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Udemy MACHINE LEARNING MASTER CLASS AI MADE EASY Zero to Hero
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Files:
  1. 2. Multiple linear regression behind the scene - Part 1.mp4 160.3 MB
  2. TutsNode.com.txt 63 bytes
  3. 2. Polynomial regression on multiple feature dataset.srt 28.0 KB
  4. 4. The log scale.srt 26.2 KB
  5. 5. Seaborn plots.srt 26.2 KB
  6. 6. Range, enumerate and zip.srt 25.8 KB
  7. 2. DataFrame introduction.srt 25.5 KB
  8. 3. Matplotlib Subplot and histogram.srt 25.4 KB
  9. 2. Updated template with GridSearchCV.srt 24.2 KB
  10. 2. Scatter plot on Iris dataset.srt 23.3 KB
  11. 1. Polynomial regression.srt 23.0 KB
  12. 5. Meet your Author.srt 2.5 KB
  13. 6. Linkedin and Instagram links.html 511 bytes
  14. 1. Master template regression model - Data creation.srt 22.9 KB
  15. 1. Bayes theorem.srt 22.2 KB
  16. 2. Linear regression implementation in python - Part 1.srt 22.0 KB
  17. 5. BeginsWith endsWith and dot character.srt 21.9 KB
  18. 1. Measuring Entropy & Gini impurity.srt 21.3 KB
  19. 3. ROC, AUC - Calculating the optimal threshold (Youdens method).srt 21.0 KB
  20. 2. Multiple linear regression behind the scene - Part 1.srt 21.0 KB
  21. 5. Maps, Filters and Lambdas.srt 20.8 KB
  22. 5. Gaussian naive bayes.srt 20.8 KB
  23. 4. Test and train data split and Feature scaling.srt 20.8 KB
  24. 7. Assignment solution and OneHotEncoding - Part 01.srt 20.2 KB
  25. 7. CAP curve with multiple models and multi-class.srt 20.1 KB
  26. 2. User defined packages.srt 20.1 KB
  27. 4. Multiple, multi level inheritance and MRO.srt 19.9 KB
  28. 2. Gradient decent - Background.srt 19.7 KB
  29. 5. Pre-processing re-visited.srt 19.5 KB
  30. 1. Why Co-relation is important.srt 19.2 KB
  31. 5. Matrices selection and conditional selection.srt 19.2 KB
  32. 8. Assignment solution and OneHotEncoding - Part 02.srt 19.2 KB
  33. 3. K Fold cross validation without GridSearchCV.srt 19.2 KB
  34. 3. Read mode, write mode and methods.srt 19.1 KB
  35. 1. Voting classifier.srt 19.1 KB
  36. 2. Boosting.srt 18.8 KB
  37. 3. DataFrame Selections.srt 18.7 KB
  38. 1. Python random class.srt 18.6 KB
  39. 1. Euler's number.srt 18.1 KB
  40. 1. KNN background.srt 18.1 KB
  41. 1. Python decorators.srt 17.7 KB
  42. 8. Boxplot and Violin Plot.srt 17.6 KB
  43. 2. Random under numpy and Arange.srt 17.6 KB
  44. 6. Special class methods.srt 17.5 KB
  45. 2. Co-variance.srt 17.5 KB
  46. 3. Python generators.srt 17.5 KB
  47. 1. R-square.srt 17.5 KB
  48. 3. Multiple linear regression behind the scene - Part 2.srt 17.4 KB
  49. 1. SVM getting started with 1D data.srt 17.3 KB
  50. 3. Python collections.srt 17.1 KB
  51. 5. Under and over sampling.srt 17.0 KB
  52. 7. Feature selection.srt 17.0 KB
  53. 3. Multinomial naive bayes.srt 16.9 KB
  54. 2. Error types, else and finally.srt 16.8 KB
  55. 5. String Start Stop and Step.srt 16.4 KB
  56. 3. Scopes.srt 16.2 KB
  57. 4. Slicing and broadcast.srt 16.2 KB
  58. 2. Paths.srt 16.0 KB
  59. 3. Visualization of decision tree model.srt 15.9 KB
  60. 7. Percentiles, moment and Quantiles.srt 15.8 KB
  61. 1. User-defined functions.srt 15.8 KB
  62. 2. NumPy array functions - Array generate.srt 15.8 KB
  63. 1. Regular expression introduction.srt 15.6 KB
  64. 2. Decision Tree implementation with 1 feature.srt 15.6 KB
  65. 2. SVM, mapping higher dimension.srt 15.6 KB
  66. 4. Vector Multiplication.srt 15.5 KB
  67. 4. Confusion matrix 3D.srt 15.3 KB
  68. 2. Logistic regression background.srt 15.1 KB
  69. 2. Jupyter notebook.srt 15.1 KB
  70. 5. Break, continue and pass.srt 15.0 KB
  71. 4. Greedy, non-greedy matches and findall.srt 15.0 KB
  72. 1. Naming conventions and introduction.srt 14.8 KB
  73. 6. Gaussian naive Bayes under Python & Visualization of models.srt 14.8 KB
  74. 10. Sets.srt 14.8 KB
  75. 3. Accuracy, precision, recall, Specificity, F1 Score.srt 14.8 KB
  76. 5. Standard deviation.srt 14.8 KB
  77. 2. handling missing data.srt 14.7 KB
  78. 5. Concatenation.srt 14.5 KB
  79. 5. CAP curve background.srt 14.3 KB
  80. 15. Logical operators.srt 14.2 KB
  81. 3. Random array based methods.srt 14.1 KB
  82. 2. Regular expression, grouping and pipe.srt 14.1 KB
  83. 1. Why Logistic regression.srt 14.1 KB
  84. 1. Introduction to ML & Supervised learning.srt 14.0 KB
  85. 2. Matplotlib Bar-graph and multiple plotting.srt 14.0 KB
  86. 4. Python counter from collections.srt 14.0 KB
  87. 7. Univariate Analysis using PDF.srt 14.0 KB
  88. 1. If ElIf & else.srt 14.0 KB
  89. 3. Co-relation.srt 13.8 KB
  90. 1. Updated template with GridSearchCV.srt 13.8 KB
  91. 1. Linear regression working and Cost function.srt 13.6 KB
  92. 1. Matplotlib simple plot, line graphs.srt 13.6 KB
  93. 3. Pair plot and limitations.srt 13.4 KB
  94. 1. The accuracy, not so accurate.srt 13.4 KB
  95. 1. Panda series.srt 13.3 KB
  96. 3. Gradient decent in 2D and 3D space.srt 13.1 KB
  97. 5. Polymorphism.srt 13.0 KB
  98. 6. CAP curve implementation.srt 13.0 KB
  99. 3. Repetition and range.srt 13.0 KB
  100. 6. Matpotlib Wireframe surface plotting.srt 12.9 KB
  101. 6. Lambda once again.srt 12.9 KB
  102. 9. List shorting, reversing, removing, clear, list of list.srt 12.9 KB
  103. 1. Bagging.srt 12.8 KB
  104. 1. Multiple linear regression in Python.srt 12.7 KB
  105. 3. Inheritance.srt 12.6 KB
  106. 4. ROC, AUC - Calculating the optimal threshold (best Accuracy method).srt 12.6 KB
  107. 4. args and kwargs.srt 12.5 KB
  108. 9. Discussion forum.srt 3.8 KB
  109. 0 61 bytes
  110. 2. Scatter plot on Iris dataset.mp4 153.4 MB
  111. 6. Pre-processing re-visited continues.srt 12.5 KB
  112. 9. HeatMap.srt 12.4 KB
  113. 4. Logistic regression on multi-class classification.srt 12.4 KB
  114. 1. Bias, Variance and overfitting.srt 12.3 KB
  115. 7. Sets.srt 12.2 KB
  116. 2. RandomizedSearchCV.srt 12.1 KB
  117. 4. Matplotlib Scatter plots and Pie charts.srt 12.0 KB
  118. 8. Literal matching, Sub and verbose.srt 12.0 KB
  119. 4. Tuple unpacking.srt 12.0 KB
  120. 2. Class attributes and Methods.srt 11.9 KB
  121. 1. AdaBoost and XGBoost regressor.srt 11.9 KB
  122. 2. Classification model master template with evaluation and different data set.srt 11.9 KB
  123. 1. Setting up.srt 11.6 KB
  124. 3. SVM, in 2D space.srt 11.6 KB
  125. 2. Class method decorator.srt 11.5 KB
  126. 1. AdaBoost and XGBoost classifier.srt 11.4 KB
  127. 6. Facetgrid plots.srt 11.4 KB
  128. 1. Python packages.srt 11.1 KB
  129. 2. While loop.srt 11.1 KB
  130. 6. Most common data distributions, PDF and PMF.srt 11.0 KB
  131. 7. In.srt 11.0 KB
  132. 2. Likelihood vs probability.srt 10.9 KB
  133. 5. Matplotlib 3D scatter and simple plot.srt 10.8 KB
  134. 1. Try except finally.srt 10.8 KB
  135. 3. For loop.srt 10.7 KB
  136. 1. Data import.srt 10.6 KB
  137. 6. Operations.srt 10.4 KB
  138. 4. SVM implementation using python.srt 10.4 KB
  139. 1. Data types.srt 10.3 KB
  140. 3. Feature selection and Encoding categorical data.srt 10.2 KB
  141. 4. Decision Tree implementation - multiple features.srt 10.2 KB
  142. 1. Matrices.srt 10.2 KB
  143. 4. GroupBy.srt 10.2 KB
  144. 4. K Fold cross validation without GridSearchCV continues.srt 10.2 KB
  145. 2. Random Forest.srt 10.1 KB
  146. 2. Python numbers.srt 10.1 KB
  147. 4. String basics.srt 10.0 KB
  148. 8. Lists in Python.srt 10.0 KB
  149. 1. Ensemble Learning.srt 9.9 KB
  150. 2. Confusion matrix.srt 9.7 KB
  151. 8. Input and import.srt 9.7 KB
  152. 2. ROC, AUC - Evaluating best model.srt 9.7 KB
  153. 4. Curse of dimensionality.srt 9.7 KB
  154. 7. String formatting.srt 9.6 KB
  155. 14. Comparison operators.srt 9.6 KB
  156. 12. Dictionary in python.srt 9.5 KB
  157. 4. Update Anaconda website updated.srt 9.3 KB
  158. 3. Visualization and few more things.srt 9.3 KB
  159. 1. Python setting up.srt 9.3 KB
  160. 2. Unsupervised learning.srt 9.2 KB
  161. 1. Model deployment basics.srt 9.2 KB
  162. 2. Prediction using value.srt 9.2 KB
  163. 3. Variables and assignment.srt 9.1 KB
  164. 1. Thanks for taking this course.srt 1.8 KB
  165. [TGx]Downloaded from torrentgalaxy.to .txt 585 bytes
  166. 1 506 bytes
  167. 2. User defined packages.mp4 144.4 MB
  168. 3. Type of data.srt 9.1 KB
  169. 6. Numpy operations.srt 9.1 KB
  170. 5. Identity matrix, matrix inverse properties, transpose of matrix.srt 9.0 KB
  171. 2. KNN in python.srt 8.9 KB
  172. 5. Math Matrix multiplication.srt 8.8 KB
  173. 1. Classification model master template.srt 8.8 KB
  174. 3. Pycharm python IDE.srt 8.8 KB
  175. 3. Matrix multiplication.srt 8.5 KB
  176. 5. KNN on multi class classification.srt 8.5 KB
  177. 1. Decision Tree and Random forest.srt 8.5 KB
  178. 6. BeginsWith endsWith and dot character continues.srt 8.5 KB
  179. 1. K Fold cross validation.srt 8.4 KB
  180. 11. Tuples.srt 8.3 KB
  181. 2. Balanced vs imbalanced data.srt 8.3 KB
  182. 4. LabelEncoding classes.srt 8.2 KB
  183. 1. SVM (regression) Background.srt 7.9 KB
  184. 3. Linear regression implementation in python - Part 2.srt 7.8 KB
  185. 2. Adjusted R-Square.srt 7.4 KB
  186. 2. Help function.srt 7.3 KB
  187. 3. Logistic regression under python.srt 6.8 KB
  188. 4. Mean Mode median.srt 6.7 KB
  189. 3. User defined packages continues.srt 6.6 KB
  190. 13. None and Bool.srt 6.5 KB
  191. 6. String slicing.srt 5.9 KB
  192. 2. Matrix operations and scalar operations.srt 5.8 KB
  193. 1. Autocomplete on jupyter notebook.srt 5.8 KB
  194. 1. Files introduction.srt 4.8 KB
  195. 6. Assignment and tips.srt 4.5 KB
  196. 4. Tips dataset.srt 4.2 KB
  197. 16. Connect on LinkedIn, It's good!.srt 4.1 KB
  198. 2. SVR under Python.srt 4.0 KB
  199. 2. Master template regression model - Models and evaluation.srt 3.9 KB
  200. 8. Short discussion.srt 3.7 KB
  201. 5. Logistic regression on multi-class classification under python.srt 3.7 KB
  202. 7. About Project files.srt 3.2 KB
  203. 2 1.3 MB
  204. 1. Polynomial regression.mp4 143.8 MB
  205. 3 187.9 KB
  206. 2. Updated template with GridSearchCV.mp4 143.3 MB
  207. 4 701.2 KB
  208. 7. CAP curve with multiple models and multi-class.mp4 135.7 MB
  209. 5 335.0 KB
  210. 1. Master template regression model - Data creation.mp4 134.7 MB
  211. 6 1.3 MB
  212. 1. ROC, AUC and PR curve background.mp4 131.4 MB
  213. 7 571.6 KB
  214. 8. Assignment solution and OneHotEncoding - Part 02.mp4 126.2 MB
  215. 8 1.8 MB
  216. 3. ROC, AUC - Calculating the optimal threshold (Youdens method).mp4 124.4 MB
  217. 9 1.6 MB
  218. 2. Polynomial regression on multiple feature dataset.mp4 119.3 MB
  219. 10 748.1 KB
  220. 2. RandomizedSearchCV.mp4 115.4 MB
  221. 11 608.7 KB
  222. 1. Voting classifier.mp4 114.7 MB
  223. 12 1.3 MB
  224. 7. Assignment solution and OneHotEncoding - Part 01.mp4 113.2 MB
  225. 13 809.1 KB
  226. 1. Why Co-relation is important.mp4 110.5 MB
  227. 14 1.5 MB
  228. 5. Pre-processing re-visited.mp4 110.4 MB
  229. 15 1.6 MB
  230. 1. Updated template with GridSearchCV.mp4 109.0 MB
  231. 16 1022.8 KB
  232. 7. Feature selection.mp4 106.1 MB
  233. 17 1.9 MB
  234. 2. DataFrame introduction.mp4 98.1 MB
  235. 18 1.9 MB
  236. 4. Test and train data split and Feature scaling.mp4 97.9 MB
  237. 19 54.2 KB
  238. 3. Read mode, write mode and methods.mp4 97.1 MB
  239. 20 958.6 KB
  240. 2. Jupyter notebook.mp4 95.4 MB
  241. 21 584.4 KB
  242. 5. Seaborn plots.mp4 95.3 MB
  243. 22 725.4 KB
  244. 2. Linear regression implementation in python - Part 1.mp4 92.5 MB
  245. 23 1.5 MB
  246. 3. K Fold cross validation without GridSearchCV.mp4 91.9 MB
  247. 24 89.6 KB
  248. 3. Visualization of decision tree model.mp4 89.3 MB
  249. 25 726.7 KB
  250. 7. Percentiles, moment and Quantiles.mp4 88.8 MB
  251. 26 1.2 MB
  252. 6. Gaussian naive Bayes under Python & Visualization of models.mp4 88.5 MB
  253. 27 1.5 MB
  254. 5. Under and over sampling.mp4 87.6 MB
  255. 28 368.9 KB
  256. 5. BeginsWith endsWith and dot character.mp4 86.9 MB
  257. 29 1.1 MB
  258. 1. Python packages.mp4 86.8 MB
  259. 30 1.2 MB
  260. 2. Error types, else and finally.mp4 86.3 MB
  261. 31 1.7 MB
  262. 3. Gradient decent in 2D and 3D space.mp4 85.1 MB
  263. 32 879.8 KB
  264. 4. The log scale.mp4 83.6 MB
  265. 33 365.6 KB
  266. 4. Vector Multiplication.mp4 82.9 MB
  267. 34 1.1 MB
  268. 3. Matplotlib Subplot and histogram.mp4 82.5 MB
  269. 35 1.5 MB
  270. 1. Measuring Entropy & Gini impurity.mp4 81.9 MB
  271. 36 59.3 KB
  272. 4. Multiple, multi level inheritance and MRO.mp4 79.8 MB
  273. 37 238.9 KB
  274. 5. Gaussian naive bayes.mp4 77.4 MB
  275. 38 583.7 KB
  276. 2. Random under numpy and Arange.mp4 77.1 MB
  277. 39 934.4 KB
  278. 1. Python setting up.mp4 76.7 MB
  279. 40 1.3 MB
  280. 3. Python generators.mp4 76.1 MB
  281. 41 1.9 MB
  282. 3. DataFrame Selections.mp4 75.7 MB
  283. 42 355.0 KB
  284. 2. Classification model master template with evaluation and different data set.mp4 75.2 MB
  285. 43 869.1 KB
  286. 3. Multiple linear regression behind the scene - Part 2.mp4 75.1 MB
  287. 44 922.4 KB
  288. 6. Range, enumerate and zip.mp4 75.0 MB
  289. 45 1.0 MB
  290. 4. Confusion matrix 3D.mp4 75.0 MB
  291. 46 1.0 MB
  292. 1. Bayes theorem.mp4 73.8 MB
  293. 47 216.1 KB
  294. 1. Python decorators.mp4 72.8 MB
  295. 48 1.2 MB
  296. 1. Regular expression introduction.mp4 72.5 MB
  297. 49 1.5 MB
  298. 5. Concatenation.mp4 72.3 MB
  299. 50 1.7 MB
  300. 3. Repetition and range.mp4 71.6 MB
  301. 51 456.4 KB
  302. 2. handling missing data.mp4 71.5 MB
  303. 52 465.7 KB
  304. 6. Pre-processing re-visited continues.mp4 71.2 MB
  305. 53 813.6 KB
  306. 16. Connect on LinkedIn, It's good!.mp4 71.0 MB
  307. 54 982.7 KB
  308. 1. Data import.mp4 71.0 MB
  309. 55 983.4 KB
  310. 1. Python random class.mp4 70.6 MB
  311. 56 1.4 MB
  312. 1. Multiple linear regression in Python.mp4 69.6 MB
  313. 57 397.4 KB
  314. 4. ROC, AUC - Calculating the optimal threshold (best Accuracy method).mp4 69.3 MB
  315. 58 674.3 KB
  316. 2. Matplotlib Bar-graph and multiple plotting.mp4 68.6 MB
  317. 59 1.4 MB
  318. 4. K Fold cross validation without GridSearchCV continues.mp4 68.4 MB
  319. 60 1.6 MB
  320. 3. Pair plot and limitations.mp4 67.8 MB
  321. 61 174.2 KB
  322. 1. AdaBoost and XGBoost classifier.mp4 67.7 MB
  323. 62 348.5 KB
  324. 5. Maps, Filters and Lambdas.mp4 67.6 MB
  325. 63 456.0 KB
  326. 6. CAP curve implementation.mp4 67.0 MB
  327. 64 1.0 MB
  328. 1. AdaBoost and XGBoost regressor.mp4 67.0 MB
  329. 65 1.0 MB
  330. 3. Accuracy, precision, recall, Specificity, F1 Score.mp4 66.1 MB
  331. 66 1.9 MB
  332. 6. Special class methods.mp4 65.8 MB
  333. 67 193.3 KB
  334. 3. Multinomial naive bayes.mp4 65.0 MB
  335. 68 1017.4 KB
  336. 4. SVM implementation using python.mp4 64.2 MB
  337. 69 1.8 MB
  338. 3. Python collections.mp4 64.0 MB
  339. 70 2.0 MB
  340. 2. Boosting.mp4 63.9 MB
  341. 71 75.4 KB
  342. 2. Paths.mp4 62.8 MB
  343. 72 1.2 MB
  344. 1. KNN background.mp4 62.3 MB
  345. 73 1.7 MB
  346. 9. Discussion forum.mp4 61.6 MB
  347. 74 393.0 KB
  348. 4. Greedy, non-greedy matches and findall.mp4 61.5 MB
  349. 75 562.0 KB
  350. 2. ROC, AUC - Evaluating best model.mp4 61.1 MB
  351. 76 905.2 KB
  352. 5. String Start Stop and Step.mp4 61.0 MB
  353. 77 1006.2 KB
  354. 3. Scopes.mp4 61.0 MB
  355. 78 1.0 MB
  356. 2. Gradient decent - Background.mp4 60.5 MB
  357. 79 1.5 MB
  358. 3. Pycharm python IDE.mp4 60.5 MB
  359. 80 1.5 MB
  360. 3. Visualization and few more things.mp4 59.8 MB
  361. 81 182.1 KB
  362. 1. Decision Tree and Random forest.mp4 59.5 MB
  363. 82 524.5 KB
  364. 1. Classification model master template.mp4 57.9 MB
  365. 83 104.8 KB
  366. 7. About Project files.mp4 57.7 MB
  367. 84 356.6 KB
  368. 3. User defined packages continues.mp4 57.6 MB
  369. 85 421.8 KB
  370. 3. Random array based methods.mp4 57.4 MB
  371. 86 617.1 KB
  372. 3. Co-relation.mp4 57.4 MB
  373. 87 623.2 KB
  374. 2. Co-variance.mp4 57.3 MB
  375. 88 675.3 KB
  376. 1. SVM getting started with 1D data.mp4 57.3 MB
  377. 89 710.4 KB
  378. 7. Univariate Analysis using PDF.mp4 57.3 MB
  379. 90 729.8 KB
  380. 6. Matpotlib Wireframe surface plotting.mp4 57.0 MB
  381. 91 1000.1 KB
  382. 4. Update Anaconda website updated.mp4 56.0 MB
  383. 92 2.0 MB
  384. 1. Matplotlib simple plot, line graphs.mp4 54.6 MB
  385. 93 1.4 MB
  386. 10. Sets.mp4 54.5 MB
  387. 94 1.5 MB
  388. 5. Matplotlib 3D scatter and simple plot.mp4 54.5 MB
  389. 95 1.5 MB
  390. 5. Matrices selection and conditional selection.mp4 54.4 MB
  391. 96 1.6 MB
  392. 2. Prediction using value.mp4 54.3 MB
  393. 97 1.7 MB
  394. 4. Python counter from collections.mp4 54.2 MB
  395. 98 1.8 MB
  396. 1. Data types.mp4 54.0 MB
  397. 99 2.0 MB
  398. 5. CAP curve background.mp4 53.9 MB
  399. 100 102.0 KB
  400. 2. Decision Tree implementation with 1 feature.mp4 53.8 MB
  401. 101 200.5 KB
  402. 4. Decision Tree implementation - multiple features.mp4 53.7 MB
  403. 102 259.1 KB
  404. 8. Boxplot and Violin Plot.mp4 53.1 MB
  405. 103 952.5 KB
  406. 2. Class method decorator.mp4 52.8 MB
  407. 104 1.2 MB
  408. 1. Euler's number.mp4 52.7 MB
  409. 105 1.3 MB
  410. 2. Random Forest.mp4 52.6 MB
  411. 106 1.4 MB
  412. 1. Ensemble Learning.mp4 52.6 MB
  413. 107 1.4 MB
  414. 2. Unsupervised learning.mp4 52.5 MB
  415. 108 1.5 MB
  416. 2. Likelihood vs probability.mp4 52.3 MB
  417. 109 1.7 MB
  418. 2. Logistic regression background.mp4 52.0 MB
  419. 110 2.0 MB
  420. 1. Naming conventions and introduction.mp4 52.0 MB
  421. 111 28.5 KB
  422. 1. R-square.mp4 51.7 MB
  423. 112 347.9 KB
  424. 6. Most common data distributions, PDF and PMF.mp4 51.4 MB
  425. 113 569.4 KB
  426. 1. Model deployment basics.mp4 51.4 MB
  427. 114 622.9 KB
  428. 1. Why Logistic regression.mp4 51.0 MB
  429. 115 999.5 KB
  430. 4. args and kwargs.mp4 51.0 MB
  431. 116 1.0 MB
  432. 6. Lambda once again.mp4 49.7 MB
  433. 117 331.0 KB
  434. 2. KNN in python.mp4 48.7 MB
  435. 118 1.3 MB
  436. 4. Matplotlib Scatter plots and Pie charts.mp4 48.5 MB
  437. 119 1.5 MB
  438. 3. Feature selection and Encoding categorical data.mp4 48.2 MB
  439. 120 1.8 MB
  440. 2. Regular expression, grouping and pipe.mp4 48.2 MB
  441. 121 1.8 MB
  442. 15. Logical operators.mp4 47.9 MB
  443. 122 118.0 KB
  444. 2. SVM, mapping higher dimension.mp4 47.8 MB
  445. 123 205.7 KB
  446. 1. User-defined functions.mp4 47.5 MB
  447. 124 544.7 KB
  448. 4. LabelEncoding classes.mp4 47.2 MB
  449. 125 863.7 KB
  450. 1. Introduction to ML & Supervised learning.mp4 46.9 MB
  451. 126 1.1 MB
  452. 1. Bagging.mp4 46.1 MB
  453. 127 1.9 MB
  454. 1. The accuracy, not so accurate.mp4 46.0 MB
  455. 128 2.0 MB
  456. 1. If ElIf & else.mp4 45.3 MB
  457. 129 702.5 KB
  458. 1. Setting up.mp4 45.0 MB
  459. 130 1.0 MB
  460. 6. Facetgrid plots.mp4 44.7 MB
  461. 131 1.3 MB
  462. 2. NumPy array functions - Array generate.mp4 44.6 MB
  463. 132 1.4 MB
  464. 3. SVM, in 2D space.mp4 44.6 MB
  465. 133 1.4 MB
  466. 5. KNN on multi class classification.mp4 44.3 MB
  467. 134 1.7 MB
  468. 4. Slicing and broadcast.mp4 44.1 MB
  469. 135 1.9 MB
  470. 9. List shorting, reversing, removing, clear, list of list.mp4 44.1 MB
  471. 136 1.9 MB
  472. 1. Try except finally.mp4 43.1 MB
  473. 137 953.9 KB
  474. 4. Logistic regression on multi-class classification.mp4 42.8 MB
  475. 138 1.2 MB
  476. 9. HeatMap.mp4 42.8 MB
  477. 139 1.2 MB
  478. 1. Panda series.mp4 42.3 MB
  479. 140 1.7 MB
  480. 5. Meet your Author.mp4 42.1 MB
  481. 141 1.9 MB
  482. 1. Bias, Variance and overfitting.mp4 42.1 MB
  483. 142 1.9 MB
  484. 3. Inheritance.mp4 42.0 MB
  485. 143 14.6 KB
  486. 3. Logistic regression under python.mp4 41.7 MB
  487. 144 323.1 KB
  488. 2. Class attributes and Methods.mp4 41.2 MB
  489. 145 826.4 KB
  490. 5. Polymorphism.mp4 41.1 MB
  491. 146 884.9 KB
  492. 6. Numpy operations.mp4 40.7 MB
  493. 147 1.3 MB
  494. 8. Literal matching, Sub and verbose.mp4 39.9 MB
  495. 148 138.0 KB
  496. 1. Autocomplete on jupyter notebook.mp4 38.7 MB
  497. 149 1.3 MB
  498. 7. String formatting.mp4 38.5 MB
  499. 150 1.5 MB
  500. 2. Confusion matrix.mp4 38.2 MB
  501. 151 1.8 MB
  502. 1. Linear regression working and Cost function.mp4 37.6 MB
  503. 152 412.2 KB
  504. 7. Sets.mp4 37.5 MB
  505. 153 506.6 KB
  506. 5. Break, continue and pass.mp4 37.3 MB
  507. 154 680.0 KB
  508. 4. GroupBy.mp4 37.3 MB
  509. 155 732.7 KB
  510. 4. String basics.mp4 35.3 MB
  511. 156 725.9 KB
  512. 3. Linear regression implementation in python - Part 2.mp4 35.3 MB
  513. 157 747.9 KB
  514. 3. For loop.mp4 35.0 MB
  515. 158 1023.2 KB
  516. 12. Dictionary in python.mp4 34.2 MB
  517. 159 1.8 MB
  518. 4. Curse of dimensionality.mp4 33.8 MB
  519. 160 194.8 KB
  520. 8. Lists in Python.mp4 33.4 MB
  521. 161 660.4 KB
  522. 6. Operations.mp4 33.0 MB
  523. 162 1.0 MB
  524. 5. Standard deviation.mp4 32.8 MB
  525. 163 1.2 MB
  526. 6. Assignment and tips.mp4 32.6 MB
  527. 164 1.4 MB
  528. 2. While loop.mp4 32.5 MB
  529. 165 1.5 MB
  530. 3. Variables and assignment.mp4 31.8 MB
  531. 166 193.2 KB
  532. 4. Tuple unpacking.mp4 31.3 MB
  533. 167 741.6 KB
  534. 1. Thanks for taking this course.mp4 29.8 MB
  535. 168 239.7 KB
  536. 5. Logistic regression on multi-class classification under python.mp4 29.5 MB
  537. 169 480.1 KB
  538. 2. Python numbers.mp4 28.9 MB
  539. 170 1.1 MB
  540. 5. Identity matrix, matrix inverse properties, transpose of matrix.mp4 28.8 MB
  541. 171 1.2 MB
  542. 6. BeginsWith endsWith and dot character continues.mp4 28.7 MB
  543. 172 1.3 MB
  544. 7. In.mp4 28.5 MB
  545. 173 1.5 MB
  546. 14. Comparison operators.mp4 28.3 MB
  547. 174 1.7 MB
  548. 2. Balanced vs imbalanced data.mp4 28.1 MB
  549. 175 1.9 MB
  550. 8. Input and import.mp4 28.0 MB
  551. 176 3.0 KB
  552. 1. SVM (regression) Background.mp4 26.9 MB
  553. 177 1.1 MB
  554. 11. Tuples.mp4 26.7 MB
  555. 178 1.3 MB
  556. 8. Short discussion.mp4 26.3 MB
  557. 179 1.7 MB
  558. 4. Tips dataset.mp4 26.1 MB
  559. 180 1.9 MB
  560. 5. Math Matrix multiplication.mp4 24.1 MB
  561. 181 1.9 MB
  562. 3. Type of data.mp4 23.8 MB
  563. 182 249.4 KB
  564. 2. Help function.mp4 23.6 MB
  565. 183 421.5 KB
  566. 3. Matrix multiplication.mp4 23.4 MB
  567. 184 628.6 KB
  568. 1. Matrices.mp4 23.3 MB
  569. 185 701.2 KB
  570. 2. Master template regression model - Models and evaluation.mp4 22.0 MB
  571. 186 24.4 KB
  572. 2. SVR under Python.mp4 21.7 MB
  573. 187 273.4 KB
  574. 13. None and Bool.mp4 21.7 MB
  575. 188 343.8 KB
  576. 2. Adjusted R-Square.mp4 21.6 MB
  577. 189 446.1 KB
  578. 6. String slicing.mp4 20.0 MB
  579. 190 2.0 MB
  580. 1. K Fold cross validation.mp4 20.0 MB
  581. 191 2.0 MB
  582. 1. Files introduction.mp4 16.8 MB
  583. 192 1.2 MB
  584. 4. Mean Mode median.mp4 16.0 MB
  585. 193 42.2 KB
  586. 2. Matrix operations and scalar operations.mp4 14.0 MB

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