Этот материал находится в платной подписке. Оформи премиум подписку и смотри или слушай Machine Learning, Data Science and Deep Learning with Python, а также все другие курсы, прямо сейчас!
• 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
• Урок 12. 00:05:27
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
• 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
• 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