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种子名称： [DesireCourse.Net] Udemy - Data Science and Machine Learning Bootcamp with R
文件类型： 视频
文件数目： 119个文件
文件大小： 2.29 GB
收录时间： 2021-7-10 12:25
已经下载： 3次
资源热度： 203
最近下载： 2025-1-12 17:34

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[DesireCourse.Net] Udemy - Data Science and Machine Learning Bootcamp with R.torrent

1. Course Introduction/1. Introduction to Course.mp412.43MB

1. Course Introduction/2. Course Curriculum.mp45.71MB

1. Course Introduction/3. What is Data Science.mp47.01MB

10. R Lists/1. List Basics.mp419.57MB

11. Data Input and Output with R/1. Introduction to Data Input and Output with R.mp4869.69KB

11. Data Input and Output with R/2. CSV Files with R.mp412.2MB

11. Data Input and Output with R/3. Excel Files with R.mp424.17MB

11. Data Input and Output with R/4. SQL with R.mp425.48MB

11. Data Input and Output with R/5. Web Scraping with R.mp417.4MB

12. R Programming Basics/1. Introduction to Programming Basics.mp41.72MB

12. R Programming Basics/10. Functions Training Exercise - Solutions.mp436.75MB

12. R Programming Basics/2. Logical Operators.mp414.52MB

12. R Programming Basics/3. if, else, and else if Statements.mp425.92MB

12. R Programming Basics/4. Conditional Statements Training Exercise.mp43.48MB

12. R Programming Basics/5. Conditional Statements Training Exercise - Solutions Walkthrough.mp421.07MB

12. R Programming Basics/6. While Loops.mp412MB

12. R Programming Basics/7. For Loops.mp423.11MB

12. R Programming Basics/8. Functions.mp435.1MB

12. R Programming Basics/9. Functions Training Exercise.mp46.7MB

13. Advanced R Programming/1. Introduction to Advanced R Programming.mp41.61MB

13. Advanced R Programming/2. Built-in R Features.mp418.03MB

13. Advanced R Programming/3. Apply.mp428.07MB

13. Advanced R Programming/4. Math Functions with R.mp49.26MB

13. Advanced R Programming/5. Regular Expressions.mp49.73MB

13. Advanced R Programming/6. Dates and Timestamps.mp424.01MB

14. Data Manipulation with R/1. Data Manipulation Overview.mp41.17MB

14. Data Manipulation with R/2. Guide to Using Dplyr.mp425.14MB

14. Data Manipulation with R/3. Guide to Using Dplyr - Part 2.mp420.53MB

14. Data Manipulation with R/4. Pipe Operator.mp413.76MB

14. Data Manipulation with R/6. Dplyr Training Exercise.mp42.68MB

14. Data Manipulation with R/7. Dplyr Training Exercise - Solutions Walkthrough.mp413.79MB

14. Data Manipulation with R/8. Guide to Using Tidyr.mp447.1MB

15. Data Visualization with R/1. Overview of ggplot2.mp411.99MB

15. Data Visualization with R/10. ggplot2 Exercise Solutions.mp426.03MB

15. Data Visualization with R/2. Histograms.mp445.61MB

15. Data Visualization with R/3. Scatterplots.mp437.54MB

15. Data Visualization with R/4. Barplots.mp416.79MB

15. Data Visualization with R/5. Boxplots.mp414.08MB

15. Data Visualization with R/6. 2 Variable Plotting.mp420.41MB

15. Data Visualization with R/7. Coordinates and Faceting.mp424.08MB

15. Data Visualization with R/8. Themes.mp411.24MB

15. Data Visualization with R/9. ggplot2 Exercises.mp46.7MB

16. Data Visualization Project/1. Data Visualization Project.mp411.61MB

16. Data Visualization Project/2. Data Visualization Project - Solutions Walkthrough - Part 1.mp432.6MB

16. Data Visualization Project/3. Data Visualization Project Solutions Walkthrough - Part 2.mp432.13MB

17. Interactive Visualizations with Plotly/1. Overview of Plotly and Interactive Visualizations.mp433.53MB

18. Capstone Data Project/1. Introduction to Capstone Project.mp434.95MB

18. Capstone Data Project/2. Capstone Project Solutions Walkthrough.mp454.46MB

19. Introduction to Machine Learning with R/2. Introduction to Machine Learning.mp448.5MB

20. Machine Learning with R - Linear Regression/1. Introduction to Linear Regression.mp410.18MB

20. Machine Learning with R - Linear Regression/2. Linear Regression with R - Part 1.mp446.94MB

20. Machine Learning with R - Linear Regression/3. Linear Regression with R - Part 2.mp445.93MB

20. Machine Learning with R - Linear Regression/4. Linear Regression with R - Part 3.mp422.79MB

21. Machine Learning Project - Linear Regression/1. Introduction to Linear Regression Project.mp433.68MB

21. Machine Learning Project - Linear Regression/2. ML - Linear Regression Project - Solutions Part 1.mp447.75MB

21. Machine Learning Project - Linear Regression/3. ML - Linear Regression Project - Solutions Part 2.mp426.07MB

22. Machine Learning with R - Logistic Regression/1. Introduction to Logistic Regression.mp419.85MB

22. Machine Learning with R - Logistic Regression/2. Logistic Regression with R - Part 1.mp439.59MB

22. Machine Learning with R - Logistic Regression/3. Logistic Regression with R - Part 2.mp440.93MB

23. Machine Learning Project - Logistic Regression/1. Introduction to Logistic Regression Project.mp410.11MB

23. Machine Learning Project - Logistic Regression/2. Logistic Regression Project Solutions - Part 1.mp447.41MB

23. Machine Learning Project - Logistic Regression/3. Logistic Regression Project Solutions - Part 2.mp432.85MB

23. Machine Learning Project - Logistic Regression/4. Logistic Regression Project - Solutions Part 3.mp432.16MB

24. Machine Learning with R - K Nearest Neighbors/1. Introduction to K Nearest Neighbors.mp48.52MB

24. Machine Learning with R - K Nearest Neighbors/2. K Nearest Neighbors with R.mp440.41MB

25. Machine Learning Project - K Nearest Neighbors/1. Introduction K Nearest Neighbors Project.mp411.72MB

25. Machine Learning Project - K Nearest Neighbors/2. K Nearest Neighbors Project Solutions.mp425.18MB

26. Machine Learning with R - Decision Trees and Random Forests/1. Introduction to Tree Methods.mp411.26MB

26. Machine Learning with R - Decision Trees and Random Forests/2. Decision Trees and Random Forests with R.mp428.82MB

27. Machine Learning Project - Decision Trees and Random Forests/1. Introduction to Decision Trees and Random Forests Project.mp48.44MB

27. Machine Learning Project - Decision Trees and Random Forests/2. Tree Methods Project Solutions - Part 1.mp433.42MB

27. Machine Learning Project - Decision Trees and Random Forests/3. Tree Methods Project Solutions - Part 2.mp49.15MB

28. Machine Learning with R - Support Vector Machines/1. Introduction to Support Vector Machines.mp47.97MB

28. Machine Learning with R - Support Vector Machines/2. Support Vector Machines with R.mp432.98MB

29. Machine Learning Project - Support Vector Machines/1. Introduction to SVM Project.mp49.33MB

29. Machine Learning Project - Support Vector Machines/2. Support Vector Machines Project - Solutions Part 1.mp424.13MB

29. Machine Learning Project - Support Vector Machines/3. Support Vector Machines Project - Solutions Part 2.mp421.03MB

3. Windows Installation Set-Up/1. Windows Installation Procedure.mp417.76MB

30. Machine Learning with R - K-means Clustering/1. Introduction to K-Means Clustering.mp48.58MB

30. Machine Learning with R - K-means Clustering/2. K Means Clustering with R.mp419.2MB

31. Machine Learning Project - K-means Clustering/1. Introduction to K Means Clustering Project.mp47.23MB

31. Machine Learning Project - K-means Clustering/2. K Means Clustering Project - Solutions Walkthrough.mp432.96MB

32. Machine Learning with R - Natural Language Processing/1. Introduction to Natural Language Processing.mp47.58MB

32. Machine Learning with R - Natural Language Processing/2. Natural Language Processing with R - Part 1.mp410.44MB

32. Machine Learning with R - Natural Language Processing/3. Natural Language Processing with R - Part 2.mp435.69MB

33. Machine Learning with R - Neural Nets/1. Introduction to Neural Nets.mp411.22MB

33. Machine Learning with R - Neural Nets/2. Neural Nets with R.mp446.27MB

34. Machine Learning Project - Neural Nets/1. Introduction to Neural Nets Project.mp48.38MB

34. Machine Learning Project - Neural Nets/2. Neural Nets Project - Solutions.mp420.61MB

4. Mac OS Installation Set-Up/1. Mac OS Installation Procedure.mp420.84MB

6. Development Environment Overview/1. Development Environment Overview.mp4870.33KB

6. Development Environment Overview/2. Course Notes.mp425.72MB

6. Development Environment Overview/3. Guide to RStudio.mp428.32MB

7. Introduction to R Basics/1. Introduction to R Basics.mp45.64MB

7. Introduction to R Basics/10. R Basics Training Exercise.mp45.38MB

7. Introduction to R Basics/11. R Basics Training Exercise - Solutions Walkthrough.mp412.79MB

7. Introduction to R Basics/2. Arithmetic in R.mp47.7MB

7. Introduction to R Basics/3. Variables.mp48.93MB

7. Introduction to R Basics/4. R Basic Data Types.mp49.07MB

7. Introduction to R Basics/5. Vector Basics.mp413.66MB

7. Introduction to R Basics/6. Vector Operations.mp47.55MB

7. Introduction to R Basics/7. Comparison Operators.mp410.71MB

7. Introduction to R Basics/8. Vector Indexing and Slicing.mp416.07MB

7. Introduction to R Basics/9. Getting Help with R and RStudio.mp45.63MB

8. R Matrices/1. Introduction to R Matrices.mp41.46MB

8. R Matrices/2. Creating a Matrix.mp418.64MB

8. R Matrices/3. Matrix Arithmetic.mp47.79MB

8. R Matrices/4. Matrix Operations.mp410.77MB

8. R Matrices/5. Matrix Selection and Indexing.mp411.78MB

8. R Matrices/6. Factor and Categorical Matrices.mp414.84MB

8. R Matrices/7. Matrix Training Exercise.mp43.25MB

8. R Matrices/8. Matrix Training Exercises - Solutions Walkthrough.mp424.63MB

9. R Data Frames/1. Introduction to R Data Frames.mp41.36MB

9. R Data Frames/2. Data Frame Basics.mp418.21MB

9. R Data Frames/3. Data Frame Indexing and Selection.mp416.81MB

9. R Data Frames/4. Overview of Data Frame Operations - Part 1.mp430.44MB

9. R Data Frames/5. Overview of Data Frame Operations - Part 2.mp434.14MB

9. R Data Frames/6. Data Frame Training Exercise.mp44.27MB

9. R Data Frames/7. Data Frame Training Exercises - Solutions Walkthrough.mp428.97MB