Download R for Data Science: Your First Step as a Data Scientist torrent

R for Data Science: Your First Step as a Data Scientist

Torrent:	R for Data Science: Your First Step as a Data Scientist
Description:	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
Downloads:	174
Category:	Other /Tutorials
Size:	5.4 GB
Show Files »	R for Data Science Your First Step as a Data Scientist [TutsNode.com] - R for Data Science Your First Step as a Data Scientist 10 Data Science Project - Kaggle Taxi Trip Duration 015 Evaluating - Preparing New Data for Scoring.mp4 (141.5 MB) 015 Evaluating - Preparing New Data for Scoring.en.srt (23.7 KB) 011 Modelling - Training a Random Forest.en.srt (18.4 KB) 008 Feature Engineering - Creating Weekday feature and Building Data Pipeline.en.srt (16.7 KB) 004 Feature Engineering - Time Based Features.en.srt (15.7 KB) 007 Feature Engineering - Visualizing Correlation and Adding Features to our table.en.srt (15.5 KB) 003 Exploratory Data Analysis - Removing Outliers.en.srt (15.5 KB) 009 Modelling - Preparing Data for Modelling.en.srt (14.2 KB) 006 Feature Engineering - Building Location Based Features (Manhattan and Euclidean).en.srt (12.7 KB) 002 Exploratory Data Analysis - Loading Taxi Trip and Analyzing Outliers.en.srt (12.0 KB) 010 Modelling - Fitting Linear Regression.en.srt (10.3 KB) 016 Evaluating - Scoring New Data and Submitting do Kaggle.en.srt (9.8 KB) 012 Modelling - Caret Implementation and API.en.srt (9.2 KB) 005 Feature Engineering - Visualizing Trip Duration per Feature.en.srt (8.7 KB) 013 Modelling - Building Custom Experiments _ Hyperparameter Tuning.en.srt (8.0 KB) 014 Modelling - Evaluating Best Model.en.srt (6.7 KB) 001 Data Science Project - Taxi Trip Duration Project - Introduction.en.srt (5.6 KB) 011 Modelling - Training a Random Forest.mp4 (112.6 MB) 007 Feature Engineering - Visualizing Correlation and Adding Features to our table.mp4 (111.3 MB) 008 Feature Engineering - Creating Weekday feature and Building Data Pipeline.mp4 (108.2 MB) 003 Exploratory Data Analysis - Removing Outliers.mp4 (106.4 MB) 004 Feature Engineering - Time Based Features.mp4 (89.2 MB) 009 Modelling - Preparing Data for Modelling.mp4 (89.2 MB) 006 Feature Engineering - Building Location Based Features (Manhattan and Euclidean).mp4 (89.1 MB) 010 Modelling - Fitting Linear Regression.mp4 (69.4 MB) 002 Exploratory Data Analysis - Loading Taxi Trip and Analyzing Outliers.mp4 (68.8 MB) 005 Feature Engineering - Visualizing Trip Duration per Feature.mp4 (62.5 MB) 016 Evaluating - Scoring New Data and Submitting do Kaggle.mp4 (61.7 MB) 012 Modelling - Caret Implementation and API.mp4 (60.1 MB) 013 Modelling - Building Custom Experiments _ Hyperparameter Tuning.mp4 (56.9 MB) 014 Modelling - Evaluating Best Model.mp4 (49.2 MB) 001 Data Science Project - Taxi Trip Duration Project - Introduction.mp4 (21.1 MB) 07 Model Evaluation and Selection 006 Performance across Training and Test Data.en.srt (20.8 KB) 004 Evaluating the Model on Unseen Data.en.srt (19.6 KB) 003 Example of a High Variance Model.en.srt (18.9 KB) 007 Regression Metrics - Plotting the Residuals.en.srt (17.9 KB) 005 Randomized Train and Test Split.en.srt (16.8 KB) 010 Classification Metrics - Fitting Logistic Regression and Confusion Matrix Intro.en.srt (16.6 KB) 002 Example of a High Bias Model.en.srt (15.2 KB) 013 Classification Metrics - Building ROC Curve.en.srt (14.3 KB) 001 Model Evaluation and Selection - Introduction.en.srt (3.1 KB) 009 Regression Metrics - R-Square Breakdown and MAPE.en.srt (10.6 KB) 008 Regression Metrics - MSE, MAE and RMSE.en.srt (10.1 KB) 014 Classification Metrics - ROCR Package and Area Under the Curve.en.srt (9.1 KB) 012 Classification Metrics - Precision, Recall and F-Score.en.srt (8.2 KB) 004 Evaluating the Model on Unseen Data.mp4 (134.3 MB) 011 Classification Metrics - TP, FP, TN, FN.en.srt (4.8 KB) 003 Example of a High Variance Model.mp4 (132.2 MB) 006 Performance across Training and Test Data.mp4 (127.7 MB) 007 Regression Metrics - Plotting the Residuals.mp4 (104.4 MB) 010 Classification Metrics - Fitting Logistic Regression and Confusion Matrix Intro.mp4 (90.3 MB) 002 Example of a High Bias Model.mp4 (88.8 MB) 013 Classification Metrics - Building ROC Curve.mp4 (83.0 MB) 005 Randomized Train and Test Split.mp4 (73.2 MB) 009 Regression Metrics - R-Square Breakdown and MAPE.mp4 (61.9 MB) 008 Regression Metrics - MSE, MAE and RMSE.mp4 (61.3 MB) 014 Classification Metrics - ROCR Package and Area Under the Curve.mp4 (45.7 MB) 012 Classification Metrics - Precision, Recall and F-Score.mp4 (40.7 MB) 011 Classification Metrics - TP, FP, TN, FN.mp4 (27.9 MB) 001 Model Evaluation and Selection - Introduction.mp4 (7.8 MB) 01 Introduction 002 Course Materials.html (1.3 KB) external-assets-links.txt (0.2 KB) 001 Welcome to the Course!.en.srt (17.6 KB) 001 Welcome to the Course!.mp4 (128.5 MB) 05 Linear Regression 009 Gradient Descent Intuition - Part 1.en.srt (20.7 KB) 012 Multivariate Linear Regression.en.srt (19.4 KB) 007 Linear Regression Evaluation.en.srt (18.0 KB) 008 Linear Regression Closed Form Solution.en.srt (17.4 KB) 001 Linear Regression - Introduction.en.srt (1.8 KB) 010 Gradient Descent Intuition - Part 2.en.srt (12.7 KB) 011 Visualizing Gradient Descent.en.srt (12.6 KB) 006 Training our First Linear Model.en.srt (6.8 KB) 004 Fitting a Random Line.en.srt (6.7 KB) 002 Loading the Data into R.en.srt (5.7 KB) 003 Plotting Feature (Age) and Target (Income) Variables.en.srt (5.6 KB) 005 Adjusting the Weight of our Linear Model.en.srt (4.9 KB) 009 Gradient Descent Intuition - Part 1.mp4 (130.8 MB) 012 Multivariate Linear Regression.mp4 (109.5 MB) 007 Linear Regression Evaluation.mp4 (108.6 MB) 010 Gradient Descent Intuition - Part 2.mp4 (84.2 MB) 008 Linear Regression Closed Form Solution.mp4 (82.0 MB) 011 Visualizing Gradient Descent.mp4 (70.9 MB) 006 Training our First Linear Model.mp4 (40.1 MB) 004 Fitting a Random Line.mp4 (39.6 MB) 003 Plotting Feature (Age) and Target (Income) Variables.mp4 (34.3 MB) 002 Loading the Data into R.mp4 (33.0 MB) 005 Adjusting the Weight of our Linear Model.mp4 (29.8 MB) 001 Linear Regression - Introduction.mp4 (12.8 MB) 06 Classification Problems and Logistic Regression 005 Lo files
Added:	2021-08-31 11:14:07
Language:	English
Peers:	Seeders : 32 , Leechers : 13
Release name:	R for Data Science: Your First Step as a Data Scientist
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