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[FreeCourseSite.com] Udemy - Data Science with Python Complete Course

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视频 2023-6-11 00:07 2024-11-3 03:39 162 8.08 GB 81
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FreeCourseSite com Udemy - Data Science with Python Complete Course

文件列表
  1. 1. Introduction/1. Getting Started with Data Science.mp471.82MB
  2. 10. Project Telecom Churn Production/1. Project Part 1 Let's get our system ready.mp4112.64MB
  3. 10. Project Telecom Churn Production/2. Project part 2.mp457.63MB
  4. 10. Project Telecom Churn Production/3. Project Part 3.mp4129.4MB
  5. 10. Project Telecom Churn Production/4. Project part 4.mp4113.85MB
  6. 10. Project Telecom Churn Production/5. Project Let's Finalise it.mp4119.43MB
  7. 2. Basic Maths Required for Data Science/1. Let's Start with Statistics.mp4128.91MB
  8. 2. Basic Maths Required for Data Science/10. Random Variables.mp457.22MB
  9. 2. Basic Maths Required for Data Science/11. Normal Probability Distribution.mp4104.21MB
  10. 2. Basic Maths Required for Data Science/12. Central Limit Theorem.mp449.37MB
  11. 2. Basic Maths Required for Data Science/13. Hypothesis Testing for Decision Making.mp483.86MB
  12. 2. Basic Maths Required for Data Science/2. Data Quality Issues.mp462.75MB
  13. 2. Basic Maths Required for Data Science/3. Types of Statistics.mp491.56MB
  14. 2. Basic Maths Required for Data Science/4. Measures of Spread.mp4148.93MB
  15. 2. Basic Maths Required for Data Science/5. Measures of Shapes.mp457.64MB
  16. 2. Basic Maths Required for Data Science/6. Plots Visualisation.mp487.49MB
  17. 2. Basic Maths Required for Data Science/7. Inferential Statistics.mp445.98MB
  18. 2. Basic Maths Required for Data Science/8. Probability.mp4179.78MB
  19. 2. Basic Maths Required for Data Science/9. Conditional Probability.mp426.46MB
  20. 3. Python for Data Science/1. Python for Data Science.mp452.24MB
  21. 3. Python for Data Science/10. Conversion of Data Types in Python.mp454.2MB
  22. 3. Python for Data Science/11. Python IO functions.mp414.24MB
  23. 3. Python for Data Science/12. Output Formatting.mp475.6MB
  24. 3. Python for Data Science/13. User Input in Python.mp432.47MB
  25. 3. Python for Data Science/14. Operators in Python.mp4120.69MB
  26. 3. Python for Data Science/15. Control Flow in Python.mp4273.5MB
  27. 3. Python for Data Science/16. Functions in Python.mp4117.24MB
  28. 3. Python for Data Science/17. Types of Functions in Python.mp4228.92MB
  29. 3. Python for Data Science/18. Argument in a Function.mp488.29MB
  30. 3. Python for Data Science/19. Recursive Functions in Python.mp478.22MB
  31. 3. Python for Data Science/2. Python Installation - Google Collab.mp436.01MB
  32. 3. Python for Data Science/20. Lambda or Anonymous Functions in Python.mp447.04MB
  33. 3. Python for Data Science/3. Python Basics.mp420.12MB
  34. 3. Python for Data Science/4. Identifiers in Python.mp435.57MB
  35. 3. Python for Data Science/5. Comments in Python.mp442.41MB
  36. 3. Python for Data Science/6. Python Indentation.mp438.34MB
  37. 3. Python for Data Science/7. Python Statements.mp417.82MB
  38. 3. Python for Data Science/8. Variables in Python.mp443.55MB
  39. 3. Python for Data Science/9. Data Types & Related Stuffs in Python.mp4203.27MB
  40. 4. Advance Python/1. Advance Programming in Python.mp4359.55MB
  41. 4. Advance Python/2. Advance Programming in Python Part 2.mp4599.88MB
  42. 4. Advance Python/3. Data Visualisations.mp4263.61MB
  43. 4. Advance Python/4. Bivariate Plotting.mp4148.84MB
  44. 4. Advance Python/5. Multivariate Plotting.mp4336.46MB
  45. 5. Let's dig deeper/1. EDA.mp427.41MB
  46. 5. Let's dig deeper/2. EDA on Mc'donalds Data Set.mp4689.8MB
  47. 5. Let's dig deeper/3. Exploratory Data Analysis.mp4438.81MB
  48. 6. Let's Explore in to Machine Learning/1. Introduction Machine Learning.mp481.42MB
  49. 6. Let's Explore in to Machine Learning/2. Unsupervised Learning.mp429.34MB
  50. 6. Let's Explore in to Machine Learning/3. Reinforement Learning.mp443.08MB
  51. 7. Module Seven/1. Linear Regression.mp4150.1MB
  52. 7. Module Seven/10. Working on Titanic Data Set.mp484.75MB
  53. 7. Module Seven/11. Random Forest.mp426.1MB
  54. 7. Module Seven/12. Types of Random Forest.mp43.76MB
  55. 7. Module Seven/13. Why Random Forest.mp49.53MB
  56. 7. Module Seven/14. Application of Random Forest.mp417.99MB
  57. 7. Module Seven/15. Random Forest Implementation on Titanic Data Set.mp473.59MB
  58. 7. Module Seven/16. Model Evaluation Technique.mp431.14MB
  59. 7. Module Seven/17. Concept of R-Squared.mp426.84MB
  60. 7. Module Seven/18. Linear Regression.mp476.2MB
  61. 7. Module Seven/19. Classification.mp421.68MB
  62. 7. Module Seven/2. How to use Linear Regression.mp4303.55MB
  63. 7. Module Seven/20. Confusion Matrix.mp437.17MB
  64. 7. Module Seven/21. Recall Sensitivity True Rate of Positive.mp422.85MB
  65. 7. Module Seven/22. FB score.mp436.24MB
  66. 7. Module Seven/23. AUC ROC curve.mp436.48MB
  67. 7. Module Seven/24. Model Evaluation recall Curve.mp490.3MB
  68. 7. Module Seven/3. Logistic Regression.mp4156.34MB
  69. 7. Module Seven/4. Logistic Regression on Titanic Data Set.mp4141.05MB
  70. 7. Module Seven/5. Decision Tree.mp427.03MB
  71. 7. Module Seven/6. Algorithms used in Decision Treee.mp486.75MB
  72. 7. Module Seven/7. Gini Index.mp439.13MB
  73. 7. Module Seven/8. Issues with Decision Tree.mp436.7MB
  74. 7. Module Seven/9. Applications of Decision Tree.mp413.06MB
  75. 8. Module Eight/1. Data Analysis using R.mp419.78MB
  76. 8. Module Eight/2. Data Analysis using R part 2.mp422.74MB
  77. 8. Module Eight/3. All about R Language.mp458.24MB
  78. 9. Featured Topics in Java/1. Big Data.mp484.59MB
  79. 9. Featured Topics in Java/2. Intro to Hadoop.mp457.96MB
  80. 9. Featured Topics in Java/3. Intro to Tableu.mp456.97MB
  81. 9. Featured Topics in Java/4. Intro to Business Analytics.mp453.86MB
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