• Урок 1. 00:03:10
    Introduction
  • Урок 2. 00:07:56
    Course Overview
  • Урок 3. 00:18:58
    What is Spark? Why Python?
  • Урок 4. 00:05:59
    Set-up Overview
  • Урок 5. 00:11:26
    Local Installation VirtualBox Part 1
  • Урок 6. 00:14:00
    Local Installation VirtualBox Part 2
  • Урок 7. 00:05:46
    Setting up PySpark
  • Урок 8. 00:02:47
    AWS EC2 Set-up Guide
  • Урок 9. 00:16:19
    Creating the EC2 Instance
  • Урок 10. 00:04:50
    SSH with Mac or Linux
  • Урок 11. 00:15:06
    Installations on EC2
  • Урок 12. 00:11:42
    Databricks Setup
  • Урок 13. 00:17:17
    AWS EMR Setup
  • Урок 14. 00:01:34
    Introduction to Python Crash Course
  • Урок 15. 00:06:50
    Jupyter Notebook Overview
  • Урок 16. 00:16:09
    Python Crash Course Part One
  • Урок 17. 00:12:08
    Python Crash Course Part Two
  • Урок 18. 00:11:20
    Python Crash Course Part Three
  • Урок 19. 00:01:30
    Python Crash Course Exercises
  • Урок 20. 00:09:27
    Python Crash Course Exercise Solutions
  • Урок 21. 00:02:27
    Introduction to Spark DataFrames
  • Урок 22. 00:10:52
    Spark DataFrame Basics
  • Урок 23. 00:09:56
    Spark DataFrame Basics Part Two
  • Урок 24. 00:10:16
    Spark DataFrame Basic Operations
  • Урок 25. 00:12:28
    Groupby and Aggregate Operations
  • Урок 26. 00:08:57
    Missing Data
  • Урок 27. 00:10:05
    Dates and Timestamps
  • Урок 28. 00:03:14
    DataFrame Project Exercise
  • Урок 29. 00:16:54
    DataFrame Project Exercise Solutions
  • Урок 30. 00:10:22
    Introduction to Machine Learning and ISLR
  • Урок 31. 00:09:05
    Machine Learning with Spark and Python with MLlib
  • Урок 32. 00:05:04
    Linear Regression Theory and Reading
  • Урок 33. 00:14:20
    Linear Regression Documentation Example
  • Урок 34. 00:06:47
    Regression Evaluation
  • Урок 35. 00:15:14
    Linear Regression Example Code Along
  • Урок 36. 00:03:12
    Linear Regression Consulting Project
  • Урок 37. 00:15:33
    Linear Regression Consulting Project Solutions
  • Урок 38. 00:11:23
    Logistic Regression Theory and Reading
  • Урок 39. 00:15:40
    Logistic Regression Example Code Along
  • Урок 40. 00:18:37
    Logistic Regression Code Along
  • Урок 41. 00:03:14
    Logistic Regression Consulting Project
  • Урок 42. 00:11:14
    Logistic Regression Consulting Project Solutions
  • Урок 43. 00:08:01
    Tree Methods Theory and Reading
  • Урок 44. 00:13:19
    Tree Methods Documentation Examples
  • Урок 45. 00:20:38
    Decision Tress and Random Forest Code Along Examples
  • Урок 46. 00:02:34
    Random Forest - Classification Consulting Project
  • Урок 47. 00:08:01
    Random Forest Classification Consulting Project Solutions
  • Урок 48. 00:06:55
    K-means Clustering Theory and Reading
  • Урок 49. 00:09:52
    KMeans Clustering Documentation Example
  • Урок 50. 00:12:46
    Clustering Example Code Along
  • Урок 51. 00:03:10
    Clustering Consulting Project
  • Урок 52. 00:08:43
    Clustering Consulting Project Solutions
  • Урок 53. 00:06:33
    Introduction to Recommender Systems
  • Урок 54. 00:12:09
    Recommender System - Code Along Project
  • Урок 55. 00:08:03
    Introduction to Natural Language Processing
  • Урок 56. 00:16:13
    NLP Tools Part One
  • Урок 57. 00:08:06
    NLP Tools Part Two
  • Урок 58. 00:14:09
    Natural Language Processing Code Along Project
  • Урок 59. 00:10:20
    Introduction to Streaming with Spark!
  • Урок 60. 00:11:48
    Spark Streaming Documentation Example
  • Урок 61. 00:04:30
    Spark Streaming Twitter Project - Part
  • Урок 62. 00:13:09
    Spark Streaming Twitter Project - Part Two
  • Урок 63. 00:17:36
    Spark Streaming Twitter Project - Part Three
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