[FreeCourseWorld.Com] Udemy - Data Science and Machine Learning Bootcamp with R

磁力链接/BT种子名称

[FreeCourseWorld.Com] Udemy - Data Science and Machine Learning Bootcamp with R

磁力链接/BT种子简介

种子哈希：8ac753299b3e48d846af7246834c8c33af0a2ac9
文件大小： 2.4G
已经下载：143次
下载速度：极快
收录时间：2021-03-18
最近下载：2025-06-11

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文件列表

  • 18. Capstone Data Project/2. Capstone Project Solutions Walkthrough.mp4 57.1 MB
  • 19. Introduction to Machine Learning with R/2. Introduction to Machine Learning.mp4 50.9 MB
  • 21. Machine Learning Project - Linear Regression/2. ML - Linear Regression Project - Solutions Part 1.mp4 50.1 MB
  • 23. Machine Learning Project - Logistic Regression/2. Logistic Regression Project Solutions - Part 1.mp4 49.7 MB
  • 14. Data Manipulation with R/8. Guide to Using Tidyr.mp4 49.4 MB
  • 20. Machine Learning with R - Linear Regression/2. Linear Regression with R - Part 1.mp4 49.2 MB
  • 33. Machine Learning with R - Neural Nets/2. Neural Nets with R.mp4 48.5 MB
  • 20. Machine Learning with R - Linear Regression/3. Linear Regression with R - Part 2.mp4 48.2 MB
  • 15. Data Visualization with R/2. Histograms.mp4 47.8 MB
  • 6. Development Environment Overview/2.1 R-Course-HTML-Notes.zip.zip 47.8 MB
  • 1. Course Introduction/4.1 R-Course-HTML-Notes.zip.zip 47.8 MB
  • 22. Machine Learning with R - Logistic Regression/3. Logistic Regression with R - Part 2.mp4 42.9 MB
  • 24. Machine Learning with R - K Nearest Neighbors/2. K Nearest Neighbors with R.mp4 42.4 MB
  • 22. Machine Learning with R - Logistic Regression/2. Logistic Regression with R - Part 1.mp4 41.5 MB
  • 15. Data Visualization with R/3. Scatterplots.mp4 39.4 MB
  • 12. R Programming Basics/10. Functions Training Exercise - Solutions.mp4 38.5 MB
  • 32. Machine Learning with R - Natural Language Processing/3. Natural Language Processing with R - Part 2.mp4 37.4 MB
  • 12. R Programming Basics/8. Functions.mp4 36.8 MB
  • 18. Capstone Data Project/1. Introduction to Capstone Project.mp4 36.6 MB
  • 9. R Data Frames/5. Overview of Data Frame Operations - Part 2.mp4 35.8 MB
  • 21. Machine Learning Project - Linear Regression/1. Introduction to Linear Regression Project.mp4 35.3 MB
  • 17. Interactive Visualizations with Plotly/1. Overview of Plotly and Interactive Visualizations.mp4 35.2 MB
  • 27. Machine Learning Project - Decision Trees and Random Forests/2. Tree Methods Project Solutions - Part 1.mp4 35.0 MB
  • 28. Machine Learning with R - Support Vector Machines/2. Support Vector Machines with R.mp4 34.6 MB
  • 31. Machine Learning Project - K-means Clustering/2. K Means Clustering Project - Solutions Walkthrough.mp4 34.6 MB
  • 23. Machine Learning Project - Logistic Regression/3. Logistic Regression Project Solutions - Part 2.mp4 34.4 MB
  • 16. Data Visualization Project/2. Data Visualization Project - Solutions Walkthrough - Part 1.mp4 34.2 MB
  • 23. Machine Learning Project - Logistic Regression/4. Logistic Regression Project - Solutions Part 3.mp4 33.7 MB
  • 16. Data Visualization Project/3. Data Visualization Project Solutions Walkthrough - Part 2.mp4 33.7 MB
  • 9. R Data Frames/4. Overview of Data Frame Operations - Part 1.mp4 31.9 MB
  • 9. R Data Frames/7. Data Frame Training Exercises - Solutions Walkthrough.mp4 30.4 MB
  • 26. Machine Learning with R - Decision Trees and Random Forests/2. Decision Trees and Random Forests with R.mp4 30.2 MB
  • 6. Development Environment Overview/3. Guide to RStudio.mp4 29.7 MB
  • 13. Advanced R Programming/3. Apply.mp4 29.4 MB
  • 21. Machine Learning Project - Linear Regression/3. ML - Linear Regression Project - Solutions Part 2.mp4 27.3 MB
  • 15. Data Visualization with R/10. ggplot2 Exercise Solutions.mp4 27.3 MB
  • 12. R Programming Basics/3. if, else, and else if Statements.mp4 27.2 MB
  • 6. Development Environment Overview/2. Course Notes.mp4 27.0 MB
  • 11. Data Input and Output with R/4. SQL with R.mp4 26.7 MB
  • 25. Machine Learning Project - K Nearest Neighbors/2. K Nearest Neighbors Project Solutions.mp4 26.4 MB
  • 14. Data Manipulation with R/2. Guide to Using Dplyr.mp4 26.4 MB
  • 8. R Matrices/8. Matrix Training Exercises - Solutions Walkthrough.mp4 25.8 MB
  • 11. Data Input and Output with R/3. Excel Files with R.mp4 25.3 MB
  • 29. Machine Learning Project - Support Vector Machines/2. Support Vector Machines Project - Solutions Part 1.mp4 25.3 MB
  • 15. Data Visualization with R/7. Coordinates and Faceting.mp4 25.2 MB
  • 13. Advanced R Programming/6. Dates and Timestamps.mp4 25.2 MB
  • 12. R Programming Basics/7. For Loops.mp4 24.2 MB
  • 20. Machine Learning with R - Linear Regression/4. Linear Regression with R - Part 3.mp4 23.9 MB
  • 12. R Programming Basics/5. Conditional Statements Training Exercise - Solutions Walkthrough.mp4 22.1 MB
  • 29. Machine Learning Project - Support Vector Machines/3. Support Vector Machines Project - Solutions Part 2.mp4 22.1 MB
  • 4. Mac OS Installation Set-Up/1. Mac OS Installation Procedure.mp4 21.9 MB
  • 34. Machine Learning Project - Neural Nets/2. Neural Nets Project - Solutions.mp4 21.6 MB
  • 14. Data Manipulation with R/3. Guide to Using Dplyr - Part 2.mp4 21.5 MB
  • 15. Data Visualization with R/6. 2 Variable Plotting.mp4 21.4 MB
  • 22. Machine Learning with R - Logistic Regression/1. Introduction to Logistic Regression.mp4 20.8 MB
  • 10. R Lists/1. List Basics.mp4 20.5 MB
  • 30. Machine Learning with R - K-means Clustering/2. K Means Clustering with R.mp4 20.1 MB
  • 8. R Matrices/2. Creating a Matrix.mp4 19.5 MB
  • 9. R Data Frames/2. Data Frame Basics.mp4 19.1 MB
  • 13. Advanced R Programming/2. Built-in R Features.mp4 18.9 MB
  • 3. Windows Installation Set-Up/1. Windows Installation Procedure.mp4 18.6 MB
  • 11. Data Input and Output with R/5. Web Scraping with R.mp4 18.2 MB
  • 9. R Data Frames/3. Data Frame Indexing and Selection.mp4 17.6 MB
  • 15. Data Visualization with R/4. Barplots.mp4 17.6 MB
  • 7. Introduction to R Basics/8. Vector Indexing and Slicing.mp4 16.8 MB
  • 8. R Matrices/6. Factor and Categorical Matrices.mp4 15.6 MB
  • 12. R Programming Basics/2. Logical Operators.mp4 15.2 MB
  • 15. Data Visualization with R/5. Boxplots.mp4 14.8 MB
  • 14. Data Manipulation with R/7. Dplyr Training Exercise - Solutions Walkthrough.mp4 14.5 MB
  • 14. Data Manipulation with R/4. Pipe Operator.mp4 14.4 MB
  • 7. Introduction to R Basics/5. Vector Basics.mp4 14.3 MB
  • 7. Introduction to R Basics/11. R Basics Training Exercise - Solutions Walkthrough.mp4 13.4 MB
  • 1. Course Introduction/1. Introduction to Course.mp4 13.0 MB
  • 11. Data Input and Output with R/2. CSV Files with R.mp4 12.8 MB
  • 12. R Programming Basics/6. While Loops.mp4 12.6 MB
  • 15. Data Visualization with R/1. Overview of ggplot2.mp4 12.6 MB
  • 8. R Matrices/5. Matrix Selection and Indexing.mp4 12.4 MB
  • 25. Machine Learning Project - K Nearest Neighbors/1. Introduction K Nearest Neighbors Project.mp4 12.3 MB
  • 16. Data Visualization Project/1. Data Visualization Project.mp4 12.2 MB
  • 26. Machine Learning with R - Decision Trees and Random Forests/1. Introduction to Tree Methods.mp4 11.8 MB
  • 15. Data Visualization with R/8. Themes.mp4 11.8 MB
  • 33. Machine Learning with R - Neural Nets/1. Introduction to Neural Nets.mp4 11.8 MB
  • 8. R Matrices/4. Matrix Operations.mp4 11.3 MB
  • 7. Introduction to R Basics/7. Comparison Operators.mp4 11.2 MB
  • 32. Machine Learning with R - Natural Language Processing/2. Natural Language Processing with R - Part 1.mp4 10.9 MB
  • 20. Machine Learning with R - Linear Regression/1. Introduction to Linear Regression.mp4 10.7 MB
  • 23. Machine Learning Project - Logistic Regression/1. Introduction to Logistic Regression Project.mp4 10.6 MB
  • 13. Advanced R Programming/5. Regular Expressions.mp4 10.2 MB
  • 29. Machine Learning Project - Support Vector Machines/1. Introduction to SVM Project.mp4 9.8 MB
  • 13. Advanced R Programming/4. Math Functions with R.mp4 9.7 MB
  • 27. Machine Learning Project - Decision Trees and Random Forests/3. Tree Methods Project Solutions - Part 2.mp4 9.6 MB
  • 7. Introduction to R Basics/4. R Basic Data Types.mp4 9.5 MB
  • 7. Introduction to R Basics/3. Variables.mp4 9.4 MB
  • 30. Machine Learning with R - K-means Clustering/1. Introduction to K-Means Clustering.mp4 9.0 MB
  • 24. Machine Learning with R - K Nearest Neighbors/1. Introduction to K Nearest Neighbors.mp4 8.9 MB
  • 27. Machine Learning Project - Decision Trees and Random Forests/1. Introduction to Decision Trees and Random Forests Project.mp4 8.8 MB
  • 34. Machine Learning Project - Neural Nets/1. Introduction to Neural Nets Project.mp4 8.8 MB
  • 28. Machine Learning with R - Support Vector Machines/1. Introduction to Support Vector Machines.mp4 8.4 MB
  • 8. R Matrices/3. Matrix Arithmetic.mp4 8.2 MB
  • 7. Introduction to R Basics/2. Arithmetic in R.mp4 8.1 MB
  • 32. Machine Learning with R - Natural Language Processing/1. Introduction to Natural Language Processing.srt 8.0 MB
  • 32. Machine Learning with R - Natural Language Processing/1. Introduction to Natural Language Processing.mp4 7.9 MB
  • 7. Introduction to R Basics/6. Vector Operations.mp4 7.9 MB
  • 31. Machine Learning Project - K-means Clustering/1. Introduction to K Means Clustering Project.mp4 7.6 MB
  • 1. Course Introduction/3. What is Data Science.mp4 7.4 MB
  • 15. Data Visualization with R/9. ggplot2 Exercises.srt 7.1 MB
  • 12. R Programming Basics/9. Functions Training Exercise.mp4 7.0 MB
  • 15. Data Visualization with R/9. ggplot2 Exercises.mp4 7.0 MB
  • 1. Course Introduction/2. Course Curriculum.mp4 6.0 MB
  • 7. Introduction to R Basics/1. Introduction to R Basics.mp4 5.9 MB
  • 7. Introduction to R Basics/9. Getting Help with R and RStudio.mp4 5.9 MB
  • 7. Introduction to R Basics/10. R Basics Training Exercise.mp4 5.6 MB
  • 9. R Data Frames/6. Data Frame Training Exercise.mp4 4.5 MB
  • 12. R Programming Basics/4. Conditional Statements Training Exercise.mp4 3.6 MB
  • 8. R Matrices/7. Matrix Training Exercise.mp4 3.4 MB
  • 19. Introduction to Machine Learning with R/2.1 Machine Learning Slides.zip.zip 3.0 MB
  • 14. Data Manipulation with R/6. Dplyr Training Exercise.mp4 2.8 MB
  • 12. R Programming Basics/1. Introduction to Programming Basics.mp4 1.8 MB
  • 13. Advanced R Programming/1. Introduction to Advanced R Programming.mp4 1.7 MB
  • 8. R Matrices/1. Introduction to R Matrices.mp4 1.5 MB
  • 9. R Data Frames/1. Introduction to R Data Frames.mp4 1.4 MB
  • 14. Data Manipulation with R/1. Data Manipulation Overview.mp4 1.2 MB
  • 6. Development Environment Overview/1. Development Environment Overview.mp4 891.2 kB
  • 11. Data Input and Output with R/1. Introduction to Data Input and Output with R.mp4 890.6 kB
  • 33. Machine Learning with R - Neural Nets/2. Neural Nets with R.srt 32.1 kB
  • 20. Machine Learning with R - Linear Regression/3. Linear Regression with R - Part 2.srt 30.6 kB
  • 18. Capstone Data Project/2. Capstone Project Solutions Walkthrough.srt 30.3 kB
  • 21. Machine Learning Project - Linear Regression/2. ML - Linear Regression Project - Solutions Part 1.srt 30.0 kB
  • 12. R Programming Basics/10. Functions Training Exercise - Solutions.srt 29.2 kB
  • 20. Machine Learning with R - Linear Regression/2. Linear Regression with R - Part 1.srt 28.9 kB
  • 22. Machine Learning with R - Logistic Regression/2. Logistic Regression with R - Part 1.srt 28.6 kB
  • 15. Data Visualization with R/2. Histograms.srt 28.6 kB
  • 14. Data Manipulation with R/8. Guide to Using Tidyr.srt 28.4 kB
  • 23. Machine Learning Project - Logistic Regression/2. Logistic Regression Project Solutions - Part 1.srt 28.4 kB
  • 19. Introduction to Machine Learning with R/2. Introduction to Machine Learning.srt 27.7 kB
  • 9. R Data Frames/5. Overview of Data Frame Operations - Part 2.srt 27.4 kB
  • 12. R Programming Basics/8. Functions.srt 26.8 kB
  • 24. Machine Learning with R - K Nearest Neighbors/2. K Nearest Neighbors with R.srt 25.9 kB
  • 22. Machine Learning with R - Logistic Regression/3. Logistic Regression with R - Part 2.srt 25.4 kB
  • 9. R Data Frames/4. Overview of Data Frame Operations - Part 1.srt 25.0 kB
  • 15. Data Visualization with R/3. Scatterplots.srt 24.4 kB
  • 31. Machine Learning Project - K-means Clustering/2. K Means Clustering Project - Solutions Walkthrough.srt 23.8 kB
  • 27. Machine Learning Project - Decision Trees and Random Forests/2. Tree Methods Project Solutions - Part 1.srt 23.3 kB
  • 23. Machine Learning Project - Logistic Regression/3. Logistic Regression Project Solutions - Part 2.srt 22.3 kB
  • 28. Machine Learning with R - Support Vector Machines/2. Support Vector Machines with R.srt 21.2 kB
  • 9. R Data Frames/7. Data Frame Training Exercises - Solutions Walkthrough.srt 21.2 kB
  • 32. Machine Learning with R - Natural Language Processing/3. Natural Language Processing with R - Part 2.srt 20.7 kB
  • 13. Advanced R Programming/3. Apply.srt 20.6 kB
  • 12. R Programming Basics/3. if, else, and else if Statements.srt 20.3 kB
  • 15. Data Visualization with R/10. ggplot2 Exercise Solutions.srt 19.7 kB
  • 23. Machine Learning Project - Logistic Regression/4. Logistic Regression Project - Solutions Part 3.srt 19.7 kB
  • 8. R Matrices/8. Matrix Training Exercises - Solutions Walkthrough.srt 19.7 kB
  • 6. Development Environment Overview/3. Guide to RStudio.srt 19.5 kB
  • 12. R Programming Basics/7. For Loops.srt 18.5 kB
  • 14. Data Manipulation with R/2. Guide to Using Dplyr.srt 18.3 kB
  • 11. Data Input and Output with R/3. Excel Files with R.srt 18.0 kB
  • 12. R Programming Basics/5. Conditional Statements Training Exercise - Solutions Walkthrough.srt 17.8 kB
  • 26. Machine Learning with R - Decision Trees and Random Forests/2. Decision Trees and Random Forests with R.srt 17.5 kB
  • 22. Machine Learning with R - Logistic Regression/1. Introduction to Logistic Regression.srt 17.4 kB
  • 13. Advanced R Programming/6. Dates and Timestamps.srt 16.9 kB
  • 16. Data Visualization Project/2. Data Visualization Project - Solutions Walkthrough - Part 1.srt 16.9 kB
  • 16. Data Visualization Project/3. Data Visualization Project Solutions Walkthrough - Part 2.srt 16.7 kB
  • 11. Data Input and Output with R/4. SQL with R.srt 16.4 kB
  • 20. Machine Learning with R - Linear Regression/4. Linear Regression with R - Part 3.srt 16.0 kB
  • 29. Machine Learning Project - Support Vector Machines/2. Support Vector Machines Project - Solutions Part 1.srt 15.7 kB
  • 21. Machine Learning Project - Linear Regression/3. ML - Linear Regression Project - Solutions Part 2.srt 15.5 kB
  • 6. Development Environment Overview/2. Course Notes.srt 15.0 kB
  • 8. R Matrices/2. Creating a Matrix.srt 14.9 kB
  • 29. Machine Learning Project - Support Vector Machines/3. Support Vector Machines Project - Solutions Part 2.srt 14.7 kB
  • 14. Data Manipulation with R/3. Guide to Using Dplyr - Part 2.srt 14.5 kB
  • 30. Machine Learning with R - K-means Clustering/2. K Means Clustering with R.srt 14.4 kB
  • 7. Introduction to R Basics/8. Vector Indexing and Slicing.srt 14.4 kB
  • 15. Data Visualization with R/7. Coordinates and Faceting.srt 14.4 kB
  • 21. Machine Learning Project - Linear Regression/1. Introduction to Linear Regression Project.srt 14.3 kB
  • 25. Machine Learning Project - K Nearest Neighbors/2. K Nearest Neighbors Project Solutions.srt 13.8 kB
  • 17. Interactive Visualizations with Plotly/1. Overview of Plotly and Interactive Visualizations.srt 13.4 kB
  • 9. R Data Frames/3. Data Frame Indexing and Selection.srt 13.1 kB
  • 10. R Lists/1. List Basics.srt 13.1 kB
  • 18. Capstone Data Project/1. Introduction to Capstone Project.srt 13.0 kB
  • 13. Advanced R Programming/2. Built-in R Features.srt 12.8 kB
  • 34. Machine Learning Project - Neural Nets/2. Neural Nets Project - Solutions.srt 12.5 kB
  • 9. R Data Frames/2. Data Frame Basics.srt 12.1 kB
  • 15. Data Visualization with R/4. Barplots.srt 11.8 kB
  • 8. R Matrices/6. Factor and Categorical Matrices.srt 11.7 kB
  • 12. R Programming Basics/2. Logical Operators.srt 11.6 kB
  • 15. Data Visualization with R/5. Boxplots.srt 11.0 kB
  • 11. Data Input and Output with R/5. Web Scraping with R.srt 10.6 kB
  • 12. R Programming Basics/6. While Loops.srt 10.6 kB
  • 15. Data Visualization with R/6. 2 Variable Plotting.srt 10.6 kB
  • 7. Introduction to R Basics/11. R Basics Training Exercise - Solutions Walkthrough.srt 10.5 kB
  • 15. Data Visualization with R/1. Overview of ggplot2.srt 10.5 kB
  • 3. Windows Installation Set-Up/1. Windows Installation Procedure.srt 10.4 kB
  • 7. Introduction to R Basics/5. Vector Basics.srt 10.3 kB
  • 14. Data Manipulation with R/7. Dplyr Training Exercise - Solutions Walkthrough.srt 10.1 kB
  • 8. R Matrices/5. Matrix Selection and Indexing.srt 9.9 kB
  • 7. Introduction to R Basics/7. Comparison Operators.srt 9.8 kB
  • 14. Data Manipulation with R/4. Pipe Operator.srt 9.5 kB
  • 11. Data Input and Output with R/2. CSV Files with R.srt 9.5 kB
  • 33. Machine Learning with R - Neural Nets/1. Introduction to Neural Nets.srt 9.5 kB
  • 26. Machine Learning with R - Decision Trees and Random Forests/1. Introduction to Tree Methods.srt 9.5 kB
  • 4. Mac OS Installation Set-Up/1. Mac OS Installation Procedure.srt 8.9 kB
  • 20. Machine Learning with R - Linear Regression/1. Introduction to Linear Regression.srt 8.3 kB
  • 15. Data Visualization with R/8. Themes.srt 7.9 kB
  • 32. Machine Learning with R - Natural Language Processing/2. Natural Language Processing with R - Part 1.srt 7.8 kB
  • 7. Introduction to R Basics/4. R Basic Data Types.srt 7.8 kB
  • 8. R Matrices/4. Matrix Operations.srt 7.8 kB
  • 7. Introduction to R Basics/3. Variables.srt 7.6 kB
  • 27. Machine Learning Project - Decision Trees and Random Forests/3. Tree Methods Project Solutions - Part 2.srt 7.3 kB
  • 24. Machine Learning with R - K Nearest Neighbors/1. Introduction to K Nearest Neighbors.srt 7.3 kB
  • 30. Machine Learning with R - K-means Clustering/1. Introduction to K-Means Clustering.srt 7.2 kB
  • 13. Advanced R Programming/5. Regular Expressions.srt 7.0 kB
  • 7. Introduction to R Basics/2. Arithmetic in R.srt 6.7 kB
  • 8. R Matrices/3. Matrix Arithmetic.srt 6.6 kB
  • 7. Introduction to R Basics/6. Vector Operations.srt 6.5 kB
  • 28. Machine Learning with R - Support Vector Machines/1. Introduction to Support Vector Machines.srt 6.2 kB
  • 1. Course Introduction/3. What is Data Science.srt 5.9 kB
  • 25. Machine Learning Project - K Nearest Neighbors/1. Introduction K Nearest Neighbors Project.srt 5.4 kB
  • 13. Advanced R Programming/4. Math Functions with R.srt 5.0 kB
  • 16. Data Visualization Project/1. Data Visualization Project.srt 4.8 kB
  • 7. Introduction to R Basics/1. Introduction to R Basics.srt 4.2 kB
  • 12. R Programming Basics/9. Functions Training Exercise.srt 4.0 kB
  • 1. Course Introduction/1. Introduction to Course.srt 4.0 kB
  • 29. Machine Learning Project - Support Vector Machines/1. Introduction to SVM Project.srt 4.0 kB
  • 34. Machine Learning Project - Neural Nets/1. Introduction to Neural Nets Project.srt 3.6 kB
  • 7. Introduction to R Basics/10. R Basics Training Exercise.srt 3.6 kB
  • 31. Machine Learning Project - K-means Clustering/1. Introduction to K Means Clustering Project.srt 3.6 kB
  • 1. Course Introduction/2. Course Curriculum.srt 3.5 kB
  • 7. Introduction to R Basics/9. Getting Help with R and RStudio.srt 3.4 kB
  • 23. Machine Learning Project - Logistic Regression/1. Introduction to Logistic Regression Project.srt 2.9 kB
  • 27. Machine Learning Project - Decision Trees and Random Forests/1. Introduction to Decision Trees and Random Forests Project.srt 2.9 kB
  • 12. R Programming Basics/4. Conditional Statements Training Exercise.srt 2.6 kB
  • 2. Course Best Practices/1. How to Get Help in the Course!.html 2.0 kB
  • 14. Data Manipulation with R/6. Dplyr Training Exercise.srt 2.0 kB
  • 9. R Data Frames/6. Data Frame Training Exercise.srt 1.8 kB
  • 12. R Programming Basics/1. Introduction to Programming Basics.srt 1.6 kB
  • 8. R Matrices/7. Matrix Training Exercise.srt 1.6 kB
  • 13. Advanced R Programming/1. Introduction to Advanced R Programming.srt 1.6 kB
  • 5. Linux Installation/1. LinuxUnbuntu Installation Procedure.html 1.5 kB
  • 1. Course Introduction/4. Course FAQ.html 1.3 kB
  • 8. R Matrices/1. Introduction to R Matrices.srt 1.3 kB
  • 9. R Data Frames/1. Introduction to R Data Frames.srt 1.1 kB
  • 14. Data Manipulation with R/1. Data Manipulation Overview.srt 1.0 kB
  • 17. Interactive Visualizations with Plotly/2. Resources for Plotly and ggplot2.html 962 Bytes
  • 35. Bonus Section/1. Bonus Lecture.html 532 Bytes
  • 11. Data Input and Output with R/1. Introduction to Data Input and Output with R.srt 494 Bytes
  • 6. Development Environment Overview/1. Development Environment Overview.srt 483 Bytes
  • 19. Introduction to Machine Learning with R/1. ISLR PDF.html 393 Bytes
  • 2. Course Best Practices/3. Installation and Set-Up.html 335 Bytes
  • 14. Data Manipulation with R/5. Quick note on Dpylr exercise.html 309 Bytes
  • 2. Course Best Practices/2. Welcome to the Course..html 159 Bytes
  • 8. R Matrices/4.1 Reference of Built-in Functions.html 117 Bytes
  • [FreeCourseWorld.Com].url 54 Bytes

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