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Премиум
  • Урок 1. 00:04:18
    COURSE OVERVIEW LECTURE - PLEASE DO NOT SKIP!
  • Урок 2. 00:13:50
    Anaconda Python and Jupyter Install and Setup
  • Урок 3. 00:09:09
    Environment Setup
  • Урок 4. 00:16:08
    Python Crash Course - Part One
  • Урок 5. 00:12:08
    Python Crash Course - Part Two
  • Урок 6. 00:11:20
    Python Crash Course - Part Three
  • Урок 7. 00:01:30
    Python Crash Course - Exercise Questions
  • Урок 8. 00:09:27
    Python Crash Course - Exercise Solutions
  • Урок 9. 00:10:17
    Machine Learning Pathway
  • Урок 10. 00:02:15
    Introduction to NumPy
  • Урок 11. 00:22:42
    NumPy Arrays
  • Урок 12. 00:11:07
    NumPy Indexing and Selection
  • Урок 13. 00:08:15
    NumPy Operations
  • Урок 14. 00:01:19
    NumPy Exercises
  • Урок 15. 00:07:06
    Numpy Exercises - Solutions
  • Урок 16. 00:04:41
    Introduction to Pandas
  • Урок 17. 00:09:29
    Series - Part One
  • Урок 18. 00:10:42
    Series - Part Two
  • Урок 19. 00:19:28
    DataFrames - Part One - Creating a DataFrame
  • Урок 20. 00:08:19
    DataFrames - Part Two - Basic Properties
  • Урок 21. 00:13:58
    DataFrames - Part Three - Working with Columns
  • Урок 22. 00:14:31
    DataFrames - Part Four - Working with Rows
  • Урок 23. 00:17:42
    Pandas - Conditional Filtering
  • Урок 24. 00:13:48
    Pandas - Useful Methods - Apply on Single Column
  • Урок 25. 00:17:24
    Pandas - Useful Methods - Apply on Multiple Columns
  • Урок 26. 00:15:49
    Pandas - Useful Methods - Statistical Information and Sorting
  • Урок 27. 00:12:00
    Missing Data - Overview
  • Урок 28. 00:18:33
    Missing Data - Pandas Operations
  • Урок 29. 00:15:50
    GroupBy Operations - Part One
  • Урок 30. 00:14:19
    GroupBy Operations - Part Two - MultiIndex
  • Урок 31. 00:10:25
    Combining DataFrames - Concatenation
  • Урок 32. 00:12:05
    Combining DataFrames - Inner Merge
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    Combining DataFrames - Left and Right Merge
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    Combining DataFrames - Outer Merge
  • Урок 35. 00:16:06
    Pandas - Text Methods for String Data
  • Урок 36. 00:21:01
    Pandas - Time Methods for Date and Time Data
  • Урок 37. 00:10:21
    Pandas Input and Output - CSV Files
  • Урок 38. 00:14:42
    Pandas Input and Output - HTML Tables
  • Урок 39. 00:07:21
    Pandas Input and Output - Excel Files
  • Урок 40. 00:18:20
    Pandas Input and Output - SQL Databases
  • Урок 41. 00:21:16
    Pandas Pivot Tables
  • Урок 42. 00:05:27
    Pandas Project Exercise Overview
  • Урок 43. 00:26:32
    Pandas Project Exercise Solutions
  • Урок 44. 00:04:07
    Introduction to Matplotlib
  • Урок 45. 00:12:36
    Matplotlib Basics
  • Урок 46. 00:07:33
    Matplotlib - Understanding the Figure Object
  • Урок 47. 00:14:32
    Matplotlib - Implementing Figures and Axes
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    Matplotlib - Figure Parameters
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    Matplotlib - Subplots Functionality
  • Урок 50. 00:07:03
    Matplotlib Styling - Legends
  • Урок 51. 00:14:30
    Matplotlib Styling - Colors and Styles
  • Урок 52. 00:03:53
    Advanced Matplotlib Commands (Optional)
  • Урок 53. 00:06:11
    Matplotlib Exercise Questions Overview
  • Урок 54. 00:16:40
    Matplotlib Exercise Questions - Solutions
  • Урок 55. 00:03:55
    Introduction to Seaborn
  • Урок 56. 00:18:20
    Scatterplots with Seaborn
  • Урок 57. 00:09:36
    Distribution Plots - Part One - Understanding Plot Types
  • Урок 58. 00:16:15
    Distribution Plots - Part Two - Coding with Seaborn
  • Урок 59. 00:05:41
    Categorical Plots - Statistics within Categories - Understanding Plot Types
  • Урок 60. 00:09:16
    Categorical Plots - Statistics within Categories - Coding with Seaborn
  • Урок 61. 00:13:21
    Categorical Plots - Distributions within Categories - Understanding Plot Types
  • Урок 62. 00:17:58
    Categorical Plots - Distributions within Categories - Coding with Seaborn
  • Урок 63. 00:05:33
    Seaborn - Comparison Plots - Understanding the Plot Types
  • Урок 64. 00:09:48
    Seaborn - Comparison Plots - Coding with Seaborn
  • Урок 65. 00:13:40
    Seaborn Grid Plots
  • Урок 66. 00:13:19
    Seaborn - Matrix Plots
  • Урок 67. 00:06:45
    Seaborn Plot Exercises Overview
  • Урок 68. 00:14:34
    Seaborn Plot Exercises Solutions
  • Урок 69. 00:12:49
    Capstone Project Overview
  • Урок 70. 00:17:16
    Capstone Project Solutions - Part One
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    Capstone Project Solutions - Part Two
  • Урок 72. 00:19:50
    Capstone Project Solutions - Part Three
  • Урок 73. 00:05:14
    Introduction to Machine Learning Overview Section
  • Урок 74. 00:09:16
    Why Machine Learning?
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    Types of Machine Learning Algorithms
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    Supervised Machine Learning Process
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    Companion Book - Introduction to Statistical Learning
  • Урок 78. 00:01:40
    Introduction to Linear Regression Section
  • Урок 79. 00:09:23
    Linear Regression - Algorithm History
  • Урок 80. 00:15:44
    Linear Regression - Understanding Ordinary Least Squares
  • Урок 81. 00:08:13
    Linear Regression - Cost Functions
  • Урок 82. 00:12:00
    Linear Regression - Gradient Descent
  • Урок 83. 00:19:38
    Python coding Simple Linear Regression
  • Урок 84. 00:08:27
    Overview of Scikit-Learn and Python
  • Урок 85. 00:15:49
    Linear Regression - Scikit-Learn Train Test Split
  • Урок 86. 00:15:45
    Linear Regression - Scikit-Learn Performance Evaluation - Regression
  • Урок 87. 00:13:58
    Linear Regression - Residual Plots
  • Урок 88. 00:17:47
    Linear Regression - Model Deployment and Coefficient Interpretation
  • Урок 89. 00:08:00
    Polynomial Regression - Theory and Motivation
  • Урок 90. 00:10:55
    Polynomial Regression - Creating Polynomial Features
  • Урок 91. 00:09:45
    Polynomial Regression - Training and Evaluation
  • Урок 92. 00:10:35
    Bias Variance Trade-Off
  • Урок 93. 00:13:38
    Polynomial Regression - Choosing Degree of Polynomial
  • Урок 94. 00:06:08
    Polynomial Regression - Model Deployment
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    Regularization Overview
  • Урок 96. 00:10:00
    Feature Scaling
  • Урок 97. 00:12:54
    Introduction to Cross Validation
  • Урок 98. 00:08:38
    Regularization Data Setup
  • Урок 99. 00:14:30
    L2 Regularization - Ridge Regression Theory
  • Урок 100. 00:17:43
    L2 Regularization - Ridge Regression - Python Implementation
  • Урок 101. 00:15:03
    L1 Regularization - Lasso Regression - Background and Implementation
  • Урок 102. 00:18:08
    L1 and L2 Regularization - Elastic Net
  • Урок 103. 00:04:31
    Linear Regression Project - Data Overview
  • Урок 104. 00:15:29
    Introduction to Feature Engineering and Data Preparation
  • Урок 105. 00:26:34
    Dealing with Outliers
  • Урок 106. 00:10:43
    Dealing with Missing Data : Part One - Evaluation of Missing Data
  • Урок 107. 00:20:41
    Dealing with Missing Data : Part Two - Filling or Dropping data based on Rows
  • Урок 108. 00:23:17
    Dealing with Missing Data : Part 3 - Fixing data based on Columns
  • Урок 109. 00:12:48
    Dealing with Categorical Data - Encoding Options
  • Урок 110. 00:03:15
    Section Overview and Introduction
  • Урок 111. 00:11:21
    Cross Validation - Test | Train Split
  • Урок 112. 00:14:49
    Cross Validation - Test | Validation | Train Split
  • Урок 113. 00:11:38
    Cross Validation - cross_val_score
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    Cross Validation - cross_validate
  • Урок 115. 00:12:15
    Grid Search
  • Урок 116. 00:03:27
    Linear Regression Project Overview
  • Урок 117. 00:12:11
    Linear Regression Project - Solutions
  • Урок 118. 00:05:28
    Introduction to Logistic Regression Section
  • Урок 119. 00:05:37
    Logistic Regression - Theory and Intuition - Part One: The Logistic Function
  • Урок 120. 00:04:55
    Logistic Regression - Theory and Intuition - Part Two: Linear to Logistic
  • Урок 121. 00:17:01
    Logistic Regression - Theory and Intuition - Linear to Logistic Math
  • Урок 122. 00:15:43
    Logistic Regression - Theory and Intuition - Best fit with Maximum Likelihood
  • Урок 123. 00:13:58
    Logistic Regression with Scikit-Learn - Part One - EDA
  • Урок 124. 00:06:39
    Logistic Regression with Scikit-Learn - Part Two - Model Training
  • Урок 125. 00:09:46
    Classification Metrics - Confusion Matrix and Accuracy
  • Урок 126. 00:06:01
    Classification Metrics - Precison, Recall, F1-Score
  • Урок 127. 00:07:14
    Classification Metrics - ROC Curves
  • Урок 128. 00:15:57
    Logistic Regression with Scikit-Learn - Part Three - Performance Evaluation
  • Урок 129. 00:08:08
    Multi-Class Classification with Logistic Regression - Part One - Data and EDA
  • Урок 130. 00:15:48
    Multi-Class Classification with Logistic Regression - Part Two - Model
  • Урок 131. 00:04:00
    Logistic Regression Exercise Project Overview
  • Урок 132. 00:21:37
    Logistic Regression Project Exercise - Solutions
  • Урок 133. 00:02:12
    Introduction to KNN Section
  • Урок 134. 00:11:19
    KNN Classification - Theory and Intuition
  • Урок 135. 00:13:41
    KNN Coding with Python - Part One
  • Урок 136. 00:23:26
    KNN Coding with Python - Part Two - Choosing K
  • Урок 137. 00:03:19
    KNN Classification Project Exercise Overview
  • Урок 138. 00:14:13
    KNN Classification Project Exercise Solutions
  • Урок 139. 00:01:30
    Introduction to Support Vector Machines
  • Урок 140. 00:04:42
    History of Support Vector Machines
  • Урок 141. 00:13:26
    SVM - Theory and Intuition - Hyperplanes and Margins
  • Урок 142. 00:04:58
    SVM - Theory and Intuition - Kernel Intuition
  • Урок 143. 00:20:51
    SVM - Theory and Intuition - Kernel Trick and Mathematics
  • Урок 144. 00:11:00
    SVM with Scikit-Learn and Python - Classification Part One
  • Урок 145. 00:16:03
    SVM with Scikit-Learn and Python - Classification Part Two
  • Урок 146. 00:21:00
    SVM with Scikit-Learn and Python - Regression Tasks
  • Урок 147. 00:04:28
    Support Vector Machine Project Overview
  • Урок 148. 00:18:32
    Support Vector Machine Project Solutions
  • Урок 149. 00:01:23
    Introduction to Tree Based Methods
  • Урок 150. 00:09:05
    Decision Tree - History
  • Урок 151. 00:04:13
    Decision Tree - Terminology
  • Урок 152. 00:07:53
    Decision Tree - Understanding Gini Impurity
  • Урок 153. 00:07:33
    Constructing Decision Trees with Gini Impurity - Part One
  • Урок 154. 00:11:25
    Constructing Decision Trees with Gini Impurity - Part Two
  • Урок 155. 00:19:19
    Coding Decision Trees - Part One - The Data
  • Урок 156. 00:20:57
    Coding Decision Trees - Part Two -Creating the Model
  • Урок 157. 00:01:47
    Introduction to Random Forests Section
  • Урок 158. 00:11:39
    Random Forests - History and Motivation
  • Урок 159. 00:03:00
    Random Forests - Key Hyperparameters
  • Урок 160. 00:10:57
    Random Forests - Number of Estimators and Features in Subsets
  • Урок 161. 00:12:47
    Random Forests - Bootstrapping and Out-of-Bag Error
  • Урок 162. 00:11:37
    Coding Classification with Random Forest Classifier - Part One
  • Урок 163. 00:22:23
    Coding Classification with Random Forest Classifier - Part Two
  • Урок 164. 00:04:29
    Coding Regression with Random Forest Regressor - Part One - Data
  • Урок 165. 00:13:34
    Coding Regression with Random Forest Regressor - Part Two - Basic Models
  • Урок 166. 00:10:31
    Coding Regression with Random Forest Regressor - Part Three - Polynomials
  • Урок 167. 00:10:37
    Coding Regression with Random Forest Regressor - Part Four - Advanced Models
  • Урок 168. 00:01:48
    Introduction to Boosting Section
  • Урок 169. 00:06:12
    Boosting Methods - Motivation and History
  • Урок 170. 00:19:52
    AdaBoost Theory and Intuition
  • Урок 171. 00:11:14
    AdaBoost Coding Part One - The Data
  • Урок 172. 00:18:10
    AdaBoost Coding Part Two - The Model
  • Урок 173. 00:10:23
    Gradient Boosting Theory
  • Урок 174. 00:12:49
    Gradient Boosting Coding Walkthrough
  • Урок 175. 00:14:24
    Introduction to Supervised Learning Capstone Project
  • Урок 176. 00:18:19
    Solution Walkthrough - Supervised Learning Project - Data and EDA
  • Урок 177. 00:23:10
    Solution Walkthrough - Supervised Learning Project - Cohort Analysis
  • Урок 178. 00:21:24
    Solution Walkthrough - Supervised Learning Project - Tree Models
  • Урок 179. 00:02:37
    Introduction to NLP and Naive Bayes Section
  • Урок 180. 00:08:05
    Naive Bayes Algorithm - Part One - Bayes Theorem
  • Урок 181. 00:17:56
    Naive Bayes Algorithm - Part Two - Model Algorithm
  • Урок 182. 00:10:34
    Feature Extraction from Text - Part One - Theory and Intuition
  • Урок 183. 00:18:54
    Feature Extraction from Text - Coding Count Vectorization Manually
  • Урок 184. 00:11:25
    Feature Extraction from Text - Coding with Scikit-Learn
  • Урок 185. 00:11:24
    Natural Language Processing - Classification of Text - Part One
  • Урок 186. 00:10:19
    Natural Language Processing - Classification of Text - Part Two
  • Урок 187. 00:04:38
    Text Classification Project Exercise Overview
  • Урок 188. 00:15:38
    Text Classification Project Exercise Solutions
  • Урок 189. 00:08:18
    Unsupervised Learning Overview
  • Урок 190. 00:02:15
    Introduction to K-Means Clustering Section
  • Урок 191. 00:10:37
    Clustering General Overview
  • Урок 192. 00:11:31
    K-Means Clustering Theory
  • Урок 193. 00:19:49
    K-Means Clustering - Coding Part One
  • Урок 194. 00:17:19
    K-Means Clustering Coding Part Two
  • Урок 195. 00:14:33
    K-Means Clustering Coding Part Three
  • Урок 196. 00:13:54
    K-Means Color Quantization - Part One
  • Урок 197. 00:14:34
    K-Means Color Quantization - Part Two
  • Урок 198. 00:07:48
    K-Means Clustering Exercise Overview
  • Урок 199. 00:13:11
    K-Means Clustering Exercise Solution - Part One
  • Урок 200. 00:15:52
    K-Means Clustering Exercise Solution - Part Two
  • Урок 201. 00:08:21
    K-Means Clustering Exercise Solution - Part Three
  • Урок 202. 00:00:51
    Introduction to Hierarchical Clustering
  • Урок 203. 00:11:49
    Hierarchical Clustering - Theory and Intuition
  • Урок 204. 00:16:13
    Hierarchical Clustering - Coding Part One - Data and Visualization
  • Урок 205. 00:28:23
    Hierarchical Clustering - Coding Part Two - Scikit-Learn
  • Урок 206. 00:01:01
    Introduction to DBSCAN Section
  • Урок 207. 00:17:27
    DBSCAN - Theory and Intuition
  • Урок 208. 00:12:24
    DBSCAN versus K-Means Clustering
  • Урок 209. 00:07:16
    DBSCAN - Hyperparameter Theory
  • Урок 210. 00:21:56
    DBSCAN - Hyperparameter Tuning Methods
  • Урок 211. 00:05:56
    DBSCAN - Outlier Project Exercise Overview
  • Урок 212. 00:23:21
    DBSCAN - Outlier Project Exercise Solutions
  • Урок 213. 00:02:48
    Introduction to Principal Component Analysis
  • Урок 214. 00:10:25
    PCA Theory and Intuition - Part One
  • Урок 215. 00:11:13
    PCA Theory and Intuition - Part Two
  • Урок 216. 00:18:17
    PCA - Manual Implementation in Python
  • Урок 217. 00:12:10
    PCA - SciKit-Learn
  • Урок 218. 00:07:22
    PCA - Project Exercise Overview
  • Урок 219. 00:17:04
    PCA - Project Exercise Solution
  • Урок 220. 00:02:20
    Model Deployment Section Overview
  • Урок 221. 00:06:52
    Model Deployment Considerations
  • Урок 222. 00:21:08
    Model Persistence
  • Урок 223. 00:07:42
    Model Deployment as an API - General Overview
  • Урок 224. 00:17:01
    Model API - Creating the Script
  • Урок 225. 00:07:50
    Testing the API