[ FreeCourseWeb ] Udemy - Machine Learning Basics- Building a Regression model in R

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[ FreeCourseWeb ] Udemy - Machine Learning Basics- Building a Regression model in R 6. Linear Regression Model
  • 20. Ridge regression and Lasso in R.mp4 (124.2 MB)
  • 1. The problem statement.mp4 (10.7 MB)
  • 1. The problem statement.vtt (1.4 KB)
  • 2. Basic equations and Ordinary Least Squared (OLS) method.mp4 (50.2 MB)
  • 2. Basic equations and Ordinary Least Squared (OLS) method.vtt (8.7 KB)
  • 3. Assessing Accuracy of predicted coefficients.mp4 (104.4 MB)
  • 3. Assessing Accuracy of predicted coefficients.vtt (14.0 KB)
  • 4. Assessing Model Accuracy - RSE and R squared.mp4 (49.7 MB)
  • 4. Assessing Model Accuracy - RSE and R squared.vtt (7.1 KB)
  • 5. Simple Linear Regression in R.mp4 (50.6 MB)
  • 5. Simple Linear Regression in R.vtt (7.1 KB)
  • 6. Project Exercise 8.html (0.3 KB)
  • 7. Multiple Linear Regression.mp4 (38.9 MB)
  • 7. Multiple Linear Regression.vtt (5.1 KB)
  • 8. The F - statistic.mp4 (64.2 MB)
  • 8. The F - statistic.vtt (8.0 KB)
  • 9. Interpreting result for categorical Variable.mp4 (27.1 MB)
  • 9. Interpreting result for categorical Variable.vtt (4.7 KB)
  • 10. Multiple Linear Regression in R.mp4 (73.1 MB)
  • 10. Multiple Linear Regression in R.vtt (7.1 KB)
  • 11. Project Exercise 9.html (0.3 KB)
  • 12. Test-Train split.mp4 (49.2 MB)
  • 12. Test-Train split.vtt (9.0 KB)
  • 13. Bias Variance trade-off.mp4 (29.6 MB)
  • 13. Bias Variance trade-off.vtt (5.7 KB)
  • 14. Test-Train Split in R.mp4 (91.0 MB)
  • 14. Test-Train Split in R.vtt (7.3 KB)
  • 15. Linear models other than OLS.mp4 (19.2 MB)
  • 15. Linear models other than OLS.vtt (3.9 KB)
  • 16. Subset Selection techniques.mp4 (87.1 MB)
  • 16. Subset Selection techniques.vtt (11.2 KB)
  • 17. Subset selection in R.mp4 (76.6 MB)
  • 17. Subset selection in R.vtt (6.7 KB)
  • 18. Project Exercise 10.html (0.2 KB)
  • 19. Shrinkage methods - Ridge Regression and The Lasso.mp4 (38.7 MB)
  • 19. Shrinkage methods - Ridge Regression and The Lasso.vtt (7.2 KB)
  • 20. Ridge regression and Lasso in R.vtt (9.9 KB)
  • 21. Project Exercise 11.html (0.4 KB)
  • 22. Final Project Exercise.html (0.3 KB)
  • 22.1 Movie_collection_test.csv.csv (11.7 KB)
  • 23. Course Conclusion.html (1.7 KB)
  • How to Support [ FreeCourseWeb.com ] for Free.txt (0.3 KB)
  • Torrent Downloaded From GloDls.to.txt (0.1 KB)
    • 1. Introduction
    • 1. Welcome to the course!.mp4 (15.4 MB)
    • 1. Welcome to the course!.vtt (2.5 KB)
    • 2. Course contents.mp4 (47.0 MB)
    • 2. Course contents.vtt (7.9 KB)
    2. Basics of Statistics
    • 1. Types of Data.mp4 (25.9 MB)
    • 1. Types of Data.vtt (4.3 KB)
    • 2. Types of Statistics.mp4 (13.2 MB)
    • 2. Types of Statistics.vtt (2.7 KB)
    • 3. Describing the data graphically.mp4 (82.2 MB)
    • 3. Describing the data graphically.vtt (11.3 KB)
    • 4. Measures of Centers.mp4 (45.7 MB)
    • 4. Measures of Centers.vtt (6.4 KB)
    • 5. Practice Exercise 1.html (0.3 KB)
    • 5.1 Exercise 1.pdf.pdf (553.8 KB)
    • 6. Measures of Dispersion.mp4 (28.4 MB)
    • 6. Measures of Dispersion.vtt (4.7 KB)
    • 7. Practice Exercise 2.html (0.3 KB)
    • 7.1 Exercise 2.pdf.pdf (469.9 KB)
    3. Getting started with R and R studio
    • 1. Installing R and R studio.mp4 (40.8 MB)
    • 1. Installing R and R studio.vtt (4.9 KB)
    • 2. Basics of R and R studio.mp4 (48.2 MB)
    • 2. Basics of R and R studio.vtt (9.4 KB)
    • 3. Packages in R.mp4 (98.7 MB)
    • 3. Packages in R.vtt (9.9 KB)
    • 4. Inputting data part 1 Inbuilt datasets of R.mp4 (46.2 MB)
    • 4. Inputting data part 1 Inbuilt datasets of R.vtt (3.5 KB)
    • 5. Inputting data part 2 Manual data entry.mp4 (30.9 MB)
    • 5. Inputting data part 2 Manual data entry.vtt (2.5 KB)
    • 6. Inputting data part 3 Importing from CSV or Text files.mp4 (69.1 MB)
    • 6. Inputting data part 3 Importing from CSV or Text files.vtt (5.5 KB)
    • 7. Creating Barplots in R.mp4 (117.5 MB)
    • 7. Creating Barplots in R.vtt (11.7 KB)
    • 8. Creating Histograms in R.mp4 (51.5 MB)
    • 8. Creating Histograms in R.vtt (5.1 KB)
    4. Introduction to Machine Learning
    • 1. Introduction to Machine Learning.mp4 (123.9 MB)
    • 1. Introduction to Machine Learning.vtt (16.3 KB)
    • 2. Building a Machine Learning model.mp4 (45.3 MB)
    • 2. Building a Machine Learning model.vtt (8.6 KB)
    • 3. Introduction to Machine learning quiz.html (0.2 KB)
    5. Data Preprocessing
    • 1. Gathering Business Knowledge.mp4 (25.1 MB)
    • 1. Gathering Business Knowledge.vtt (3.4 KB)
    • 2. Data Exploration.mp4 (23.4 MB)
    • 2. Data Exploration.vtt (3.2 KB)
    • 3. The Data and the Data Dictionary.mp4 (78.6 MB)
    • 3. The Data and the Data Dictionary.vtt (6.9 KB)
    • 3.1 House_Price.csv.csv (53.5 KB)
    • 4. Importing the dataset into R.mp4 (16.0 MB)
    • 4. Importing the dataset into R.vtt (2.3 KB)
    • 4.1 House_Price.csv.csv (53.5 KB)
    • 5. Project Exercise 1.html (0.4 KB)
    • 5.1 Movie_collection_train.csv.csv (43.3 KB)
    • 6. Univariate Analysis and EDD.mp4 (27.3 MB)
    • 6. Univariate Analysis and EDD.vtt (3.1 KB)
    • 7. EDD in R.mp4 (112.3 MB)
    • 7. EDD in R.vtt (10.1 KB)
    • 8. Project Exercise 2.html (0.2 KB)
    • 9. Outlier Treatment.mp4 (27.8 MB)
    • 9. Outlier Treatment.vtt (4.0 KB)
    • 10. Outlier Treatment in R.mp4 (38.0 MB)
    • 10. Outlier Treatment in R.vtt (3.8 KB)
    • 11. Project Exercise 3.html (0.2 KB)
    • 12. Missing Value imputation.mp4 (27.6 MB)
    • 12. Missing Value imputation.vtt (3.6 KB)
    • 13. Missing Value imputation in

Description

[ FreeCourseWeb.com ]Machine Learning Basics: Building a Regression model in R

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MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English + .VTT | Duration: 6 hour | Size: 2.75 GB
Use Linear Regression to solve business problems and master the basics of Machine Learning Linear Regression in R
What you'll learn

Learn how to solve real life problem using the Linear Regression technique
Preliminary analysis of data using Univariate and Bivariate analysis before running Linear regression
Predict future outcomes basis past data by implementing Simplest Machine Learning algorithm
Understand how to interpret the result of Linear Regression model and translate them into actionable insight
Understanding of basics of statistics and concepts of Machine Learning
Indepth knowledge of data collection and data preprocessing for Machine Learning Linear Regression problem
Learn advanced variations of OLS method of Linear Regression
Course contains a end-to-end DIY project to implement your learnings from the lectures
How to convert business problem into a Machine learning Linear Regression problem
How to do basic statistical operations in R
Advanced Linear regression techniques using GLMNET package of R
Graphically representing data in R before and after analysis

Requirements

Students will need to install R and R studio software but we have a separate lecture to help you install the same

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[ FreeCourseWeb ] Udemy - Machine Learning Basics- Building a Regression model in R

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2.8 GB
seeders:16
leechers:10
[ FreeCourseWeb ] Udemy - Machine Learning Basics- Building a Regression model in R

Torrent hash: 7E92BBEDA65A72B19FB72B31BCCE6F05505D10F7