Udemy - Complete Machine Learning with R Studio - ML for 2021 [Giga Course]

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01 Welcome to the course
  • 001 Introduction.mp4 (21.2 MB)
  • 002 Course Resources.html (1.2 KB)
02 Setting up R Studio and R crash course
  • 001 Installing R and R studio.mp4 (40.8 MB)
  • 002 This is a milestone!.mp4 (20.7 MB)
  • 003 Basics of R and R studio.mp4 (48.0 MB)
  • 004 Packages in R.mp4 (98.5 MB)
  • 005 Inputting data part 1_ Inbuilt datasets of R.mp4 (46.1 MB)
  • 006 Inputting data part 2_ Manual data entry.mp4 (30.8 MB)
  • 007 Inputting data part 3_ Importing from CSV or Text files.mp4 (69.0 MB)
  • 008 Creating Barplots in R.mp4 (117.2 MB)
  • 009 Creating Histograms in R.mp4 (51.3 MB)
  • 009 Customer.csv (64.0 KB)
  • 009 Product.txt (137.7 KB)
03 Basics of Statistics
  • 001 Types of Data.mp4 (21.8 MB)
  • 002 Types of Statistics.mp4 (10.9 MB)
  • 003 Describing the data graphically.mp4 (65.4 MB)
  • 004 Measures of Centers.mp4 (38.5 MB)
  • 005 Measures of Dispersion.mp4 (22.8 MB)
04 Intorduction to Machine Learning
  • 001 Introduction to Machine Learning.mp4 (123.3 MB)
  • 002 Building a Machine Learning Model.mp4 (44.9 MB)
05 Data Preprocessing for Regression Analysis
  • 001 Gathering Business Knowledge.mp4 (25.0 MB)
  • 002 Data Exploration.mp4 (23.3 MB)
  • 003 The Data and the Data Dictionary.mp4 (78.3 MB)
  • 004 Importing the dataset into R.mp4 (15.9 MB)
  • 005 Univariate Analysis and EDD.mp4 (27.2 MB)
  • 006 EDD in R.mp4 (112.0 MB)
  • 007 Outlier Treatment.mp4 (27.7 MB)
  • 008 Outlier Treatment in R.mp4 (37.8 MB)
  • 009 Missing Value imputation.mp4 (27.4 MB)
  • 010 Missing Value imputation in R.mp4 (31.7 MB)
  • 011 Seasonality in Data.mp4 (20.8 MB)
  • 012 Bi-variate Analysis and Variable Transformation.mp4 (113.1 MB)
  • 013 Variable transformation in R.mp4 (67.6 MB)
  • 014 Non Usable Variables.mp4 (23.7 MB)
  • 015 Dummy variable creation_ Handling qualitative data.mp4 (40.5 MB)
  • 016 Dummy variable creation in R.mp4 (52.2 MB)
  • 017 Correlation Matrix and cause-effect relationship.mp4 (80.8 MB)
  • 018 Correlation Matrix in R.mp4 (94.9 MB)
  • [GigaCourse.Com].url (0.0 KB)
06 Linear Regression Model
  • 001 The problem statement.mp4 (10.6 MB)
  • 002 Basic equations and Ordinary Least Squared (OLS) method.mp4 (49.9 MB)
  • 003 Assessing Accuracy of predicted coefficients.mp4 (103.9 MB)
  • 004 Assessing Model Accuracy - RSE and R squared.mp4 (49.5 MB)
  • 005 Simple Linear Regression in R.mp4 (50.5 MB)
  • 006 Multiple Linear Regression.mp4 (38.7 MB)
  • 007 The F - statistic.mp4 (63.8 MB)
  • 008 Interpreting result for categorical Variable.mp4 (26.9 MB)
  • 009 Multiple Linear Regression in R.mp4 (72.8 MB)
  • 010 Test-Train split.mp4 (48.8 MB)
  • 011 Bias Variance trade-off.mp4 (29.4 MB)
  • 012 More about test-train split.html (1.4 KB)
  • 013 Test-Train Split in R.mp4 (90.9 MB)
07 Regression models other than OLS
  • 001 Linear models other than OLS.mp4 (19.0 MB)
  • 002 Subset Selection techniques.mp4 (86.7 MB)
  • 003 Subset selection in R.mp4 (76.6 MB)
  • 004 Shrinkage methods - Ridge Regression and The Lasso.mp4 (38.4 MB)
  • 005 Ridge regression and Lasso in R.mp4 (124.0 MB)
08 Classification Models_ Data Preparation
  • 001 The Data and the Data Dictionary.mp4 (87.4 MB)
  • 002 Importing the dataset into R.mp4 (16.3 MB)
  • 003 EDD in R.mp4 (77.8 MB)
  • 004 Outlier Treatment in R.mp4 (31.2 MB)
  • 005 Missing Value imputation in R.mp4 (23.4 MB)
  • 006 Variable transformation in R.mp4 (46.5 MB)
  • 007 Dummy variable creation in R.mp4 (52.5 MB)
09 The Three classification models
  • 001 Three Classifiers and the problem statement.mp4 (22.8 MB)
  • 002 Why can't we use Linear Regression_.mp4 (20.2 MB)
10 Logistic Regression
  • 001 Logistic Regression.mp4 (38.8 MB)
  • 002 Training a Simple Logistic model in R.mp4 (31.0 MB)
  • 003 Results of Simple Logistic Regression.mp4 (30.9 MB)
  • 004 Logistic with multiple predictors.mp4 (9.9 MB)
  • 005 Training multiple predictor Logistic model in R.mp4 (18.3 MB)
  • 006 Confusion Matrix.mp4 (26.6 MB)
  • 007 Evaluating Model performance.mp4 (42.5 MB)
  • 008 Predicting probabilities, assigning classes and making Confusion Matrix in R.mp4 (66.1 MB)
  • [GigaCourse.Com].url (0.0 KB)
11 Linear Discriminant Analysis
  • 001 Linear Discriminant Analysis.mp4 (48.4 MB)
  • 002 Linear Discriminant Analysis in R.mp4 (89.5 MB)
12 K-Nearest Neighbors
  • 001 Test-Train Split.mp4 (45.4 MB)
  • 002 Test-Train Split in R.mp4 (90.2 MB)
  • 003 K-Nearest Neighbors classifier.mp4 (83.3 MB)
  • 004 K-Nearest Neighbors in R.mp4 (79.6 MB)
13 Comparing results from 3 models
  • 001 Understanding the results of classification models.mp4 (45.8 MB)
  • 002 Summary of the three models.mp4 (25.1 MB)
14 Simple Decision Trees
  • 001 Basics of Decision Trees.mp4 (50.6 MB)
  • 002 Understanding a Regression Tree.mp4 (52.2 MB)
  • 003 The stopping criteria for controlling tree growth.mp4 (16.5 MB)
  • 004 The Data set for this part.mp4 (42.0 MB)
  • 005 Course resources_ Notes and Datasets.html (1.0 KB)
  • 006 Importing the Data set into R.mp4 (51.8 MB)
  • 007 Splitting Data into Test and Train Set in R.mp4 (52.6 MB)
  • 008 Building a Regression Tree in R.mp4 (121.9 MB)
  • 009 Pruning a tree.mp4 (22.2 MB)
  • 010 Pruning a Tree in R.mp4 (97.0 MB)
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Description

Udemy - Complete Machine Learning with R Studio - ML for 2021 [Giga Course]

Linear & Logistic Regression, Decision Trees, XGBoost, SVM & other ML models in R programming language - R studio

Created by Start-Tech Academy
Last updated 4/2021
English
English [Auto]

For More Udemy Courses Visit: https://gigacourse.com



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Udemy - Complete Machine Learning with R Studio - ML for 2021 [Giga Course]

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5.9 GB
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Udemy - Complete Machine Learning with R Studio - ML for 2021 [Giga Course]

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