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  1. Урок 1. 00:04:18
    COURSE OVERVIEW LECTURE - PLEASE DO NOT SKIP!
  2. Урок 2. 00:13:50
    Anaconda Python and Jupyter Install and Setup
  3. Урок 3. 00:09:09
    Environment Setup
  4. Урок 4. 00:16:08
    Python Crash Course - Part One
  5. Урок 5. 00:12:08
    Python Crash Course - Part Two
  6. Урок 6. 00:11:20
    Python Crash Course - Part Three
  7. Урок 7. 00:01:30
    Python Crash Course - Exercise Questions
  8. Урок 8. 00:09:27
    Python Crash Course - Exercise Solutions
  9. Урок 9. 00:10:17
    Machine Learning Pathway
  10. Урок 10. 00:02:15
    Introduction to NumPy
  11. Урок 11. 00:22:42
    NumPy Arrays
  12. Урок 12. 00:11:07
    NumPy Indexing and Selection
  13. Урок 13. 00:08:15
    NumPy Operations
  14. Урок 14. 00:01:19
    NumPy Exercises
  15. Урок 15. 00:07:06
    Numpy Exercises - Solutions
  16. Урок 16. 00:04:41
    Introduction to Pandas
  17. Урок 17. 00:09:29
    Series - Part One
  18. Урок 18. 00:10:42
    Series - Part Two
  19. Урок 19. 00:19:28
    DataFrames - Part One - Creating a DataFrame
  20. Урок 20. 00:08:19
    DataFrames - Part Two - Basic Properties
  21. Урок 21. 00:13:58
    DataFrames - Part Three - Working with Columns
  22. Урок 22. 00:14:31
    DataFrames - Part Four - Working with Rows
  23. Урок 23. 00:17:42
    Pandas - Conditional Filtering
  24. Урок 24. 00:13:48
    Pandas - Useful Methods - Apply on Single Column
  25. Урок 25. 00:17:24
    Pandas - Useful Methods - Apply on Multiple Columns
  26. Урок 26. 00:15:49
    Pandas - Useful Methods - Statistical Information and Sorting
  27. Урок 27. 00:12:00
    Missing Data - Overview
  28. Урок 28. 00:18:33
    Missing Data - Pandas Operations
  29. Урок 29. 00:15:50
    GroupBy Operations - Part One
  30. Урок 30. 00:14:19
    GroupBy Operations - Part Two - MultiIndex
  31. Урок 31. 00:10:25
    Combining DataFrames - Concatenation
  32. Урок 32. 00:12:05
    Combining DataFrames - Inner Merge
  33. Урок 33. 00:06:08
    Combining DataFrames - Left and Right Merge
  34. Урок 34. 00:10:39
    Combining DataFrames - Outer Merge
  35. Урок 35. 00:16:06
    Pandas - Text Methods for String Data
  36. Урок 36. 00:21:01
    Pandas - Time Methods for Date and Time Data
  37. Урок 37. 00:10:21
    Pandas Input and Output - CSV Files
  38. Урок 38. 00:14:42
    Pandas Input and Output - HTML Tables
  39. Урок 39. 00:07:21
    Pandas Input and Output - Excel Files
  40. Урок 40. 00:18:20
    Pandas Input and Output - SQL Databases
  41. Урок 41. 00:21:16
    Pandas Pivot Tables
  42. Урок 42. 00:05:27
    Pandas Project Exercise Overview
  43. Урок 43. 00:26:32
    Pandas Project Exercise Solutions
  44. Урок 44. 00:04:07
    Introduction to Matplotlib
  45. Урок 45. 00:12:36
    Matplotlib Basics
  46. Урок 46. 00:07:33
    Matplotlib - Understanding the Figure Object
  47. Урок 47. 00:14:32
    Matplotlib - Implementing Figures and Axes
  48. Урок 48. 00:04:57
    Matplotlib - Figure Parameters
  49. Урок 49. 00:19:18
    Matplotlib - Subplots Functionality
  50. Урок 50. 00:07:03
    Matplotlib Styling - Legends
  51. Урок 51. 00:14:30
    Matplotlib Styling - Colors and Styles
  52. Урок 52. 00:03:53
    Advanced Matplotlib Commands (Optional)
  53. Урок 53. 00:06:11
    Matplotlib Exercise Questions Overview
  54. Урок 54. 00:16:40
    Matplotlib Exercise Questions - Solutions
  55. Урок 55. 00:03:55
    Introduction to Seaborn
  56. Урок 56. 00:18:20
    Scatterplots with Seaborn
  57. Урок 57. 00:09:36
    Distribution Plots - Part One - Understanding Plot Types
  58. Урок 58. 00:16:15
    Distribution Plots - Part Two - Coding with Seaborn
  59. Урок 59. 00:05:41
    Categorical Plots - Statistics within Categories - Understanding Plot Types
  60. Урок 60. 00:09:16
    Categorical Plots - Statistics within Categories - Coding with Seaborn
  61. Урок 61. 00:13:21
    Categorical Plots - Distributions within Categories - Understanding Plot Types
  62. Урок 62. 00:17:58
    Categorical Plots - Distributions within Categories - Coding with Seaborn
  63. Урок 63. 00:05:33
    Seaborn - Comparison Plots - Understanding the Plot Types
  64. Урок 64. 00:09:48
    Seaborn - Comparison Plots - Coding with Seaborn
  65. Урок 65. 00:13:40
    Seaborn Grid Plots
  66. Урок 66. 00:13:19
    Seaborn - Matrix Plots
  67. Урок 67. 00:06:45
    Seaborn Plot Exercises Overview
  68. Урок 68. 00:14:34
    Seaborn Plot Exercises Solutions
  69. Урок 69. 00:12:49
    Capstone Project Overview
  70. Урок 70. 00:17:16
    Capstone Project Solutions - Part One
  71. Урок 71. 00:14:51
    Capstone Project Solutions - Part Two
  72. Урок 72. 00:19:50
    Capstone Project Solutions - Part Three
  73. Урок 73. 00:05:14
    Introduction to Machine Learning Overview Section
  74. Урок 74. 00:09:16
    Why Machine Learning?
  75. Урок 75. 00:07:48
    Types of Machine Learning Algorithms
  76. Урок 76. 00:13:42
    Supervised Machine Learning Process
  77. Урок 77. 00:02:53
    Companion Book - Introduction to Statistical Learning
  78. Урок 78. 00:01:40
    Introduction to Linear Regression Section
  79. Урок 79. 00:09:23
    Linear Regression - Algorithm History
  80. Урок 80. 00:15:44
    Linear Regression - Understanding Ordinary Least Squares
  81. Урок 81. 00:08:13
    Linear Regression - Cost Functions
  82. Урок 82. 00:12:00
    Linear Regression - Gradient Descent
  83. Урок 83. 00:19:38
    Python coding Simple Linear Regression
  84. Урок 84. 00:08:27
    Overview of Scikit-Learn and Python
  85. Урок 85. 00:15:49
    Linear Regression - Scikit-Learn Train Test Split
  86. Урок 86. 00:15:45
    Linear Regression - Scikit-Learn Performance Evaluation - Regression
  87. Урок 87. 00:13:58
    Linear Regression - Residual Plots
  88. Урок 88. 00:17:47
    Linear Regression - Model Deployment and Coefficient Interpretation
  89. Урок 89. 00:08:00
    Polynomial Regression - Theory and Motivation
  90. Урок 90. 00:10:55
    Polynomial Regression - Creating Polynomial Features
  91. Урок 91. 00:09:45
    Polynomial Regression - Training and Evaluation
  92. Урок 92. 00:10:35
    Bias Variance Trade-Off
  93. Урок 93. 00:13:38
    Polynomial Regression - Choosing Degree of Polynomial
  94. Урок 94. 00:06:08
    Polynomial Regression - Model Deployment
  95. Урок 95. 00:06:40
    Regularization Overview
  96. Урок 96. 00:10:00
    Feature Scaling
  97. Урок 97. 00:12:54
    Introduction to Cross Validation
  98. Урок 98. 00:08:38
    Regularization Data Setup
  99. Урок 99. 00:14:30
    L2 Regularization - Ridge Regression Theory
  100. Урок 100. 00:17:43
    L2 Regularization - Ridge Regression - Python Implementation
  101. Урок 101. 00:15:03
    L1 Regularization - Lasso Regression - Background and Implementation
  102. Урок 102. 00:18:08
    L1 and L2 Regularization - Elastic Net
  103. Урок 103. 00:04:31
    Linear Regression Project - Data Overview
  104. Урок 104. 00:15:29
    Introduction to Feature Engineering and Data Preparation
  105. Урок 105. 00:26:34
    Dealing with Outliers
  106. Урок 106. 00:10:43
    Dealing with Missing Data : Part One - Evaluation of Missing Data
  107. Урок 107. 00:20:41
    Dealing with Missing Data : Part Two - Filling or Dropping data based on Rows
  108. Урок 108. 00:23:17
    Dealing with Missing Data : Part 3 - Fixing data based on Columns
  109. Урок 109. 00:12:48
    Dealing with Categorical Data - Encoding Options
  110. Урок 110. 00:03:15
    Section Overview and Introduction
  111. Урок 111. 00:11:21
    Cross Validation - Test | Train Split
  112. Урок 112. 00:14:49
    Cross Validation - Test | Validation | Train Split
  113. Урок 113. 00:11:38
    Cross Validation - cross_val_score
  114. Урок 114. 00:06:57
    Cross Validation - cross_validate
  115. Урок 115. 00:12:15
    Grid Search
  116. Урок 116. 00:03:27
    Linear Regression Project Overview
  117. Урок 117. 00:12:11
    Linear Regression Project - Solutions
  118. Урок 118. 00:05:28
    Introduction to Logistic Regression Section
  119. Урок 119. 00:05:37
    Logistic Regression - Theory and Intuition - Part One: The Logistic Function
  120. Урок 120. 00:04:55
    Logistic Regression - Theory and Intuition - Part Two: Linear to Logistic
  121. Урок 121. 00:17:01
    Logistic Regression - Theory and Intuition - Linear to Logistic Math
  122. Урок 122. 00:15:43
    Logistic Regression - Theory and Intuition - Best fit with Maximum Likelihood
  123. Урок 123. 00:13:58
    Logistic Regression with Scikit-Learn - Part One - EDA
  124. Урок 124. 00:06:39
    Logistic Regression with Scikit-Learn - Part Two - Model Training
  125. Урок 125. 00:09:46
    Classification Metrics - Confusion Matrix and Accuracy
  126. Урок 126. 00:06:01
    Classification Metrics - Precison, Recall, F1-Score
  127. Урок 127. 00:07:14
    Classification Metrics - ROC Curves
  128. Урок 128. 00:15:57
    Logistic Regression with Scikit-Learn - Part Three - Performance Evaluation
  129. Урок 129. 00:08:08
    Multi-Class Classification with Logistic Regression - Part One - Data and EDA
  130. Урок 130. 00:15:48
    Multi-Class Classification with Logistic Regression - Part Two - Model
  131. Урок 131. 00:04:00
    Logistic Regression Exercise Project Overview
  132. Урок 132. 00:21:37
    Logistic Regression Project Exercise - Solutions
  133. Урок 133. 00:02:12
    Introduction to KNN Section
  134. Урок 134. 00:11:19
    KNN Classification - Theory and Intuition
  135. Урок 135. 00:13:41
    KNN Coding with Python - Part One
  136. Урок 136. 00:23:26
    KNN Coding with Python - Part Two - Choosing K
  137. Урок 137. 00:03:19
    KNN Classification Project Exercise Overview
  138. Урок 138. 00:14:13
    KNN Classification Project Exercise Solutions
  139. Урок 139. 00:01:30
    Introduction to Support Vector Machines
  140. Урок 140. 00:04:42
    History of Support Vector Machines
  141. Урок 141. 00:13:26
    SVM - Theory and Intuition - Hyperplanes and Margins
  142. Урок 142. 00:04:58
    SVM - Theory and Intuition - Kernel Intuition
  143. Урок 143. 00:20:51
    SVM - Theory and Intuition - Kernel Trick and Mathematics
  144. Урок 144. 00:11:00
    SVM with Scikit-Learn and Python - Classification Part One
  145. Урок 145. 00:16:03
    SVM with Scikit-Learn and Python - Classification Part Two
  146. Урок 146. 00:21:00
    SVM with Scikit-Learn and Python - Regression Tasks
  147. Урок 147. 00:04:28
    Support Vector Machine Project Overview
  148. Урок 148. 00:18:32
    Support Vector Machine Project Solutions
  149. Урок 149. 00:01:23
    Introduction to Tree Based Methods
  150. Урок 150. 00:09:05
    Decision Tree - History
  151. Урок 151. 00:04:13
    Decision Tree - Terminology
  152. Урок 152. 00:07:53
    Decision Tree - Understanding Gini Impurity
  153. Урок 153. 00:07:33
    Constructing Decision Trees with Gini Impurity - Part One
  154. Урок 154. 00:11:25
    Constructing Decision Trees with Gini Impurity - Part Two
  155. Урок 155. 00:19:19
    Coding Decision Trees - Part One - The Data
  156. Урок 156. 00:20:57
    Coding Decision Trees - Part Two -Creating the Model
  157. Урок 157. 00:01:47
    Introduction to Random Forests Section
  158. Урок 158. 00:11:39
    Random Forests - History and Motivation
  159. Урок 159. 00:03:00
    Random Forests - Key Hyperparameters
  160. Урок 160. 00:10:57
    Random Forests - Number of Estimators and Features in Subsets
  161. Урок 161. 00:12:47
    Random Forests - Bootstrapping and Out-of-Bag Error
  162. Урок 162. 00:11:37
    Coding Classification with Random Forest Classifier - Part One
  163. Урок 163. 00:22:23
    Coding Classification with Random Forest Classifier - Part Two
  164. Урок 164. 00:04:29
    Coding Regression with Random Forest Regressor - Part One - Data
  165. Урок 165. 00:13:34
    Coding Regression with Random Forest Regressor - Part Two - Basic Models
  166. Урок 166. 00:10:31
    Coding Regression with Random Forest Regressor - Part Three - Polynomials
  167. Урок 167. 00:10:37
    Coding Regression with Random Forest Regressor - Part Four - Advanced Models
  168. Урок 168. 00:01:48
    Introduction to Boosting Section
  169. Урок 169. 00:06:12
    Boosting Methods - Motivation and History
  170. Урок 170. 00:19:52
    AdaBoost Theory and Intuition
  171. Урок 171. 00:11:14
    AdaBoost Coding Part One - The Data
  172. Урок 172. 00:18:10
    AdaBoost Coding Part Two - The Model
  173. Урок 173. 00:10:23
    Gradient Boosting Theory
  174. Урок 174. 00:12:49
    Gradient Boosting Coding Walkthrough
  175. Урок 175. 00:14:24
    Introduction to Supervised Learning Capstone Project
  176. Урок 176. 00:18:19
    Solution Walkthrough - Supervised Learning Project - Data and EDA
  177. Урок 177. 00:23:10
    Solution Walkthrough - Supervised Learning Project - Cohort Analysis
  178. Урок 178. 00:21:24
    Solution Walkthrough - Supervised Learning Project - Tree Models
  179. Урок 179. 00:02:37
    Introduction to NLP and Naive Bayes Section
  180. Урок 180. 00:08:05
    Naive Bayes Algorithm - Part One - Bayes Theorem
  181. Урок 181. 00:17:56
    Naive Bayes Algorithm - Part Two - Model Algorithm
  182. Урок 182. 00:10:34
    Feature Extraction from Text - Part One - Theory and Intuition
  183. Урок 183. 00:18:54
    Feature Extraction from Text - Coding Count Vectorization Manually
  184. Урок 184. 00:11:25
    Feature Extraction from Text - Coding with Scikit-Learn
  185. Урок 185. 00:11:24
    Natural Language Processing - Classification of Text - Part One
  186. Урок 186. 00:10:19
    Natural Language Processing - Classification of Text - Part Two
  187. Урок 187. 00:04:38
    Text Classification Project Exercise Overview
  188. Урок 188. 00:15:38
    Text Classification Project Exercise Solutions
  189. Урок 189. 00:08:18
    Unsupervised Learning Overview
  190. Урок 190. 00:02:15
    Introduction to K-Means Clustering Section
  191. Урок 191. 00:10:37
    Clustering General Overview
  192. Урок 192. 00:11:31
    K-Means Clustering Theory
  193. Урок 193. 00:19:49
    K-Means Clustering - Coding Part One
  194. Урок 194. 00:17:19
    K-Means Clustering Coding Part Two
  195. Урок 195. 00:14:33
    K-Means Clustering Coding Part Three
  196. Урок 196. 00:13:54
    K-Means Color Quantization - Part One
  197. Урок 197. 00:14:34
    K-Means Color Quantization - Part Two
  198. Урок 198. 00:07:48
    K-Means Clustering Exercise Overview
  199. Урок 199. 00:13:11
    K-Means Clustering Exercise Solution - Part One
  200. Урок 200. 00:15:52
    K-Means Clustering Exercise Solution - Part Two
  201. Урок 201. 00:08:21
    K-Means Clustering Exercise Solution - Part Three
  202. Урок 202. 00:00:51
    Introduction to Hierarchical Clustering
  203. Урок 203. 00:11:49
    Hierarchical Clustering - Theory and Intuition
  204. Урок 204. 00:16:13
    Hierarchical Clustering - Coding Part One - Data and Visualization
  205. Урок 205. 00:28:23
    Hierarchical Clustering - Coding Part Two - Scikit-Learn
  206. Урок 206. 00:01:01
    Introduction to DBSCAN Section
  207. Урок 207. 00:17:27
    DBSCAN - Theory and Intuition
  208. Урок 208. 00:12:24
    DBSCAN versus K-Means Clustering
  209. Урок 209. 00:07:16
    DBSCAN - Hyperparameter Theory
  210. Урок 210. 00:21:56
    DBSCAN - Hyperparameter Tuning Methods
  211. Урок 211. 00:05:56
    DBSCAN - Outlier Project Exercise Overview
  212. Урок 212. 00:23:21
    DBSCAN - Outlier Project Exercise Solutions
  213. Урок 213. 00:02:48
    Introduction to Principal Component Analysis
  214. Урок 214. 00:10:25
    PCA Theory and Intuition - Part One
  215. Урок 215. 00:11:13
    PCA Theory and Intuition - Part Two
  216. Урок 216. 00:18:17
    PCA - Manual Implementation in Python
  217. Урок 217. 00:12:10
    PCA - SciKit-Learn
  218. Урок 218. 00:07:22
    PCA - Project Exercise Overview
  219. Урок 219. 00:17:04
    PCA - Project Exercise Solution
  220. Урок 220. 00:02:20
    Model Deployment Section Overview
  221. Урок 221. 00:06:52
    Model Deployment Considerations
  222. Урок 222. 00:21:08
    Model Persistence
  223. Урок 223. 00:07:42
    Model Deployment as an API - General Overview
  224. Урок 224. 00:17:01
    Model API - Creating the Script
  225. Урок 225. 00:07:50
    Testing the API