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[] Udemy - Python for Data Science & Machine Learning from A-Z

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  • 文件大小:7GB
  • 下载次数:1
  • 最近下载:2021-10-17 12:16:57
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文件列表

  1. 19. PCA/7. PCA - Image Compression.mp4 250MB
  2. 16. Ensemble Learning and Random Forests/6. Implementing Random Forests from scratch Part 1.mp4 203MB
  3. 15. Decision Trees/7. ID3 - Putting Everything Together.mp4 182MB
  4. 14. K Nearest Neighbors/3. EDA on Iris Dataset.mp4 162MB
  5. 15. Decision Trees/3. What is Entropy and Information Gain.mp4 136MB
  6. 19. PCA/9. PCA - Biplot and the Screen Plot.mp4 136MB
  7. 1. Introduction/6. How To Get a Data Science Job.mp4 131MB
  8. 1. Introduction/5. What is a Data Scientist.mp4 127MB
  9. 17. Support Vector Machines/6. SVM - Kernel Types.mp4 126MB
  10. 15. Decision Trees/2. EDA on Adult Dataset.mp4 123MB
  11. 15. Decision Trees/8. Evaluating our ID3 implementation.mp4 122MB
  12. 19. PCA/8. PCA Data Preprocessing.mp4 120MB
  13. 15. Decision Trees/13. Pruning.mp4 113MB
  14. 17. Support Vector Machines/8. SVM with Non-linear Dataset.mp4 112MB
  15. 13. Linear and Logistic Regression/3. Linear Regression + Correlation Methods.mp4 110MB
  16. 18. K-means/3. Representing Clusters.mp4 110MB
  17. 3. Python For Data Science/15. Python Dictionaries.mp4 104MB
  18. 17. Support Vector Machines/7. SVM with Linear Dataset (Iris).mp4 102MB
  19. 18. K-means/1. Unsupervised Machine Learning Intro.mp4 101MB
  20. 9. Machine Learning/1. Introduction To Machine Learning.mp4 99MB
  21. 16. Ensemble Learning and Random Forests/2. What is Ensemble Learning.mp4 92MB
  22. 14. K Nearest Neighbors/7. Hyperparameter tuning using the cross-validation.mp4 90MB
  23. 2. Data Science & Machine Learning Concepts/2. What is Data Science.mp4 88MB
  24. 14. K Nearest Neighbors/5. Implement the KNN algorithm from scratch.mp4 87MB
  25. 3. Python For Data Science/7. Python Operators.mp4 87MB
  26. 16. Ensemble Learning and Random Forests/13. AdaBoost Part 2.mp4 86MB
  27. 15. Decision Trees/4. The Decision Tree ID3 algorithm from scratch Part 1.mp4 85MB
  28. 2. Data Science & Machine Learning Concepts/3. What is Machine Learning.mp4 83MB
  29. 18. K-means/2. Unsupervised Machine Learning Continued.mp4 83MB
  30. 15. Decision Trees/12. Decision Trees Hyper-parameters.mp4 81MB
  31. 1. Introduction/4. Data Science Job Roles.mp4 80MB
  32. 1. Introduction/7. Data Science Projects Overview.mp4 79MB
  33. 2. Data Science & Machine Learning Concepts/4. Machine Learning Concepts & Algorithms.mp4 78MB
  34. 2. Data Science & Machine Learning Concepts/5. What is Deep Learning.mp4 78MB
  35. 17. Support Vector Machines/5. Kernel Trick.mp4 77MB
  36. 2. Data Science & Machine Learning Concepts/6. Machine Learning vs Deep Learning.mp4 76MB
  37. 8. Python Data Visualization/1. Data Visualization Overview.mp4 73MB
  38. 7. Pandas Data Analysis/2. Introduction to Pandas Continued.mp4 71MB
  39. 3. Python For Data Science/19. Object Oriented Programming in Python.mp4 70MB
  40. 19. PCA/10. PCA - Feature Scaling and Screen Plot.mp4 68MB
  41. 15. Decision Trees/10. Visualizing the tree.mp4 68MB
  42. 19. PCA/12. PCA - Visualization.mp4 68MB
  43. 17. Support Vector Machines/3. Hard vs Soft Margins.mp4 66MB
  44. 15. Decision Trees/9. Compare with Sklearn implementation.mp4 66MB
  45. 15. Decision Trees/5. The Decision Tree ID3 algorithm from scratch Part 2.mp4 64MB
  46. 3. Python For Data Science/18. Python Functions.mp4 63MB
  47. 5. Probability & Hypothesis Testing/4. Hypothesis Testing Overview.mp4 61MB
  48. 3. Python For Data Science/13. More about Lists.mp4 60MB
  49. 19. PCA/4. PCA Algorithm Steps (Mathematics).mp4 58MB
  50. 3. Python For Data Science/9. Python Strings.mp4 56MB
  51. 16. Ensemble Learning and Random Forests/3. What is Bootstrap Sampling.mp4 56MB
  52. 3. Python For Data Science/10. Python Conditional Statements.mp4 55MB
  53. 3. Python For Data Science/14. Python Tuples.mp4 55MB
  54. 10. Data Loading & Exploration/1. Exploratory Data Analysis.mp4 51MB
  55. 16. Ensemble Learning and Random Forests/7. Implementing Random Forests from scratch Part 2.mp4 51MB
  56. 14. K Nearest Neighbors/10. Feature scaling in KNN.mp4 49MB
  57. 17. Support Vector Machines/2. SVM intuition.mp4 49MB
  58. 15. Decision Trees/15. Decision Trees Pros and Cons.mp4 48MB
  59. 19. PCA/2. What is PCA.mp4 47MB
  60. 3. Python For Data Science/17. Compound Data Types & When to use each one.mp4 47MB
  61. 1. Introduction/2. Data Science + Machine Learning Marketplace.mp4 47MB
  62. 7. Pandas Data Analysis/1. Introduction to Pandas.mp4 47MB
  63. 14. K Nearest Neighbors/11. Curse of dimensionality.mp4 46MB
  64. 4. Statistics for Data Science/6. Inferential Statistics.mp4 45MB
  65. 16. Ensemble Learning and Random Forests/5. Out-of-Bag Error (OOB Error).mp4 42MB
  66. 6. NumPy Data Analysis/3. NumPy Arrays Basics.mp4 40MB
  67. 16. Ensemble Learning and Random Forests/9. Random Forests Hyper-Parameters.mp4 40MB
  68. 17. Support Vector Machines/10. SMV - Project Overview.mp4 40MB
  69. 19. PCA/5. Covariance Matrix vs SVD.mp4 39MB
  70. 3. Python For Data Science/5. Python Variables, Booleans and None.mp4 38MB
  71. 4. Statistics for Data Science/3. Measure of Variability.mp4 38MB
  72. 20. Data Science Career/1. Creating A Data Science Resume.mp4 37MB
  73. 19. PCA/11. PCA - Supervised vs Unsupervised.mp4 36MB
  74. 16. Ensemble Learning and Random Forests/11. What is Boosting.mp4 35MB
  75. 17. Support Vector Machines/1. SVM Outline.mp4 35MB
  76. 3. Python For Data Science/6. Getting Started with Google Colab.mp4 35MB
  77. 6. NumPy Data Analysis/4. NumPy Array Indexing.mp4 35MB
  78. 6. NumPy Data Analysis/1. Intro NumPy Array Data Types.mp4 35MB
  79. 4. Statistics for Data Science/4. Measure of Variability Continued.mp4 35MB
  80. 15. Decision Trees/6. The Decision Tree ID3 algorithm from scratch Part 3.mp4 33MB
  81. 5. Probability & Hypothesis Testing/3. Relative Frequency.mp4 33MB
  82. 6. NumPy Data Analysis/2. NumPy Arrays.mp4 32MB
  83. 19. PCA/1. PCA Section Overview.mp4 32MB
  84. 15. Decision Trees/11. Plot the features importance.mp4 32MB
  85. 13. Linear and Logistic Regression/1. Linear Regression Intro.mp4 31MB
  86. 20. Data Science Career/6. Personal Branding.mp4 30MB
  87. 14. K Nearest Neighbors/9. Manhattan vs Euclidean Distance.mp4 30MB
  88. 14. K Nearest Neighbors/13. KNN pros and cons.mp4 30MB
  89. 20. Data Science Career/4. Getting Started with Freelancing.mp4 30MB
  90. 11. Data Cleaning/2. Data Cleaning.mp4 30MB
  91. 20. Data Science Career/5. Top Freelance Websites.mp4 30MB
  92. 16. Ensemble Learning and Random Forests/4. What is Bagging.mp4 29MB
  93. 3. Python For Data Science/16. Python Sets.mp4 29MB
  94. 1. Introduction/3. Data Science Job Opportunities.mp4 29MB
  95. 14. K Nearest Neighbors/12. KNN use cases.mp4 29MB
  96. 16. Ensemble Learning and Random Forests/8. Compare with sklearn implementation.mp4 28MB
  97. 8. Python Data Visualization/3. Python Data Visualization Implementation.mp4 27MB
  98. 5. Probability & Hypothesis Testing/1. What is Exactly is Probability.mp4 27MB
  99. 4. Statistics for Data Science/8. Sampling Distribution.mp4 26MB
  100. 3. Python For Data Science/8. Python Numbers & Booleans.mp4 26MB
  101. 3. Python For Data Science/11. Python For Loops and While Loops.mp4 26MB
  102. 16. Ensemble Learning and Random Forests/12. AdaBoost Part 1.mp4 26MB
  103. 17. Support Vector Machines/9. SVM with Regression.mp4 25MB
  104. 20. Data Science Career/3. How to Contact Recruiters.mp4 25MB
  105. 14. K Nearest Neighbors/6. Compare the result with the sklearn library.mp4 25MB
  106. 20. Data Science Career/7. Networking Do's and Don'ts.mp4 24MB
  107. 4. Statistics for Data Science/5. Measures of Variable Relationship.mp4 24MB
  108. 20. Data Science Career/2. Data Science Cover Letter.mp4 23MB
  109. 4. Statistics for Data Science/2. Descriptive Statistics.mp4 21MB
  110. 3. Python For Data Science/12. Python Lists.mp4 21MB
  111. 4. Statistics for Data Science/1. Intro To Statistics.mp4 21MB
  112. 17. Support Vector Machines/4. C hyper-parameter.mp4 21MB
  113. 16. Ensemble Learning and Random Forests/10. Random Forests Pros and Cons.mp4 20MB
  114. 19. PCA/3. PCA Drawbacks.mp4 19MB
  115. 11. Data Cleaning/1. Feature Scaling.mp4 19MB
  116. 15. Decision Trees/14. [Optional] Gain Ration.mp4 19MB
  117. 12. Feature Selecting and Engineering/1. Feature Engineering.mp4 18MB
  118. 3. Python For Data Science/1. What is Programming.mp4 18MB
  119. 6. NumPy Data Analysis/6. Broadcasting.mp4 18MB
  120. 13. Linear and Logistic Regression/4. Linear Regression Implementation.mp4 18MB
  121. 1. Introduction/1. Who is This Course For.mp4 17MB
  122. 6. NumPy Data Analysis/5. NumPy Array Computations.mp4 17MB
  123. 14. K Nearest Neighbors/8. The decision boundary visualization.mp4 17MB
  124. 15. Decision Trees/1. Decision Trees Section Overview.mp4 16MB
  125. 3. Python For Data Science/2. Why Python for Data Science.mp4 16MB
  126. 16. Ensemble Learning and Random Forests/1. Ensemble Learning Section Overview.mp4 16MB
  127. 8. Python Data Visualization/2. Different Data Visualization Libraries in Python.mp4 16MB
  128. 13. Linear and Logistic Regression/2. Gradient Descent.mp4 16MB
  129. 14. K Nearest Neighbors/2. parametric vs non-parametric models.mp4 16MB
  130. 20. Data Science Career/8. Importance of a Website.mp4 15MB
  131. 15. Decision Trees/16. [Project] Predict whether income exceeds $50Kyr - Overview.mp4 15MB
  132. 5. Probability & Hypothesis Testing/2. Expected Values.mp4 15MB
  133. 3. Python For Data Science/3. What is Jupyter.mp4 15MB
  134. 2. Data Science & Machine Learning Concepts/1. Why We Use Python.mp4 14MB
  135. 14. K Nearest Neighbors/1. KNN Overview.mp4 13MB
  136. 19. PCA/6. PCA - Main Applications.mp4 10MB
  137. 13. Linear and Logistic Regression/5. Logistic Regression.mp4 9MB
  138. 3. Python For Data Science/4. What is Google Colab.mp4 8MB
  139. 14. K Nearest Neighbors/4. The KNN Intuition.mp4 8MB
  140. 4. Statistics for Data Science/7. Measure of Asymmetry.mp4 7MB
  141. 9. Machine Learning/1.1 Supervised Learning.pdf 837KB
  142. 18. K-means/1.1 Unsupervised Learning.pdf 637KB
  143. 3. Python For Data Science/3.1 Jupyter Notebook.pdf 307KB
  144. 3. Python For Data Science/2.2 Python Basics.pdf 128KB
  145. 7. Pandas Data Analysis/1.1 Pandas.pdf 110KB
  146. 6. NumPy Data Analysis/1.1 NumPy Basics.pdf 77KB
  147. 7. Pandas Data Analysis/1.2 Pandas Basics.pdf 77KB
  148. 3. Python For Data Science/2.1 Importing Python Data.pdf 62KB
  149. 19. PCA/7. PCA - Image Compression.srt 39KB
  150. 13. Linear and Logistic Regression/3. Linear Regression + Correlation Methods.srt 39KB
  151. 9. Machine Learning/1. Introduction To Machine Learning.srt 37KB
  152. 8. Python Data Visualization/1. Data Visualization Overview.srt 37KB
  153. 14. K Nearest Neighbors/3. EDA on Iris Dataset.srt 32KB
  154. 3. Python For Data Science/7. Python Operators.srt 31KB
  155. 15. Decision Trees/7. ID3 - Putting Everything Together.srt 31KB
  156. 1. Introduction/6. How To Get a Data Science Job.srt 31KB
  157. 16. Ensemble Learning and Random Forests/6. Implementing Random Forests from scratch Part 1.srt 30KB
  158. 18. K-means/1. Unsupervised Machine Learning Intro.srt 29KB
  159. 15. Decision Trees/3. What is Entropy and Information Gain.srt 29KB
  160. 18. K-means/2. Unsupervised Machine Learning Continued.srt 29KB
  161. 18. K-means/3. Representing Clusters.srt 28KB
  162. 3. Python For Data Science/15. Python Dictionaries.srt 28KB
  163. 1. Introduction/5. What is a Data Scientist.srt 27KB
  164. 7. Pandas Data Analysis/2. Introduction to Pandas Continued.srt 27KB
  165. 17. Support Vector Machines/6. SVM - Kernel Types.srt 27KB
  166. 19. PCA/9. PCA - Biplot and the Screen Plot.srt 26KB
  167. 3. Python For Data Science/19. Object Oriented Programming in Python.srt 26KB
  168. 15. Decision Trees/8. Evaluating our ID3 implementation.srt 25KB
  169. 15. Decision Trees/13. Pruning.srt 24KB
  170. 15. Decision Trees/2. EDA on Adult Dataset.srt 24KB
  171. 2. Data Science & Machine Learning Concepts/4. Machine Learning Concepts & Algorithms.srt 24KB
  172. 2. Data Science & Machine Learning Concepts/3. What is Machine Learning.srt 23KB
  173. 7. Pandas Data Analysis/1. Introduction to Pandas.srt 22KB
  174. 4. Statistics for Data Science/6. Inferential Statistics.srt 22KB
  175. 2. Data Science & Machine Learning Concepts/2. What is Data Science.srt 21KB
  176. 19. PCA/8. PCA Data Preprocessing.srt 21KB
  177. 16. Ensemble Learning and Random Forests/13. AdaBoost Part 2.srt 21KB
  178. 3. Python For Data Science/18. Python Functions.srt 21KB
  179. 17. Support Vector Machines/7. SVM with Linear Dataset (Iris).srt 20KB
  180. 3. Python For Data Science/13. More about Lists.srt 19KB
  181. 1. Introduction/7. Data Science Projects Overview.srt 19KB
  182. 10. Data Loading & Exploration/1. Exploratory Data Analysis.srt 19KB
  183. 17. Support Vector Machines/3. Hard vs Soft Margins.srt 19KB
  184. 19. PCA/4. PCA Algorithm Steps (Mathematics).srt 19KB
  185. 17. Support Vector Machines/8. SVM with Non-linear Dataset.srt 18KB
  186. 6. NumPy Data Analysis/1. Intro NumPy Array Data Types.srt 18KB
  187. 4. Statistics for Data Science/3. Measure of Variability.srt 18KB
  188. 17. Support Vector Machines/5. Kernel Trick.srt 18KB
  189. 3. Python For Data Science/10. Python Conditional Statements.srt 18KB
  190. 3. Python For Data Science/17. Compound Data Types & When to use each one.srt 18KB
  191. 2. Data Science & Machine Learning Concepts/6. Machine Learning vs Deep Learning.srt 18KB
  192. 16. Ensemble Learning and Random Forests/2. What is Ensemble Learning.srt 17KB
  193. 14. K Nearest Neighbors/5. Implement the KNN algorithm from scratch.srt 17KB
  194. 6. NumPy Data Analysis/3. NumPy Arrays Basics.srt 17KB
  195. 3. Python For Data Science/9. Python Strings.srt 16KB
  196. 15. Decision Trees/12. Decision Trees Hyper-parameters.srt 16KB
  197. 17. Support Vector Machines/2. SVM intuition.srt 16KB
  198. 2. Data Science & Machine Learning Concepts/5. What is Deep Learning.srt 16KB
  199. 1. Introduction/4. Data Science Job Roles.srt 16KB
  200. 3. Python For Data Science/5. Python Variables, Booleans and None.srt 15KB
  201. 15. Decision Trees/10. Visualizing the tree.srt 15KB
  202. 3. Python For Data Science/14. Python Tuples.srt 15KB
  203. 15. Decision Trees/4. The Decision Tree ID3 algorithm from scratch Part 1.srt 15KB
  204. 14. K Nearest Neighbors/7. Hyperparameter tuning using the cross-validation.srt 15KB
  205. 19. PCA/2. What is PCA.srt 15KB
  206. 5. Probability & Hypothesis Testing/4. Hypothesis Testing Overview.srt 15KB
  207. 19. PCA/10. PCA - Feature Scaling and Screen Plot.srt 14KB
  208. 6. NumPy Data Analysis/4. NumPy Array Indexing.srt 14KB
  209. 3. Python For Data Science/16. Python Sets.srt 13KB
  210. 4. Statistics for Data Science/4. Measure of Variability Continued.srt 13KB
  211. 8. Python Data Visualization/3. Python Data Visualization Implementation.srt 12KB
  212. 3. Python For Data Science/6. Getting Started with Google Colab.srt 12KB
  213. 15. Decision Trees/9. Compare with Sklearn implementation.srt 12KB
  214. 13. Linear and Logistic Regression/1. Linear Regression Intro.srt 12KB
  215. 11. Data Cleaning/1. Feature Scaling.srt 12KB
  216. 11. Data Cleaning/2. Data Cleaning.srt 12KB
  217. 4. Statistics for Data Science/1. Intro To Statistics.srt 11KB
  218. 16. Ensemble Learning and Random Forests/3. What is Bootstrap Sampling.srt 11KB
  219. 6. NumPy Data Analysis/2. NumPy Arrays.srt 11KB
  220. 19. PCA/12. PCA - Visualization.srt 11KB
  221. 3. Python For Data Science/11. Python For Loops and While Loops.srt 11KB
  222. 4. Statistics for Data Science/5. Measures of Variable Relationship.srt 11KB
  223. 15. Decision Trees/5. The Decision Tree ID3 algorithm from scratch Part 2.srt 11KB
  224. 15. Decision Trees/15. Decision Trees Pros and Cons.srt 11KB
  225. 20. Data Science Career/1. Creating A Data Science Resume.srt 11KB
  226. 1. Introduction/2. Data Science + Machine Learning Marketplace.srt 10KB
  227. 4. Statistics for Data Science/8. Sampling Distribution.srt 10KB
  228. 4. Statistics for Data Science/2. Descriptive Statistics.srt 10KB
  229. 16. Ensemble Learning and Random Forests/5. Out-of-Bag Error (OOB Error).srt 10KB
  230. 3. Python For Data Science/8. Python Numbers & Booleans.srt 10KB
  231. 14. K Nearest Neighbors/11. Curse of dimensionality.srt 10KB
  232. 12. Feature Selecting and Engineering/1. Feature Engineering.srt 9KB
  233. 3. Python For Data Science/1. What is Programming.srt 9KB
  234. 8. Python Data Visualization/2. Different Data Visualization Libraries in Python.srt 9KB
  235. 5. Probability & Hypothesis Testing/3. Relative Frequency.srt 9KB
  236. 6. NumPy Data Analysis/5. NumPy Array Computations.srt 9KB
  237. 13. Linear and Logistic Regression/2. Gradient Descent.srt 8KB
  238. 20. Data Science Career/5. Top Freelance Websites.srt 8KB
  239. 16. Ensemble Learning and Random Forests/7. Implementing Random Forests from scratch Part 2.srt 8KB
  240. 14. K Nearest Neighbors/10. Feature scaling in KNN.srt 8KB
  241. 17. Support Vector Machines/9. SVM with Regression.srt 8KB
  242. 16. Ensemble Learning and Random Forests/10. Random Forests Pros and Cons.srt 8KB
  243. 14. K Nearest Neighbors/13. KNN pros and cons.srt 8KB
  244. 14. K Nearest Neighbors/9. Manhattan vs Euclidean Distance.srt 8KB
  245. 15. Decision Trees/11. Plot the features importance.srt 8KB
  246. 16. Ensemble Learning and Random Forests/4. What is Bagging.srt 8KB
  247. 17. Support Vector Machines/1. SVM Outline.srt 7KB
  248. 20. Data Science Career/3. How to Contact Recruiters.srt 7KB
  249. 19. PCA/11. PCA - Supervised vs Unsupervised.srt 7KB
  250. 3. Python For Data Science/12. Python Lists.srt 7KB
  251. 20. Data Science Career/4. Getting Started with Freelancing.srt 7KB
  252. 14. K Nearest Neighbors/8. The decision boundary visualization.srt 7KB
  253. 19. PCA/1. PCA Section Overview.srt 7KB
  254. 13. Linear and Logistic Regression/4. Linear Regression Implementation.srt 7KB
  255. 1. Introduction/3. Data Science Job Opportunities.srt 7KB
  256. 16. Ensemble Learning and Random Forests/11. What is Boosting.srt 7KB
  257. 3. Python For Data Science/2. Why Python for Data Science.srt 7KB
  258. 5. Probability & Hypothesis Testing/1. What is Exactly is Probability.srt 7KB
  259. 19. PCA/5. Covariance Matrix vs SVD.srt 7KB
  260. 20. Data Science Career/6. Personal Branding.srt 6KB
  261. 6. NumPy Data Analysis/6. Broadcasting.srt 6KB
  262. 20. Data Science Career/7. Networking Do's and Don'ts.srt 6KB
  263. 17. Support Vector Machines/10. SMV - Project Overview.srt 6KB
  264. 20. Data Science Career/2. Data Science Cover Letter.srt 6KB
  265. 16. Ensemble Learning and Random Forests/9. Random Forests Hyper-Parameters.srt 6KB
  266. 3. Python For Data Science/3. What is Jupyter.srt 6KB
  267. 15. Decision Trees/6. The Decision Tree ID3 algorithm from scratch Part 3.srt 6KB
  268. 17. Support Vector Machines/4. C hyper-parameter.srt 6KB
  269. 15. Decision Trees/1. Decision Trees Section Overview.srt 6KB
  270. 16. Ensemble Learning and Random Forests/12. AdaBoost Part 1.srt 5KB
  271. 16. Ensemble Learning and Random Forests/1. Ensemble Learning Section Overview.srt 5KB
  272. 14. K Nearest Neighbors/6. Compare the result with the sklearn library.srt 5KB
  273. 16. Ensemble Learning and Random Forests/8. Compare with sklearn implementation.srt 5KB
  274. 13. Linear and Logistic Regression/5. Logistic Regression.srt 5KB
  275. 2. Data Science & Machine Learning Concepts/1. Why We Use Python.srt 5KB
  276. 19. PCA/3. PCA Drawbacks.srt 5KB
  277. 3. Python For Data Science/4. What is Google Colab.srt 5KB
  278. 14. K Nearest Neighbors/12. KNN use cases.srt 5KB
  279. 20. Data Science Career/8. Importance of a Website.srt 5KB
  280. 14. K Nearest Neighbors/2. parametric vs non-parametric models.srt 5KB
  281. 14. K Nearest Neighbors/1. KNN Overview.srt 4KB
  282. 5. Probability & Hypothesis Testing/2. Expected Values.srt 4KB
  283. 1. Introduction/1. Who is This Course For.srt 4KB
  284. 19. PCA/6. PCA - Main Applications.srt 4KB
  285. 15. Decision Trees/14. [Optional] Gain Ration.srt 4KB
  286. 15. Decision Trees/16. [Project] Predict whether income exceeds $50Kyr - Overview.srt 4KB
  287. 14. K Nearest Neighbors/4. The KNN Intuition.srt 3KB
  288. 4. Statistics for Data Science/7. Measure of Asymmetry.srt 3KB
  289. 0. Websites you may like/[CourseClub.ME].url 122B
  290. 16. Ensemble Learning and Random Forests/[CourseClub.Me].url 122B
  291. 3. Python For Data Science/[CourseClub.Me].url 122B
  292. 9. Machine Learning/[CourseClub.Me].url 122B
  293. [CourseClub.Me].url 122B
  294. 0. Websites you may like/[GigaCourse.Com].url 49B
  295. 16. Ensemble Learning and Random Forests/[GigaCourse.Com].url 49B
  296. 3. Python For Data Science/[GigaCourse.Com].url 49B
  297. 9. Machine Learning/[GigaCourse.Com].url 49B
  298. [GigaCourse.Com].url 49B