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[UdemyCourseDownloader] Machine Learning A-Z Become Kaggle Master

  • 收录时间:2019-05-09 13:42:25
  • 文件大小:14GB
  • 下载次数:51
  • 最近下载:2020-09-29 14:18:31
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文件列表

  1. 17. Logistic Regression/4. Case Study.mp4 198MB
  2. 7. Data Visualisation/2. Seaborn.mp4 185MB
  3. 15. Model Selection Part1/4. Gridsearch Case study Part2.mp4 179MB
  4. 7. Data Visualisation/1. Matplotlib.mp4 173MB
  5. 2. Numpy/3. Numpy Operations Part2.mp4 170MB
  6. 18. Support Vector Machine (SVM)/14. Case Study 4.mp4 164MB
  7. 4. Some Fun With Maths/1. Linear Algebra Vectors.mp4 162MB
  8. 23. Dimension Reduction/1. Introduction.mp4 157MB
  9. 20. Ensembling/16. Case Study Part1.mp4 142MB
  10. 9. Simple Linear Regression/7. LR Case Study Part1.mp4 138MB
  11. 26. Project Kaggle/2. Playing With The Data.mp4 137MB
  12. 20. Ensembling/17. Case Study Part2.mp4 137MB
  13. 26. Project Kaggle/17. Building Machine Learning model part2.mp4 135MB
  14. 10. Multiple Linear Regression/9. Case Study Part4.mp4 132MB
  15. 2. Numpy/2. Numpy Operations Part1.mp4 129MB
  16. 19. Decision Tree/9. DT Case Study Part1.mp4 125MB
  17. 15. Model Selection Part1/3. Gridsearch Case study Part1.mp4 124MB
  18. 26. Project Kaggle/16. Building Machine Learning model part1.mp4 124MB
  19. 23. Dimension Reduction/5. Case Study Part2.mp4 123MB
  20. 26. Project Kaggle/5. Train, Test And Cross Validation Split.mp4 116MB
  21. 14. Model Performance Metrics/1. Performance Metrics Part1.mp4 114MB
  22. 7. Data Visualisation/3. Case Study.mp4 113MB
  23. 26. Project Kaggle/3. Translating the Problem In Machine Learning World.mp4 113MB
  24. 1. Python Fundamentals/5. Variables in Python.mp4 110MB
  25. 24. Advanced Machine Learning Algorithms/8. Case Study.mp4 106MB
  26. 1. Python Fundamentals/11. String Part1.mp4 106MB
  27. 21. Model Selection Part2/1. Model Selection Part1.mp4 104MB
  28. 25. Deep Learning/6. Neural Network Playground.mp4 104MB
  29. 10. Multiple Linear Regression/3. Case Study part2.mp4 98MB
  30. 23. Dimension Reduction/2. PCA.mp4 98MB
  31. 26. Project Kaggle/4. Dealing with Text Data.mp4 98MB
  32. 23. Dimension Reduction/3. Maths Behind PCA.mp4 97MB
  33. 22. Unsupervised Learning/9. Case Study Part1.mp4 96MB
  34. 19. Decision Tree/10. DT Case Study Part2.mp4 96MB
  35. 16. Naive Bayes/9. Case Study 1.mp4 95MB
  36. 4. Some Fun With Maths/2. Linear Algebra Matrix Part1.mp4 95MB
  37. 1. Python Fundamentals/1. Introduction to the course.mp4 94MB
  38. 26. Project Kaggle/1. Introduction to the Problem Statement.mp4 93MB
  39. 14. Model Performance Metrics/2. Performance Metrics Part2.mp4 90MB
  40. 1. Python Fundamentals/2. Introduction to Kaggle.mp4 90MB
  41. 18. Support Vector Machine (SVM)/11. Case Study 2.mp4 90MB
  42. 11. HotstarNetflix Real world Case Study for Multiple Linear Regression/4. Building Model Part2.mp4 88MB
  43. 1. Python Fundamentals/14. List Part2.mp4 87MB
  44. 1. Python Fundamentals/10. Functions.mp4 86MB
  45. 26. Project Kaggle/6. Understanding Evaluation Matrix Log Loss.mp4 86MB
  46. 13. KNN/11. Classification Case1.mp4 84MB
  47. 10. Multiple Linear Regression/2. Case Study part1.mp4 83MB
  48. 8. Exploratory Data Analysis/10. Univariate Analysis Part1.mp4 83MB
  49. 1. Python Fundamentals/3. Installation of Python and Anaconda.mp4 82MB
  50. 11. HotstarNetflix Real world Case Study for Multiple Linear Regression/2. Playing With Data.mp4 81MB
  51. 16. Naive Bayes/3. Practical Example from NB with One Column.mp4 81MB
  52. 4. Some Fun With Maths/3. Linear Algebra Matrix Part2.mp4 78MB
  53. 1. Python Fundamentals/9. for while Loop.mp4 78MB
  54. 8. Exploratory Data Analysis/8. Data Cleaning part1.mp4 76MB
  55. 20. Ensembling/18. Case Study Part3.mp4 75MB
  56. 16. Naive Bayes/10. Case Study 2 Part1.mp4 75MB
  57. 18. Support Vector Machine (SVM)/7. SVM Case Study Part1.mp4 74MB
  58. 1. Python Fundamentals/15. List Part3.mp4 74MB
  59. 16. Naive Bayes/1. Introduction to Naive Bayes.mp4 73MB
  60. 20. Ensembling/5. Case study.mp4 73MB
  61. 10. Multiple Linear Regression/7. Case Study Part2.mp4 73MB
  62. 26. Project Kaggle/12. Significance of first categorical column.mp4 72MB
  63. 12. Gradient Descent/8. Gradient Descent case study.mp4 72MB
  64. 20. Ensembling/2. Bagging.mp4 71MB
  65. 18. Support Vector Machine (SVM)/10. Kernel Part2.mp4 71MB
  66. 26. Project Kaggle/9. First Categorical column analysis.mp4 71MB
  67. 13. KNN/10. Case Study.mp4 71MB
  68. 1. Python Fundamentals/20. Comprehentions.mp4 71MB
  69. 10. Multiple Linear Regression/4. Case Study part3.mp4 69MB
  70. 10. Multiple Linear Regression/6. Case Study Part1.mp4 69MB
  71. 26. Project Kaggle/7. Building A Worst Model.mp4 68MB
  72. 1. Python Fundamentals/17. Tuples.mp4 67MB
  73. 26. Project Kaggle/14. Third Categorical column.mp4 67MB
  74. 10. Multiple Linear Regression/8. Case Study Part3.mp4 67MB
  75. 3. Pandas/3. DataFrame.mp4 66MB
  76. 18. Support Vector Machine (SVM)/8. SVM Case Study Part2.mp4 66MB
  77. 18. Support Vector Machine (SVM)/3. Hyperplane Part2.mp4 65MB
  78. 24. Advanced Machine Learning Algorithms/4. Optimal Solution.mp4 65MB
  79. 10. Multiple Linear Regression/11. Case Study Part6 (RFE).mp4 64MB
  80. 1. Python Fundamentals/8. If else Loop.mp4 64MB
  81. 1. Python Fundamentals/16. List Part4.mp4 64MB
  82. 25. Deep Learning/5. Multi Layered Perceptron.mp4 64MB
  83. 16. Naive Bayes/2. Bayes Theorem.mp4 63MB
  84. 25. Deep Learning/3. History.mp4 62MB
  85. 1. Python Fundamentals/19. Dictionaries.mp4 62MB
  86. 3. Pandas/2. Series.mp4 61MB
  87. 22. Unsupervised Learning/10. Case Study Part2.mp4 61MB
  88. 18. Support Vector Machine (SVM)/13. Case Study 3 Part2.mp4 61MB
  89. 12. Gradient Descent/1. Pre-Req For Gradient Descent Part1.mp4 61MB
  90. 8. Exploratory Data Analysis/11. Univariate Analysis Part2.mp4 61MB
  91. 8. Exploratory Data Analysis/13. Bivariate Analysis.mp4 61MB
  92. 26. Project Kaggle/1.1 training.zip.zip 60MB
  93. 16. Naive Bayes/4. Practical Example from NB with Multiple Columns.mp4 60MB
  94. 3. Pandas/7. loc and iloc.mp4 59MB
  95. 22. Unsupervised Learning/1. Introduction to Clustering.mp4 59MB
  96. 26. Project Kaggle/8. Evaluating Worst ML Model.mp4 59MB
  97. 18. Support Vector Machine (SVM)/1. Introduction.mp4 59MB
  98. 9. Simple Linear Regression/4. How LR Works.mp4 59MB
  99. 6. Hypothesis Testing/6. z Table.mp4 59MB
  100. 1. Python Fundamentals/18. Sets.mp4 58MB
  101. 22. Unsupervised Learning/3. Kmeans.mp4 58MB
  102. 13. KNN/4. Accuracy of KNN.mp4 57MB
  103. 18. Support Vector Machine (SVM)/12. Case Study 3 Part1.mp4 56MB
  104. 16. Naive Bayes/7. Laplace Smoothing.mp4 55MB
  105. 11. HotstarNetflix Real world Case Study for Multiple Linear Regression/3. Building Model Part1.mp4 55MB
  106. 5. Inferential Statistics/2. Probability Theory.mp4 55MB
  107. 16. Naive Bayes/5. Naive Bayes On Text Data Part1.mp4 55MB
  108. 26. Project Kaggle/10. Response encoding and one hot encoder.mp4 55MB
  109. 13. KNN/1. Introduction to Classification.mp4 54MB
  110. 7. Data Visualisation/4. Seaborn On Time Series Data.mp4 54MB
  111. 22. Unsupervised Learning/4. Maths Behind Kmeans.mp4 54MB
  112. 8. Exploratory Data Analysis/7. Data Sourcing and Cleaning part6.mp4 54MB
  113. 20. Ensembling/11. Adaboost Case Study.mp4 54MB
  114. 9. Simple Linear Regression/8. LR Case Study Part2.mp4 53MB
  115. 13. KNN/13. Classification Case3.mp4 53MB
  116. 9. Simple Linear Regression/5. Some Fun With Maths Behind LR.mp4 53MB
  117. 9. Simple Linear Regression/6. R Square.mp4 52MB
  118. 13. KNN/12. Classification Case2.mp4 52MB
  119. 15. Model Selection Part1/1. Model Creation Case1.mp4 52MB
  120. 22. Unsupervised Learning/6. Kmeans plus.mp4 52MB
  121. 26. Project Kaggle/21. Building Machine Learning model part6.mp4 51MB
  122. 26. Project Kaggle/15. Data pre-processing before building machine learning model.mp4 51MB
  123. 3. Pandas/6. Indexes.mp4 50MB
  124. 18. Support Vector Machine (SVM)/9. Kernel Part1.mp4 49MB
  125. 25. Deep Learning/2. Introduction.mp4 49MB
  126. 24. Advanced Machine Learning Algorithms/6. Regularization.mp4 49MB
  127. 11. HotstarNetflix Real world Case Study for Multiple Linear Regression/5. Building Model Part3.mp4 49MB
  128. 26. Project Kaggle/11. Laplace Smoothing and Calibrated classifier.mp4 48MB
  129. 13. KNN/5. Effectiveness of KNN.mp4 48MB
  130. 13. KNN/6. Distance Metrics.mp4 48MB
  131. 13. KNN/3. Introduction to KNN.mp4 47MB
  132. 3. Pandas/10. groupby.mp4 47MB
  133. 9. Simple Linear Regression/9. LR Case Study Part3.mp4 46MB
  134. 16. Naive Bayes/6. Naive Bayes On Text Data Part2.mp4 46MB
  135. 10. Multiple Linear Regression/10. Case Study Part5.mp4 46MB
  136. 26. Project Kaggle/13. Second Categorical column.mp4 46MB
  137. 23. Dimension Reduction/4. Case Study Part1.mp4 45MB
  138. 24. Advanced Machine Learning Algorithms/3. Example Part2.mp4 45MB
  139. 17. Logistic Regression/2. Sigmoid Function.mp4 44MB
  140. 19. Decision Tree/4. Gini Index.mp4 44MB
  141. 3. Pandas/5. Operations Part2.mp4 44MB
  142. 3. Pandas/8. Reading CSV.mp4 42MB
  143. 26. Project Kaggle/20. Building Machine Learning model part5.mp4 42MB
  144. 8. Exploratory Data Analysis/14. Derived Columns.mp4 42MB
  145. 17. Logistic Regression/3. Log Odds.mp4 42MB
  146. 20. Ensembling/9. Adaboost Part1.mp4 42MB
  147. 21. Model Selection Part2/2. Model Selection Part2.mp4 41MB
  148. 13. KNN/14. Classification Case4.mp4 41MB
  149. 11. HotstarNetflix Real world Case Study for Multiple Linear Regression/1. Introduction to the Problem Statement.mp4 41MB
  150. 19. Decision Tree/2. Example of DT.mp4 41MB
  151. 19. Decision Tree/8. Preventing Overfitting Issues in DT.mp4 40MB
  152. 13. KNN/2. Defining Classification Mathematically.mp4 40MB
  153. 24. Advanced Machine Learning Algorithms/5. Case study.mp4 40MB
  154. 24. Advanced Machine Learning Algorithms/7. Ridge and Lasso.mp4 40MB
  155. 11. HotstarNetflix Real world Case Study for Multiple Linear Regression/6. Verification of Model.mp4 39MB
  156. 20. Ensembling/1. Introduction to Ensembles.mp4 39MB
  157. 3. Pandas/1. Introduction.mp4 39MB
  158. 20. Ensembling/15. XGboost Algorithm.mp4 39MB
  159. 5. Inferential Statistics/12. Sampling.mp4 39MB
  160. 20. Ensembling/10. Adaboost Part2.mp4 38MB
  161. 26. Project Kaggle/18. Building Machine Learning model part3.mp4 38MB
  162. 22. Unsupervised Learning/12. Hierarchial Clustering.mp4 38MB
  163. 6. Hypothesis Testing/4. OneTwo Tailed Tests.mp4 38MB
  164. 12. Gradient Descent/5. Gradient Descent.mp4 38MB
  165. 1. Python Fundamentals/6. Numeric Operations in Python.mp4 37MB
  166. 12. Gradient Descent/4. Defining Cost Functions More Formally.mp4 37MB
  167. 22. Unsupervised Learning/7. Value of K.mp4 36MB
  168. 21. Model Selection Part2/3. Model Selection Part3.mp4 36MB
  169. 20. Ensembling/14. Boosting Part2.mp4 36MB
  170. 9. Simple Linear Regression/2. Types of Machine Learning.mp4 35MB
  171. 15. Model Selection Part1/2. Model Creation Case2.mp4 35MB
  172. 5. Inferential Statistics/15. Confidence Interval Part1.mp4 35MB
  173. 22. Unsupervised Learning/13. Case Study.mp4 34MB
  174. 3. Pandas/11. Merging Part2.mp4 34MB
  175. 6. Hypothesis Testing/9. p Value.mp4 33MB
  176. 13. KNN/8. Finding k.mp4 33MB
  177. 18. Support Vector Machine (SVM)/6. Slack Variable.mp4 33MB
  178. 26. Project Kaggle/19. Building Machine Learning model part4.mp4 33MB
  179. 20. Ensembling/6. Introduction to Boosting.mp4 33MB
  180. 12. Gradient Descent/2. Pre-Req For Gradient Descent Part2.mp4 33MB
  181. 24. Advanced Machine Learning Algorithms/9. Model Selection.mp4 31MB
  182. 6. Hypothesis Testing/1. Introduction.mp4 31MB
  183. 24. Advanced Machine Learning Algorithms/1. Introduction.mp4 31MB
  184. 3. Pandas/9. Merging Part1.mp4 30MB
  185. 25. Deep Learning/4. Perceptron.mp4 30MB
  186. 19. Decision Tree/1. Introduction.mp4 30MB
  187. 8. Exploratory Data Analysis/9. Data Cleaning part2.mp4 30MB
  188. 6. Hypothesis Testing/12. t- distribution Part2.mp4 29MB
  189. 19. Decision Tree/5. Information Gain Part1.mp4 29MB
  190. 13. KNN/7. Distance Metrics Part2.mp4 29MB
  191. 6. Hypothesis Testing/2. NULL And Alternate Hypothesis.mp4 29MB
  192. 5. Inferential Statistics/6. Without Experiment.mp4 29MB
  193. 22. Unsupervised Learning/2. Segmentation.mp4 29MB
  194. 6. Hypothesis Testing/3. Examples.mp4 28MB
  195. 4. Some Fun With Maths/4. Linear Algebra Going From 2D to nD Part1.mp4 28MB
  196. 3. Pandas/12. Pivot Table.mp4 28MB
  197. 24. Advanced Machine Learning Algorithms/2. Example Part1.mp4 27MB
  198. 1. Python Fundamentals/12. String Part2.mp4 27MB
  199. 19. Decision Tree/6. Information Gain Part2.mp4 27MB
  200. 16. Naive Bayes/8. Bernoulli Naive Bayes.mp4 27MB
  201. 18. Support Vector Machine (SVM)/2. Hyperplane Part1.mp4 27MB
  202. 12. Gradient Descent/7. Closed Form Vs Gradient Descent.mp4 27MB
  203. 17. Logistic Regression/1. Introduction.mp4 27MB
  204. 6. Hypothesis Testing/7. Examples.mp4 26MB
  205. 4. Some Fun With Maths/5. Linear Algebra 2D to nD Part2.mp4 26MB
  206. 5. Inferential Statistics/13. Sampling Distribution.mp4 26MB
  207. 16. Naive Bayes/11. Case Study 2 Part2.mp4 25MB
  208. 2. Numpy/1. Introduction.mp4 25MB
  209. 6. Hypothesis Testing/5. Critical Value Method.mp4 25MB
  210. 8. Exploratory Data Analysis/12. Segmented Analysis.mp4 24MB
  211. 5. Inferential Statistics/4. Expected Values Part1.mp4 24MB
  212. 5. Inferential Statistics/3. Probability Distribution.mp4 24MB
  213. 18. Support Vector Machine (SVM)/4. Maths Behind SVM.mp4 24MB
  214. 14. Model Performance Metrics/3. Performance Metrics Part3.mp4 24MB
  215. 5. Inferential Statistics/11. z Score.mp4 24MB
  216. 20. Ensembling/12. XGBoost.mp4 23MB
  217. 12. Gradient Descent/6. Optimisation.mp4 22MB
  218. 6. Hypothesis Testing/11. t- distribution Part1.mp4 21MB
  219. 5. Inferential Statistics/9. PDF.mp4 21MB
  220. 19. Decision Tree/3. Homogenity.mp4 21MB
  221. 24. Advanced Machine Learning Algorithms/10. Adjusted R Square.mp4 20MB
  222. 5. Inferential Statistics/10. Normal Distribution.mp4 19MB
  223. 22. Unsupervised Learning/11. More on Segmentation.mp4 18MB
  224. 20. Ensembling/7. Weak Learners.mp4 18MB
  225. 9. Simple Linear Regression/3. Introduction to Linear Regression (LR).mp4 18MB
  226. 5. Inferential Statistics/7. Binomial Distribution.mp4 18MB
  227. 1. Python Fundamentals/7. Logical Operations.mp4 17MB
  228. 6. Hypothesis Testing/8. More Examples.mp4 16MB
  229. 10. Multiple Linear Regression/1. Introduction.mp4 16MB
  230. 20. Ensembling/4. Runtime.mp4 16MB
  231. 8. Exploratory Data Analysis/3. Data Sourcing and Cleaning part2.mp4 16MB
  232. 8. Exploratory Data Analysis/2. Data Sourcing and Cleaning part1.mp4 16MB
  233. 19. Decision Tree/7. Advantages and Disadvantages of DT.mp4 15MB
  234. 18. Support Vector Machine (SVM)/1.1 SVM.zip.zip 15MB
  235. 6. Hypothesis Testing/10. Types of Error.mp4 15MB
  236. 20. Ensembling/8. Shallow Decision Tree.mp4 15MB
  237. 20. Ensembling/3. Advantages.mp4 15MB
  238. 5. Inferential Statistics/5. Expected Values Part2.mp4 14MB
  239. 20. Ensembling/13. Boosting Part1.mp4 14MB
  240. 5. Inferential Statistics/16. Confidence Interval Part2.mp4 13MB
  241. 12. Gradient Descent/3. Cost Functions.mp4 13MB
  242. 5. Inferential Statistics/14. Central Limit Theorem.mp4 13MB
  243. 8. Exploratory Data Analysis/6. Data Sourcing and Cleaning part5.mp4 12MB
  244. 22. Unsupervised Learning/8. Hopkins test.mp4 12MB
  245. 3. Pandas/4. Operations Part1.mp4 12MB
  246. 9. Simple Linear Regression/1. Introduction to Machine Learning.mp4 11MB
  247. 18. Support Vector Machine (SVM)/5. Support Vectors.mp4 11MB
  248. 8. Exploratory Data Analysis/5. Data Sourcing and Cleaning part4.mp4 10MB
  249. 5. Inferential Statistics/1. Inferential Statistics.mp4 10MB
  250. 1. Python Fundamentals/4. Python Introduction.mp4 10MB
  251. 1. Python Fundamentals/13. List Part1.mp4 10MB
  252. 8. Exploratory Data Analysis/4. Data Sourcing and Cleaning part3.mp4 10MB
  253. 22. Unsupervised Learning/5. More Maths.mp4 9MB
  254. 25. Deep Learning/1. Expectations.mp4 9MB
  255. 13. KNN/9. KNN on Regression.mp4 9MB
  256. 23. Dimension Reduction/1.1 PCA code for udemy.zip.zip 9MB
  257. 5. Inferential Statistics/8. Commulative Distribution.mp4 8MB
  258. 10. Multiple Linear Regression/5. Adjusted R Square.mp4 8MB
  259. 22. Unsupervised Learning/1.1 Unsupervised.zip.zip 7MB
  260. 9. Simple Linear Regression/10. Residual Square Error (RSE).mp4 5MB
  261. 19. Decision Tree/1.1 DT_forudemy.zip.zip 4MB
  262. 8. Exploratory Data Analysis/1. Introduction.mp4 4MB
  263. 1. Python Fundamentals/3.2 Installing-Python.Teclov.pdf.pdf 1MB
  264. 13. KNN/1.1 KNN.zip.zip 1MB
  265. 26. Project Kaggle/1.2 Teclov Project - Medical treatment.ipynb.zip.zip 1MB
  266. 20. Ensembling/1.1 Boosting.zip.zip 1MB
  267. 7. Data Visualisation/1.1 Datavisual.zip.zip 1MB
  268. 24. Advanced Machine Learning Algorithms/1.1 AdvanceReg.zip.zip 1MB
  269. 20. Ensembling/1.2 RF_forudemy.zip.zip 1MB
  270. 17. Logistic Regression/1.1 LogisticReg.zip.zip 984KB
  271. 10. Multiple Linear Regression/1.1 Multplr_LR_Code_for Udemy.zip.zip 521KB
  272. 15. Model Selection Part1/1.1 CrossValidation_Linear Regression.zip.zip 342KB
  273. 16. Naive Bayes/1.1 NaiveBayes.zip.zip 266KB
  274. 11. HotstarNetflix Real world Case Study for Multiple Linear Regression/1.1 Hotstarcode-for-udemy.zip.zip 255KB
  275. 12. Gradient Descent/1.1 Gradient+Descent+Updated.zip.zip 161KB
  276. 6. Hypothesis Testing/1.2 t-table.pdf.pdf 147KB
  277. 9. Simple Linear Regression/1.1 code-LR-Teclov.zip.zip 77KB
  278. 6. Hypothesis Testing/1.1 z-table.pdf.pdf 59KB
  279. 4. Some Fun With Maths/1. Linear Algebra Vectors.vtt 50KB
  280. 23. Dimension Reduction/1. Introduction.vtt 32KB
  281. 2. Numpy/3. Numpy Operations Part2.vtt 30KB
  282. 14. Model Performance Metrics/1. Performance Metrics Part1.vtt 27KB
  283. 23. Dimension Reduction/2. PCA.vtt 26KB
  284. 7. Data Visualisation/1. Matplotlib.vtt 26KB
  285. 7. Data Visualisation/2. Seaborn.vtt 26KB
  286. 8. Exploratory Data Analysis/10. Univariate Analysis Part1.vtt 26KB
  287. 23. Dimension Reduction/3. Maths Behind PCA.vtt 26KB
  288. 13. KNN/11. Classification Case1.vtt 25KB
  289. 2. Numpy/2. Numpy Operations Part1.vtt 24KB
  290. 21. Model Selection Part2/1. Model Selection Part1.vtt 23KB
  291. 1. Python Fundamentals/5. Variables in Python.vtt 21KB
  292. 17. Logistic Regression/4. Case Study.vtt 20KB
  293. 18. Support Vector Machine (SVM)/14. Case Study 4.vtt 20KB
  294. 26. Project Kaggle/6. Understanding Evaluation Matrix Log Loss.vtt 20KB
  295. 8. Exploratory Data Analysis/11. Univariate Analysis Part2.vtt 20KB
  296. 4. Some Fun With Maths/3. Linear Algebra Matrix Part2.vtt 19KB
  297. 14. Model Performance Metrics/2. Performance Metrics Part2.vtt 19KB
  298. 23. Dimension Reduction/5. Case Study Part2.vtt 19KB
  299. 15. Model Selection Part1/4. Gridsearch Case study Part2.vtt 18KB
  300. 25. Deep Learning/3. History.vtt 18KB
  301. 26. Project Kaggle/2. Playing With The Data.vtt 18KB
  302. 10. Multiple Linear Regression/9. Case Study Part4.vtt 18KB
  303. 16. Naive Bayes/1. Introduction to Naive Bayes.vtt 18KB
  304. 12. Gradient Descent/1. Pre-Req For Gradient Descent Part1.vtt 18KB
  305. 9. Simple Linear Regression/7. LR Case Study Part1.vtt 17KB
  306. 26. Project Kaggle/16. Building Machine Learning model part1.vtt 17KB
  307. 13. KNN/12. Classification Case2.vtt 17KB
  308. 4. Some Fun With Maths/2. Linear Algebra Matrix Part1.vtt 17KB
  309. 18. Support Vector Machine (SVM)/3. Hyperplane Part2.vtt 17KB
  310. 24. Advanced Machine Learning Algorithms/4. Optimal Solution.vtt 17KB
  311. 8. Exploratory Data Analysis/8. Data Cleaning part1.vtt 17KB
  312. 8. Exploratory Data Analysis/13. Bivariate Analysis.vtt 16KB
  313. 1. Python Fundamentals/3.1 Python-code-udemy.zip.zip 16KB
  314. 1. Python Fundamentals/4.1 Python-code-udemy.zip.zip 16KB
  315. 1. Python Fundamentals/1. Introduction to the course.vtt 16KB
  316. 13. KNN/5. Effectiveness of KNN.vtt 16KB
  317. 1. Python Fundamentals/11. String Part1.vtt 16KB
  318. 13. KNN/1. Introduction to Classification.vtt 16KB
  319. 3. Pandas/1.1 Pandas.zip.zip 15KB
  320. 20. Ensembling/2. Bagging.vtt 15KB
  321. 26. Project Kaggle/17. Building Machine Learning model part2.vtt 15KB
  322. 13. KNN/13. Classification Case3.vtt 15KB
  323. 13. KNN/4. Accuracy of KNN.vtt 15KB
  324. 26. Project Kaggle/9. First Categorical column analysis.vtt 15KB
  325. 13. KNN/6. Distance Metrics.vtt 15KB
  326. 21. Model Selection Part2/2. Model Selection Part2.vtt 15KB
  327. 1. Python Fundamentals/10. Functions.vtt 14KB
  328. 25. Deep Learning/5. Multi Layered Perceptron.vtt 14KB
  329. 26. Project Kaggle/11. Laplace Smoothing and Calibrated classifier.vtt 14KB
  330. 8. Exploratory Data Analysis/14. Derived Columns.vtt 14KB
  331. 5. Inferential Statistics/2. Probability Theory.vtt 14KB
  332. 13. KNN/14. Classification Case4.vtt 14KB
  333. 18. Support Vector Machine (SVM)/1. Introduction.vtt 14KB
  334. 13. KNN/3. Introduction to KNN.vtt 14KB
  335. 25. Deep Learning/6. Neural Network Playground.vtt 14KB
  336. 15. Model Selection Part1/3. Gridsearch Case study Part1.vtt 13KB
  337. 20. Ensembling/17. Case Study Part2.vtt 13KB
  338. 22. Unsupervised Learning/4. Maths Behind Kmeans.vtt 13KB
  339. 22. Unsupervised Learning/9. Case Study Part1.vtt 13KB
  340. 1. Python Fundamentals/14. List Part2.vtt 13KB
  341. 16. Naive Bayes/4. Practical Example from NB with Multiple Columns.vtt 13KB
  342. 1. Python Fundamentals/9. for while Loop.vtt 13KB
  343. 19. Decision Tree/9. DT Case Study Part1.vtt 13KB
  344. 7. Data Visualisation/3. Case Study.vtt 13KB
  345. 16. Naive Bayes/2. Bayes Theorem.vtt 13KB
  346. 22. Unsupervised Learning/1. Introduction to Clustering.vtt 13KB
  347. 18. Support Vector Machine (SVM)/10. Kernel Part2.vtt 13KB
  348. 12. Gradient Descent/5. Gradient Descent.vtt 13KB
  349. 15. Model Selection Part1/1. Model Creation Case1.vtt 12KB
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  381. 22. Unsupervised Learning/3. Kmeans.vtt 10KB
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  414. 26. Project Kaggle/14. Third Categorical column.vtt 9KB
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  421. 10. Multiple Linear Regression/11. Case Study Part6 (RFE).vtt 8KB
  422. 17. Logistic Regression/1. Introduction.vtt 8KB
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  432. 24. Advanced Machine Learning Algorithms/7. Ridge and Lasso.vtt 8KB
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  434. 22. Unsupervised Learning/7. Value of K.vtt 8KB
  435. 6. Hypothesis Testing/2. NULL And Alternate Hypothesis.vtt 8KB
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  438. 5. Inferential Statistics/6. Without Experiment.vtt 7KB
  439. 24. Advanced Machine Learning Algorithms/1. Introduction.vtt 7KB
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  447. 12. Gradient Descent/8. Gradient Descent case study.vtt 7KB
  448. 19. Decision Tree/5. Information Gain Part1.vtt 7KB
  449. 6. Hypothesis Testing/3. Examples.vtt 7KB
  450. 16. Naive Bayes/6. Naive Bayes On Text Data Part2.vtt 7KB
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  452. 22. Unsupervised Learning/13. Case Study.vtt 7KB
  453. 24. Advanced Machine Learning Algorithms/9. Model Selection.vtt 7KB
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  455. 20. Ensembling/6. Introduction to Boosting.vtt 6KB
  456. 6. Hypothesis Testing/9. p Value.vtt 6KB
  457. 2. Numpy/1. Introduction.vtt 6KB
  458. 11. HotstarNetflix Real world Case Study for Multiple Linear Regression/1. Introduction to the Problem Statement.vtt 6KB
  459. 14. Model Performance Metrics/3. Performance Metrics Part3.vtt 6KB
  460. 18. Support Vector Machine (SVM)/13. Case Study 3 Part2.vtt 6KB
  461. 18. Support Vector Machine (SVM)/2. Hyperplane Part1.vtt 6KB
  462. 10. Multiple Linear Regression/10. Case Study Part5.vtt 6KB
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  464. 23. Dimension Reduction/4. Case Study Part1.vtt 6KB
  465. 24. Advanced Machine Learning Algorithms/2. Example Part1.vtt 6KB
  466. 20. Ensembling/11. Adaboost Case Study.vtt 6KB
  467. 11. HotstarNetflix Real world Case Study for Multiple Linear Regression/3. Building Model Part1.vtt 6KB
  468. 19. Decision Tree/3. Homogenity.vtt 6KB
  469. 12. Gradient Descent/7. Closed Form Vs Gradient Descent.vtt 6KB
  470. 3. Pandas/11. Merging Part2.vtt 6KB
  471. 19. Decision Tree/6. Information Gain Part2.vtt 6KB
  472. 26. Project Kaggle/15. Data pre-processing before building machine learning model.vtt 6KB
  473. 7. Data Visualisation/4. Seaborn On Time Series Data.vtt 6KB
  474. 9. Simple Linear Regression/9. LR Case Study Part3.vtt 6KB
  475. 5. Inferential Statistics/4. Expected Values Part1.vtt 6KB
  476. 22. Unsupervised Learning/11. More on Segmentation.vtt 5KB
  477. 5. Inferential Statistics/3. Probability Distribution.vtt 5KB
  478. 9. Simple Linear Regression/8. LR Case Study Part2.vtt 5KB
  479. 5. Inferential Statistics/9. PDF.vtt 5KB
  480. 5. Inferential Statistics/11. z Score.vtt 5KB
  481. 5. Inferential Statistics/10. Normal Distribution.vtt 5KB
  482. 26. Project Kaggle/13. Second Categorical column.vtt 5KB
  483. 2. Numpy/1.1 Teclov-numpy.ipynb.zip.zip 5KB
  484. 26. Project Kaggle/20. Building Machine Learning model part5.vtt 5KB
  485. 20. Ensembling/3. Advantages.vtt 5KB
  486. 12. Gradient Descent/6. Optimisation.vtt 5KB
  487. 16. Naive Bayes/7. Laplace Smoothing.vtt 5KB
  488. 11. HotstarNetflix Real world Case Study for Multiple Linear Regression/6. Verification of Model.vtt 5KB
  489. 20. Ensembling/12. XGBoost.vtt 5KB
  490. 20. Ensembling/4. Runtime.vtt 5KB
  491. 11. HotstarNetflix Real world Case Study for Multiple Linear Regression/5. Building Model Part3.vtt 5KB
  492. 6. Hypothesis Testing/5. Critical Value Method.vtt 5KB
  493. 3. Pandas/12. Pivot Table.vtt 4KB
  494. 19. Decision Tree/7. Advantages and Disadvantages of DT.vtt 4KB
  495. 8. Exploratory Data Analysis/7. Data Sourcing and Cleaning part6.vtt 4KB
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  497. 24. Advanced Machine Learning Algorithms/5. Case study.vtt 4KB
  498. 5. Inferential Statistics/7. Binomial Distribution.vtt 4KB
  499. 26. Project Kaggle/18. Building Machine Learning model part3.vtt 4KB
  500. 6. Hypothesis Testing/11. t- distribution Part1.vtt 4KB
  501. 24. Advanced Machine Learning Algorithms/10. Adjusted R Square.vtt 4KB
  502. 8. Exploratory Data Analysis/2. Data Sourcing and Cleaning part1.vtt 4KB
  503. 18. Support Vector Machine (SVM)/5. Support Vectors.vtt 4KB
  504. 26. Project Kaggle/19. Building Machine Learning model part4.vtt 4KB
  505. 5. Inferential Statistics/5. Expected Values Part2.vtt 4KB
  506. 8. Exploratory Data Analysis/5. Data Sourcing and Cleaning part4.vtt 4KB
  507. 8. Exploratory Data Analysis/6. Data Sourcing and Cleaning part5.vtt 4KB
  508. 20. Ensembling/13. Boosting Part1.vtt 4KB
  509. 10. Multiple Linear Regression/1. Introduction.vtt 4KB
  510. 6. Hypothesis Testing/7. Examples.vtt 4KB
  511. 1. Python Fundamentals/4. Python Introduction.vtt 4KB
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  513. 6. Hypothesis Testing/10. Types of Error.vtt 3KB
  514. 6. Hypothesis Testing/8. More Examples.vtt 3KB
  515. 8. Exploratory Data Analysis/4. Data Sourcing and Cleaning part3.vtt 3KB
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  518. 20. Ensembling/7. Weak Learners.vtt 3KB
  519. 6. Hypothesis Testing/12. t- distribution Part2.vtt 3KB
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  521. 22. Unsupervised Learning/8. Hopkins test.vtt 3KB
  522. 5. Inferential Statistics/14. Central Limit Theorem.vtt 3KB
  523. 9. Simple Linear Regression/3. Introduction to Linear Regression (LR).vtt 3KB
  524. 16. Naive Bayes/11. Case Study 2 Part2.vtt 3KB
  525. 13. KNN/9. KNN on Regression.vtt 3KB
  526. 1. Python Fundamentals/13. List Part1.vtt 3KB
  527. 22. Unsupervised Learning/5. More Maths.vtt 3KB
  528. 12. Gradient Descent/3. Cost Functions.vtt 3KB
  529. 25. Deep Learning/1. Expectations.vtt 3KB
  530. 20. Ensembling/8. Shallow Decision Tree.vtt 3KB
  531. 5. Inferential Statistics/8. Commulative Distribution.vtt 3KB
  532. 8. Exploratory Data Analysis/3. Data Sourcing and Cleaning part2.vtt 3KB
  533. 9. Simple Linear Regression/1. Introduction to Machine Learning.vtt 2KB
  534. 16. Naive Bayes/8. Bernoulli Naive Bayes.vtt 2KB
  535. 3. Pandas/4. Operations Part1.vtt 1KB
  536. 9. Simple Linear Regression/10. Residual Square Error (RSE).vtt 1KB
  537. 8. Exploratory Data Analysis/1. Introduction.vtt 897B
  538. 10. Multiple Linear Regression/5. Adjusted R Square.vtt 855B
  539. udemycoursedownloader.com.url 132B
  540. Udemy Course downloader.txt 94B