mp4 | 82 MB | ||
| 008 Regression Metrics - MSE, MAE and RMSE.en.srt | 10.1 KB | ||
| 008 Regression Metrics - MSE, MAE and RMSE.mp4 | 61.3 MB | ||
| 008 Regression Trees - Calculating Residual Sum of Squares.en.srt | 6.3 KB | ||
| 8 | 393 KB | ||
| 008 Regression Trees - Calculating Residual Sum of Squares.mp4 | 38.5 MB | ||
| 008 Summarize and Group By.en.srt | 4.4 KB | ||
| 008 Summarize and Group By.mp4 | 29.8 MB | ||
| 009 Gradient Descent Intuition - Part 1.en.srt | 20.7 KB | ||
| 009 Gradient Descent Intuition - Part 1.mp4 | 130.8 MB | ||
| 009 Joining Dataframes.en.srt | 8.8 KB | ||
| 009 Joining Dataframes.mp4 | 61.7 MB | ||
| 009 Modelling - Preparing Data for Modelling.en.srt | 14.2 KB | ||
| 009 Modelling - Preparing Data for Modelling.mp4 | 89.2 MB | ||
| 009 Regression Metrics - R-Square Breakdown and MAPE.en.srt | 10.6 KB | ||
| 009 Regression Metrics - R-Square Breakdown and MAPE.mp4 | 61.9 MB | ||
| 9 | 508.5 KB | ||
| 009 Regression Trees - Finding the Best Split with Residual Sum of Squares.en.srt | 7.9 KB | ||
| 009 Regression Trees - Finding the Best Split with Residual Sum of Squares.mp4 | 55 MB | ||
| 010 Small Typo.html | 1.1 KB | ||
| 10 | 764.9 KB | ||
| 010 Classification Metrics - Fitting Logistic Regression and Confusion Matrix Intro.en.srt | 16.6 KB | ||
| 010 Classification Metrics - Fitting Logistic Regression and Confusion Matrix Intro.mp4 | 90.3 MB | ||
| 010 Gradient Descent Intuition - Part 2.en.srt | 12.7 KB | ||
| 010 Gradient Descent Intuition - Part 2.mp4 | 84.2 MB | ||
| 010 Modelling - Fitting Linear Regression.en.srt | 10.3 KB | ||
| 010 Modelling - Fitting Linear Regression.mp4 | 69.4 MB | ||
| 010 Regression Trees - Fitting the Algorithm.en.srt | 8.5 KB | ||
| 010 Regression Trees - Fitting the Algorithm.mp4 | 52.1 MB | ||
| 011 Classification Metrics - TP, FP, TN, FN.en.srt | 4.8 KB | ||
| 011 Classification Metrics - TP, FP, TN, FN.mp4 | 27.9 MB | ||
| 011 Modelling - Training a Random Forest.en.srt | 18.4 KB | ||
| 011 Modelling - Training a Random Forest.mp4 | 112.6 MB | ||
| 011 Regression Trees - Comparing between Tree and Linear Model.en.srt | 17.6 KB | ||
| 011 Regression Trees - Comparing between Tree and Linear Model.mp4 | 119.7 MB | ||
| 011 Visualizing Gradient Descent.en.srt | 12.6 KB | ||
| 11 | 524.4 KB | ||
| 011 Visualizing Gradient Descent.mp4 | 70.9 MB | ||
| 012 Classification Metrics - Precision, Recall and F-Score.en.srt | 8.2 KB | ||
| 12 | 391.3 KB | ||
| 012 Classification Metrics - Precision, Recall and F-Score.mp4 | 40.7 MB | ||
| 012 Modelling - Caret Implementation and API.en.srt | 9.2 KB | ||
| 012 Modelling - Caret Implementation and API.mp4 | 60.1 MB | ||
| 012 Multivariate Linear Regression.en.srt | 19.4 KB | ||
| 012 Multivariate Linear Regression.mp4 | 109.5 MB | ||
| 013 Classification Metrics - Building ROC Curve.en.srt | 14.3 KB | ||
| 013 Classification Metrics - Building ROC Curve.mp4 | 83 MB | ||
| 013 Modelling - Building Custom Experiments _ Hyperparameter Tuning.en.srt | 8 KB | ||
| 013 Modelling - Building Custom Experiments _ Hyperparameter Tuning.mp4 | 56.9 MB | ||
| 13 | 778.1 KB | ||
| 014 Classification Metrics - ROCR Package and Area Under the Curve.en.srt | 9.1 KB | ||
| 014 Classification Metrics - ROCR Package and Area Under the Curve.mp4 | 45.7 MB | ||
| 014 Modelling - Evaluating Best Model.mp4 | 49.2 MB | ||
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| 014 Modelling - Evaluating Best Model.en.srt | 6.7 KB | ||
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| 015 Evaluating - Preparing New Data for Scoring.en.srt | 23.7 KB | ||
| 015 Evaluating - Preparing New Data for Scoring.mp4 | 141.5 MB | ||
| 016 Evaluating - Scoring New Data and Submitting do Kaggle.en.srt | 9.8 KB | ||
| 016 Evaluating - Scoring New Data and Submitting do Kaggle.mp4 | 61.7 MB | ||
| TutsNode.com.txt | 102.4 B | ||
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Description
So, you’ve learned a bit of R Basics and are looking to understand how R can be used for Data Science? And are looking for a course that explains all the theory behind algorithms with coding?
R is on of the de facto languages for a lot of Data Science projects today – either for enterprise-level projects or research, R is a modern and flexible language with a smooth learning curve that enables most professionals to build predictive models in quick fashion.
This course was designed to be your next step into the R programming world! We will delve deeper into the concepts of Linear and Logistic Regression, understand how Tree Based models work, learn how to evaluate predictive models and more. This course contains lectures around the following groups:
Code along lectures where you will see how we can implement the stuff we will learn!
Test your knowledge with questions and practical exercises with different levels of difficulty!
This course was designed to be focused on the practical side of coding in R – other than studying the functions that let us build algorithms automatically we will investigate deeply how models are trained and how they get to the optimum solution to solve our data science projects.
At the end of the course you should be able to use R in a data science context – understanding the choices you have to make when it comes to algorithms and learn how to evaluate those choises. Along the way you will also learn how to manipulate data with Dplyr because most of the times, in a Data Science project, more than half of time is spent on data preparation!
Here are some examples of things you will be able to do after finishing the course:
Solving Regression problems using Linear Regression or Regression Trees.
Solving Classification problems using Logistic Regression or Classification Trees.
Learn how to evaluate algorithms using different metrics.
Understanding the concept of bias and variance.
Using Random Forests and understanding the reasoning behind them.
Manipulating data using Dplyr.
Build your own Kaggle Data Science project!
Join thousands of professionals and students in this R journey and discover the amazing power of this statistical open-source language.
This course will be constantly updated based on students feedback.
Who this course is for:
Entry-Level Data Scientists
R Coders
Statisticians
Business Analysts
Financial Modelers
Requirements
Computer with at least 4 GB of RAM
Knowing the Basics of R Programming (R Objects, Functions and Libraries)
Last Updated 8/2021
| torrent name | size | uploader | age | seed | leech |
|---|---|---|---|---|---|
| 803.9 MB | freecoursewb | 4 weeks | 15 | 15 | |
| 1.4 GB | freecoursewb | 9 months | 0 | 0 | |
| 1.5 GB | freecoursewb | 1 year | 0 | 0 | |
| 1.7 GB | freecoursewb | 1 year | 3 | 0 | |
| 3 GB | freecoursewb | 1 year | 0 | 0 |
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