• Урок 1. 00:02:35
    Introduction to Course
  • Урок 2. 00:02:03
    Course Curriculum
  • Урок 3. 00:03:51
    What is Data Science?
  • Урок 4. 00:06:20
    Windows Installation Procedure
  • Урок 5. 00:05:28
    Mac OS Installation Procedure
  • Урок 6. 00:00:22
    Development Environment Overview
  • Урок 7. 00:09:01
    Course Notes
  • Урок 8. 00:12:34
    Guide to RStudio
  • Урок 9. 00:02:28
    Introduction to R Basics
  • Урок 10. 00:04:31
    Arithmetic in R
  • Урок 11. 00:05:26
    Variables
  • Урок 12. 00:05:32
    R Basic Data Types
  • Урок 13. 00:07:36
    Vector Basics
  • Урок 14. 00:04:24
    Vector Operations
  • Урок 15. 00:06:32
    Comparison Operators
  • Урок 16. 00:09:37
    Vector Indexing and Slicing
  • Урок 17. 00:02:13
    Getting Help with R and RStudio
  • Урок 18. 00:02:14
    R Basics Training Exercise
  • Урок 19. 00:07:22
    R Basics Training Exercise - Solutions Walkthrough
  • Урок 20. 00:00:49
    Introduction to R Matrices
  • Урок 21. 00:10:24
    Creating a Matrix
  • Урок 22. 00:04:16
    Matrix Arithmetic
  • Урок 23. 00:05:23
    Matrix Operations
  • Урок 24. 00:06:35
    Matrix Selection and Indexing
  • Урок 25. 00:08:15
    Factor and Categorical Matrices
  • Урок 26. 00:01:01
    Matrix Training Exercise
  • Урок 27. 00:13:11
    Matrix Training Exercises - Solutions Walkthrough
  • Урок 28. 00:00:45
    Introduction to R Data Frames
  • Урок 29. 00:08:44
    Data Frame Basics
  • Урок 30. 00:09:16
    Data Frame Indexing and Selection
  • Урок 31. 00:15:59
    Overview of Data Frame Operations - Part 1
  • Урок 32. 00:18:41
    Overview of Data Frame Operations - Part 2
  • Урок 33. 00:01:07
    Data Frame Training Exercise
  • Урок 34. 00:15:09
    Data Frame Training Exercises - Solutions Walkthrough
  • Урок 35. 00:09:01
    List Basics
  • Урок 36. 00:00:25
    Introduction to Data Input and Output with R
  • Урок 37. 00:06:10
    CSV Files with R
  • Урок 38. 00:11:44
    Excel Files with R
  • Урок 39. 00:09:57
    SQL with R
  • Урок 40. 00:06:53
    Web Scraping with R
  • Урок 41. 00:00:59
    Introduction to Programming Basics
  • Урок 42. 00:08:06
    Logical Operators
  • Урок 43. 00:15:01
    if, else, and else if Statements
  • Урок 44. 00:01:28
    Conditional Statements Training Exercise
  • Урок 45. 00:12:06
    Conditional Statements Training Exercise - Solutions Walkthrough
  • Урок 46. 00:06:54
    While Loops
  • Урок 47. 00:12:29
    For Loops
  • Урок 48. 00:19:16
    Functions
  • Урок 49. 00:02:15
    Functions Training Exercise
  • Урок 50. 00:20:16
    Functions Training Exercise - Solutions
  • Урок 51. 00:00:55
    Introduction to Advanced R Programming
  • Урок 52. 00:09:50
    Built-in R Features
  • Урок 53. 00:15:17
    Apply
  • Урок 54. 00:03:23
    Math Functions with R
  • Урок 55. 00:05:17
    Regular Expressions
  • Урок 56. 00:12:08
    Dates and Timestamps
  • Урок 57. 00:00:41
    Data Manipulation Overview
  • Урок 58. 00:11:43
    Guide to Using Dplyr
  • Урок 59. 00:10:05
    Guide to Using Dplyr - Part 2
  • Урок 60. 00:06:20
    Pipe Operator
  • Урок 61. 00:01:10
    Dplyr Training Exercise
  • Урок 62. 00:06:48
    Dplyr Training Exercise - Solutions Walkthrough
  • Урок 63. 00:20:32
    Guide to Using Tidyr
  • Урок 64. 00:06:44
    Overview of ggplot2
  • Урок 65. 00:18:38
    Histograms
  • Урок 66. 00:17:01
    Scatterplots
  • Урок 67. 00:07:58
    Barplots
  • Урок 68. 00:07:03
    Boxplots
  • Урок 69. 00:07:49
    2 Variable Plotting
  • Урок 70. 00:10:48
    Coordinates and Faceting
  • Урок 71. 00:05:24
    Themes
  • Урок 72. 00:02:30
    ggplot2 Exercises
  • Урок 73. 00:12:52
    ggplot2 Exercise Solutions
  • Урок 74. 00:02:48
    Data Visualization Project
  • Урок 75. 00:10:57
    Data Visualization Project - Solutions Walkthrough - Part 1
  • Урок 76. 00:10:49
    Data Visualization Project Solutions Walkthrough - Part 2
  • Урок 77. 00:08:50
    Overview of Plotly and Interactive Visualizations
  • Урок 78. 00:07:56
    Introduction to Capstone Project
  • Урок 79. 00:22:00
    Capstone Project Solutions Walkthrough
  • Урок 80. 00:16:49
    Introduction to Machine Learning
  • Урок 81. 00:05:27
    Introduction to Linear Regression
  • Урок 82. 00:19:41
    Linear Regression with R - Part 1
  • Урок 83. 00:20:12
    Linear Regression with R - Part 2
  • Урок 84. 00:11:55
    Linear Regression with R - Part 3
  • Урок 85. 00:08:29
    Introduction to Linear Regression Project
  • Урок 86. 00:21:24
    ML - Linear Regression Project - Solutions Part 1
  • Урок 87. 00:10:56
    ML - Linear Regression Project - Solutions Part 2
  • Урок 88. 00:11:38
    Introduction to Logistic Regression
  • Урок 89. 00:20:01
    Logistic Regression with R - Part 1
  • Урок 90. 00:18:42
    Logistic Regression with R - Part 2
  • Урок 91. 00:01:41
    Introduction to Logistic Regression Project
  • Урок 92. 00:20:03
    Logistic Regression Project Solutions - Part 1
  • Урок 93. 00:15:05
    Logistic Regression Project Solutions - Part 2
  • Урок 94. 00:13:10
    Logistic Regression Project - Solutions Part 3
  • Урок 95. 00:05:01
    Introduction to K Nearest Neighbors
  • Урок 96. 00:19:06
    K Nearest Neighbors with R
  • Урок 97. 00:03:18
    Introduction K Nearest Neighbors Project
  • Урок 98. 00:11:23
    K Nearest Neighbors Project Solutions
  • Урок 99. 00:06:31
    Introduction to Tree Methods
  • Урок 100. 00:12:02
    Decision Trees and Random Forests with R
  • Урок 101. 00:01:42
    Introduction to Decision Trees and Random Forests Project
  • Урок 102. 00:16:43
    Tree Methods Project Solutions - Part 1
  • Урок 103. 00:04:47
    Tree Methods Project Solutions - Part 2
  • Урок 104. 00:04:14
    Introduction to Support Vector Machines
  • Урок 105. 00:14:51
    Support Vector Machines with R
  • Урок 106. 00:02:14
    Introduction to SVM Project
  • Урок 107. 00:11:05
    Support Vector Machines Project - Solutions Part 1
  • Урок 108. 00:10:19
    Support Vector Machines Project - Solutions Part 2
  • Урок 109. 00:04:51
    Introduction to K-Means Clustering
  • Урок 110. 00:09:34
    K Means Clustering with R
  • Урок 111. 00:01:57
    Introduction to K Means Clustering Project
  • Урок 112. 00:17:13
    K Means Clustering Project - Solutions Walkthrough
  • Урок 113. 00:04:26
    Introduction to Natural Language Processing
  • Урок 114. 00:04:51
    Natural Language Processing with R - Part 1
  • Урок 115. 00:15:57
    Natural Language Processing with R - Part 2
  • Урок 116. 00:06:14
    Introduction to Neural Nets
  • Урок 117. 00:21:53
    Neural Nets with R
  • Урок 118. 00:02:09
    Introduction to Neural Nets Project
  • Урок 119. 00:09:13
    Neural Nets Project - Solutions
Этот курс находится в платной подписке. Оформи премиум подписку и смотри Data Science and Machine Learning Bootcamp with R, а также все другие курсы, прямо сейчас!
Премиум