• Урок 1. 00:02:42
    Introduction
  • Урок 2. 00:02:11
    Udemy 101: Getting the Most From This Course
  • Урок 3. 00:10:44
    [Activity] WINDOWS: Installing and Using Anaconda & Course Materials
  • Урок 4. 00:08:18
    [Activity] MAC: Installing and Using Anaconda & Course Materials
  • Урок 5. 00:09:12
    [Activity] LINUX: Installing and Using Anaconda & Course Materials
  • Урок 6. 00:05:00
    Python Basics, Part 1 [Optional]
  • Урок 7. 00:05:18
    [Activity] Python Basics, Part 2 [Optional]
  • Урок 8. 00:02:47
    [Activity] Python Basics, Part 3 [Optional]
  • Урок 9. 00:04:03
    [Activity] Python Basics, Part 4 [Optional]
  • Урок 10. 00:10:09
    Introducing the Pandas Library [Optional]
  • Урок 11. 00:06:59
    Types of Data
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    Mean, Median, Mode
  • Урок 13. 00:08:22
    [Activity] Using mean, median, and mode in Python
  • Урок 14. 00:11:13
    [Activity] Variation and Standard Deviation
  • Урок 15. 00:03:28
    Probability Density Function; Probability Mass Function
  • Урок 16. 00:07:46
    Common Data Distributions
  • Урок 17. 00:12:34
    [Activity] Percentiles and Moments
  • Урок 18. 00:13:47
    [Activity] A Crash Course in matplotlib
  • Урок 19. 00:17:31
    [Activity] Advanced Visualization with Seaborn
  • Урок 20. 00:11:32
    [Activity] Covariance and Correlation
  • Урок 21. 00:16:05
    [Exercise] Conditional Probability
  • Урок 22. 00:02:21
    Exercise Solution: Conditional Probability of Purchase by Age
  • Урок 23. 00:05:24
    Bayes' Theorem
  • Урок 24. 00:11:02
    [Activity] Linear Regression
  • Урок 25. 00:08:05
    [Activity] Polynomial Regression
  • Урок 26. 00:11:27
    [Activity] Multiple Regression, and Predicting Car Prices
  • Урок 27. 00:04:37
    Multi-Level Models
  • Урок 28. 00:08:58
    Supervised vs. Unsupervised Learning, and Train/Test
  • Урок 29. 00:05:48
    [Activity] Using Train/Test to Prevent Overfitting a Polynomial Regression
  • Урок 30. 00:04:00
    Bayesian Methods: Concepts
  • Урок 31. 00:08:06
    [Activity] Implementing a Spam Classifier with Naive Bayes
  • Урок 32. 00:07:24
    K-Means Clustering
  • Урок 33. 00:05:15
    [Activity] Clustering people based on income and age
  • Урок 34. 00:03:10
    Measuring Entropy
  • Урок 35. 00:00:23
    [Activity] WINDOWS: Installing Graphviz
  • Урок 36. 00:01:17
    [Activity] MAC: Installing Graphviz
  • Урок 37. 00:00:55
    [Activity] LINUX: Installing Graphviz
  • Урок 38. 00:08:44
    Decision Trees: Concepts
  • Урок 39. 00:09:48
    [Activity] Decision Trees: Predicting Hiring Decisions
  • Урок 40. 00:06:00
    Ensemble Learning
  • Урок 41. 00:15:30
    [Activity] XGBoost
  • Урок 42. 00:04:28
    Support Vector Machines (SVM) Overview
  • Урок 43. 00:09:30
    [Activity] Using SVM to cluster people using scikit-learn
  • Урок 44. 00:07:58
    User-Based Collaborative Filtering
  • Урок 45. 00:08:16
    Item-Based Collaborative Filtering
  • Урок 46. 00:09:09
    [Activity] Finding Movie Similarities
  • Урок 47. 00:08:00
    [Activity] Improving the Results of Movie Similarities
  • Урок 48. 00:10:23
    [Activity] Making Movie Recommendations to People
  • Урок 49. 00:05:30
    [Exercise] Improve the recommender's results
  • Урок 50. 00:03:45
    K-Nearest-Neighbors: Concepts
  • Урок 51. 00:12:30
    [Activity] Using KNN to predict a rating for a movie
  • Урок 52. 00:05:45
    Dimensionality Reduction; Principal Component Analysis
  • Урок 53. 00:09:06
    [Activity] PCA Example with the Iris data set
  • Урок 54. 00:09:06
    Data Warehousing Overview: ETL and ELT
  • Урок 55. 00:12:45
    Reinforcement Learning
  • Урок 56. 00:12:57
    [Activity] Reinforcement Learning & Q-Learning with Gym
  • Урок 57. 00:05:18
    Understanding a Confusion Matrix
  • Урок 58. 00:06:41
    Measuring Classifiers (Precision, Recall, F1, ROC, AUC)
  • Урок 59. 00:06:16
    Bias/Variance Tradeoff
  • Урок 60. 00:10:56
    [Activity] K-Fold Cross-Validation to avoid overfitting
  • Урок 61. 00:07:11
    Data Cleaning and Normalization
  • Урок 62. 00:10:57
    [Activity] Cleaning web log data
  • Урок 63. 00:03:23
    Normalizing numerical data
  • Урок 64. 00:06:22
    [Activity] Detecting outliers
  • Урок 65. 00:06:04
    Feature Engineering and the Curse of Dimensionality
  • Урок 66. 00:07:49
    Imputation Techniques for Missing Data
  • Урок 67. 00:05:36
    Handling Unbalanced Data: Oversampling, Undersampling, and SMOTE
  • Урок 68. 00:07:52
    Binning, Transforming, Encoding, Scaling, and Shuffling
  • Урок 69. 00:07:00
    [Activity] Installing Spark - Part 1
  • Урок 70. 00:07:21
    [Activity] Installing Spark - Part 2
  • Урок 71. 00:09:11
    Spark Introduction
  • Урок 72. 00:11:43
    Spark and the Resilient Distributed Dataset (RDD)
  • Урок 73. 00:05:10
    Introducing MLLib
  • Урок 74. 00:16:16
    Introduction to Decision Trees in Spark
  • Урок 75. 00:11:24
    [Activity] K-Means Clustering in Spark
  • Урок 76. 00:06:44
    TF / IDF
  • Урок 77. 00:08:22
    [Activity] Searching Wikipedia with Spark
  • Урок 78. 00:08:08
    [Activity] Using the Spark 2.0 DataFrame API for MLLib
  • Урок 79. 00:08:43
    Deploying Models to Real-Time Systems
  • Урок 80. 00:08:24
    A/B Testing Concepts
  • Урок 81. 00:06:00
    T-Tests and P-Values
  • Урок 82. 00:06:05
    [Activity] Hands-on With T-Tests
  • Урок 83. 00:03:25
    Determining How Long to Run an Experiment
  • Урок 84. 00:09:27
    A/B Test Gotchas
  • Урок 85. 00:11:44
    Deep Learning Pre-Requisites
  • Урок 86. 00:11:15
    The History of Artificial Neural Networks
  • Урок 87. 00:12:01
    [Activity] Deep Learning in the Tensorflow Playground
  • Урок 88. 00:09:30
    Deep Learning Details
  • Урок 89. 00:11:30
    Introducing Tensorflow
  • Урок 90. 00:13:12
    [Activity] Using Tensorflow, Part 1
  • Урок 91. 00:12:04
    [Activity] Using Tensorflow, Part 2
  • Урок 92. 00:13:34
    [Activity] Introducing Keras
  • Урок 93. 00:12:06
    [Activity] Using Keras to Predict Political Affiliations
  • Урок 94. 00:11:29
    Convolutional Neural Networks (CNN's)
  • Урок 95. 00:08:03
    [Activity] Using CNN's for handwriting recognition
  • Урок 96. 00:11:03
    Recurrent Neural Networks (RNN's)
  • Урок 97. 00:09:38
    [Activity] Using a RNN for sentiment analysis
  • Урок 98. 00:12:15
    [Activity] Transfer Learning
  • Урок 99. 00:04:40
    Tuning Neural Networks: Learning Rate and Batch Size Hyperparameters
  • Урок 100. 00:06:22
    Deep Learning Regularization with Dropout and Early Stopping
  • Урок 101. 00:11:03
    The Ethics of Deep Learning
  • Урок 102. 00:01:45
    Learning More about Deep Learning
  • Урок 103. 00:06:20
    Your final project assignment
  • Урок 104. 00:10:27
    Final project review
  • Урок 105. 00:03:00
    More to Explore
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