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LinkedIn Learning Advance Your Skills as a Machine Learning Spe

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LinkedIn Learning Advance Your Skills as a Machine Learning Spe
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Files:
  1. Exercises_Link - OneHack.us.txt 121 bytes
  2. Exercises_Link.txt 123 bytes
  3. $10 ChatGPT for 1 Year & More.txt 252 bytes
  4. description.html 1006 bytes
  5. description.html 1015 bytes
  6. description.html 1.1 KB
  7. description.html 1.1 KB
  8. description.html 1.1 KB
  9. 1. Continuing your deep learning journey.srt 1.2 KB
  10. description.html 1.2 KB
  11. 1. Making decisions with Python.srt 1.3 KB
  12. 1. Getting started with Python and k-means clustering.srt 1.3 KB
  13. description.html 1.3 KB
  14. description.html 1.3 KB
  15. 4. Tuning backpropagation.srt 1.3 KB
  16. 1. Optimizing neural networks.srt 1.4 KB
  17. 3. Regularization experiment.srt 1.4 KB
  18. 2. Regularization.srt 1.4 KB
  19. 5. Avoiding overfitting.srt 1.4 KB
  20. 5. Dropout experiment.srt 1.5 KB
  21. 2. Acquire and process data.srt 1.5 KB
  22. 1. Exploring the world of explainable AI and interpretable machine learning.srt 1.6 KB
  23. 2. What you should know.srt 1.6 KB
  24. 3. What you should know.srt 1.6 KB
  25. 1. Next steps.srt 1.6 KB
  26. 1. Review.srt 1.7 KB
  27. 1. Classifying data with logistic regression.srt 1.8 KB
  28. 4. Dropouts.srt 1.8 KB
  29. 1. Association rule mining.srt 1.9 KB
  30. 2. What you should know.srt 1.9 KB
  31. 1. MPG data set.srt 1.9 KB
  32. 6. Learning rate experiment.srt 1.9 KB
  33. 2. What you should know.srt 1.9 KB
  34. 2. What you should know.srt 2.0 KB
  35. 3. Tuning the network.srt 2.0 KB
  36. 2. p-value review.srt 2.0 KB
  37. 2. What you should know.srt 2.0 KB
  38. 7. Evaluating the accuracy of your CART tree.srt 2.0 KB
  39. 5. Learning rate.srt 2.0 KB
  40. 3. The tools you need.srt 2.0 KB
  41. 3. The tools you need.srt 2.1 KB
  42. 2. Why causation matters in a business setting.srt 2.1 KB
  43. 3. Using the exercise files.srt 2.1 KB
  44. 1. The basics of decision trees.srt 2.1 KB
  45. 2. Target audience.srt 2.1 KB
  46. 3. Using the exercise files.srt 2.2 KB
  47. 4. Optimizer experiment.srt 2.2 KB
  48. 1. Prediction, causation, and statistical inference.srt 2.2 KB
  49. 3. How to use the practice files.srt 2.2 KB
  50. 6. Building the final model.srt 2.3 KB
  51. 8. How C4.5 handles continuous variables.srt 2.3 KB
  52. 7. Challenge Conditional probability and Bayes' theorem.srt 2.4 KB
  53. 2. What you should know.srt 2.4 KB
  54. 4. Using the exercise files.srt 2.5 KB
  55. 3. Optimizers.srt 2.5 KB
  56. 3. An ANN model.srt 2.5 KB
  57. 4. Model optimization and tuning.srt 2.5 KB
  58. 5. Challenge Evaluate significant finding.srt 2.6 KB
  59. 5. How CART handles nominal variables.srt 2.6 KB
  60. 4. Using the exercise files.srt 2.7 KB
  61. 1. Thinking about causality.srt 2.7 KB
  62. 1. What is deep learning.srt 2.7 KB
  63. 4. Challenge What is causing what.srt 2.8 KB
  64. 4. Why and when to use logistic regression.srt 2.9 KB
  65. 4. Double blind studies.srt 2.9 KB
  66. 6. Initializing weights.srt 2.9 KB
  67. 5. Challenge JASP.srt 2.9 KB
  68. 1. Next steps with decision trees.srt 3.0 KB
  69. 1. Next steps.srt 3.0 KB
  70. 2. Batch normalization.srt 3.2 KB
  71. 1. Overfitting in ANNs.srt 3.3 KB
  72. 9. Equal size sampling.srt 3.3 KB
  73. 3. What is a causal model.srt 3.3 KB
  74. 1. Next steps.srt 3.3 KB
  75. 3. Hidden layers tuning.srt 3.3 KB
  76. 1. Epoch and batch size tuning.srt 3.4 KB
  77. 6. Experiment setups for the course.srt 3.4 KB
  78. 5. Choosing activation functions.srt 3.4 KB
  79. 1. Next steps.srt 3.4 KB
  80. 9. Challenge Moderation, mediation, or a third variable.srt 3.4 KB
  81. 3. Setting up exercise files.srt 3.5 KB
  82. 2. Variable importance and reason codes.srt 3.5 KB
  83. 4. Determining nodes in a layer.srt 3.5 KB
  84. 7. KNIME support of global and local explanations.srt 3.6 KB
  85. 9. Accuracy.srt 3.6 KB
  86. 2. Downloading BayesiaLab and resources.srt 3.6 KB
  87. 3. The math behind regression trees.srt 3.6 KB
  88. 6. XAI for debugging models.srt 3.6 KB
  89. 1. Ross Quinlan, ID3, C4.5, and C5.0.srt 3.6 KB
  90. 6. A quick look at the complete CART tree.srt 3.6 KB
  91. 7. How C4.5 handles nominal variables.srt 3.6 KB
  92. 4. Taleb on normality, mediocristan, and extremistan.srt 3.7 KB
  93. 5. Local and global explanations.srt 3.7 KB
  94. 5. Counterfactuals Pearl on induction and causality.srt 3.8 KB
  95. 8. Line plot.srt 3.8 KB
  96. 8. Solution Conditional probability and Bayes' theorem.srt 4.0 KB
  97. 2. What is the Gini coefficient.srt 4.0 KB
  98. 6. Why and when to use association rules.srt 4.1 KB
  99. 3. AB testing during the evaluation phase.srt 4.2 KB
  100. 1. Vanishing and exploding gradients.srt 4.2 KB
  101. 10. A quick look at the complete C4.5 tree.srt 4.3 KB
  102. 6. Judea Pearl Problems with control variables.srt 4.4 KB
  103. 2. Introducing path analysis and SEM.srt 4.4 KB
  104. 2. Review of artificial neural networks.srt 4.4 KB
  105. 1. Skepticism about data Truman 1948 Election Poll.srt 4.4 KB
  106. 1. Taking causality further.srt 4.4 KB
  107. 11. Evaluating the accuracy of your C4.5 tree.srt 4.4 KB
  108. 3. How C4.5 handles missing data.srt 4.4 KB
  109. 5. Latent variables in SEM.srt 4.5 KB
  110. 7. KNIME's missing data options for regression trees.srt 4.5 KB
  111. 4. Changing the settings in KNIME.srt 4.5 KB
  112. 3. Skepticism about causes Is X really causing Y.srt 4.5 KB
  113. 2. Prerequisites for the course.srt 4.6 KB
  114. 4. Why and when to use k-means clustering.srt 4.6 KB
  115. 4. The Give Me Some Credit data set.srt 4.6 KB
  116. 6. KNIME settings for C4.5.srt 4.9 KB
  117. 1. What is a decision tree.srt 4.9 KB
  118. 1. The investigator, the jury, and the judge.srt 5.0 KB
  119. 6. Why and when to use a decision tree.srt 5.0 KB
  120. 5. Bayesian Networks Black Swan case study.srt 5.0 KB
  121. 2. Epoch and batch size experiment.srt 5.1 KB
  122. 5. The deep learning tuning process.srt 5.2 KB
  123. 6. Finding direction of causality with SEM (PSAT).srt 5.3 KB
  124. 6. Closer look at a full regression tree.srt 5.3 KB
  125. 1. What is regression.srt 5.3 KB
  126. 3. Google Optimize.srt 5.4 KB
  127. 5. Ordinal variable handling.srt 5.4 KB
  128. 2. Enigma and uncertainty.srt 5.7 KB
  129. 10. Solution Moderation, mediation, or a third variable.srt 5.7 KB
  130. 2. How to evaluate and visualize clusters in Python.srt 5.7 KB
  131. 5. An overview of decision tree algorithms.srt 5.8 KB
  132. 2. Hume on induction.srt 5.8 KB
  133. 2. Skepticism about results Is that really the best predictor.srt 5.8 KB
  134. 1. Introducing Leo Breiman and CART.srt 5.9 KB
  135. 3. Introducing KNIME.srt 6.0 KB
  136. 2. What is k-means clustering.srt 6.1 KB
  137. 3. SEM example Intention.srt 6.2 KB
  138. 4. Myths about SEM.srt 6.2 KB
  139. 4. Bayes and rare events.srt 6.2 KB
  140. 3. Introducing BayesiaLab Hair and eye color.srt 6.3 KB
  141. 2. The anatomy of a regression model.srt 6.3 KB
  142. 2. The regression tree prebuilt example.srt 6.3 KB
  143. 6. Solution JASP.srt 6.4 KB
  144. 1. Sewell Wright.srt 6.5 KB
  145. 4. How RT handles nominal variables.srt 6.5 KB
  146. 4. Taleb on induction.srt 6.5 KB
  147. 5. Wordle, bans, and bits.srt 6.5 KB
  148. 3. Hypothesis testing checklist.srt 6.5 KB
  149. 2. How to visualize a classification tree in Python.srt 6.6 KB
  150. 6. Wordle and Bayes' theorem.srt 6.6 KB
  151. 1. What are association rules.srt 6.6 KB
  152. 1. Judea Pearl and the causal revolution.srt 6.6 KB
  153. 3. Popper on induction and falsification.srt 6.7 KB
  154. 1. What are induction and deduction.srt 6.7 KB
  155. 4. Applying the two methods at work.srt 6.7 KB
  156. 3. The Apriori algorithm.srt 6.8 KB
  157. 3. Comparing IML and XAI.srt 6.8 KB
  158. 2. Making predictions with logistic regression.srt 6.8 KB
  159. 4. Wordle and conditional probability.srt 6.8 KB
  160. 1. Tuning exercise Problem statement.srt 6.8 KB
  161. 1. Understanding the what and why your models predict.srt 6.9 KB
  162. 1. Contrasting frequentist statistics and Bayesian statistics.srt 7.0 KB
  163. 3. How to prune a classification tree in Python.srt 7.1 KB
  164. 2. TrainTest What can go wrong.srt 7.2 KB
  165. 1. What is a decision tree.srt 7.3 KB
  166. Ex_Files_ML_with_Python_k_Means_Clustering.zip 7.3 KB
  167. 1. Lady tasting tea.srt 7.4 KB
  168. 2. Pearson on correlation and causation.srt 7.4 KB
  169. 2. Explain vs. predict.srt 7.4 KB
  170. 3. Correlation and regression.srt 7.5 KB
  171. 3. How to build a logistic regression model in Python.srt 7.7 KB
  172. 3. Comparing CRISP-DM and the scientific method.srt 7.8 KB
  173. 1. The Two Cultures.srt 7.9 KB
  174. 4. How to interpret the results of k-means clustering in Python.srt 8.0 KB
  175. 3. How to find the right number of clusters in Python.srt 8.0 KB
  176. 3. How CART handles missing data using surrogates.srt 8.0 KB
  177. 2. Fisher and experiments.srt 8.1 KB
  178. 1. What is clustering.srt 8.1 KB
  179. 2. The pros and cons of decision trees.srt 8.1 KB
  180. 2. How to visualize a regression tree in Python.srt 8.1 KB
  181. 3. How to prune a regression tree in Python.srt 8.2 KB
  182. 4. How is a regression tree built.srt 8.3 KB
  183. 4. Trends in AI making the XAI problem more prominent.srt 8.4 KB
  184. 1. Data mining vs. data dredging.srt 8.5 KB
  185. 12. When to turn off pruning.srt 8.6 KB
  186. 1. Turing, Enigma, and CAPTCHA.srt 8.6 KB
  187. 3. Common types of regression.srt 8.8 KB
  188. 5. Working with the prebuilt example.srt 8.8 KB
  189. 3. How do classification trees measure impurity.srt 8.8 KB
  190. 1. How to build a classification tree in Python.srt 8.9 KB
  191. 2. Understanding the entropy calculation.srt 9.1 KB
  192. 2. How to prepare data for logistic regression in Python.srt 9.3 KB
  193. 4. Introduction to causal modeling with Bayesian networks.srt 9.4 KB
  194. 2. How is a classification tree built.srt 9.5 KB
  195. 4. Using GitHub Codespaces with this course.srt 9.5 KB
  196. 1. What is logistic regression.srt 9.8 KB
  197. 7. Moderation, mediation, and lurking variables.srt 9.8 KB
  198. 6. Solution Evaluate significant finding.srt 9.9 KB
  199. 1. What is a strong correlation.srt 10.2 KB
  200. 4. A quick review of machine learning basics with examples.srt 10.4 KB
  201. 2. Frequent itemset generation.srt 10.4 KB
  202. 4. Using GitHub Codespaces with this course.srt 10.6 KB
  203. 3. Interpreting the coefficients of logistic regression.srt 10.7 KB
  204. Ex_Files_Machine_Learning_with_Python_Decision_Trees.zip 10.8 KB
  205. 4. The FP-Growth algorithm.srt 10.9 KB
  206. 5. How to prune a decision tree.srt 11.0 KB
  207. 2. How to generate frequent itemsets.srt 11.0 KB
  208. 1. How to build a regression tree in Python.srt 11.0 KB
  209. 5. Evaluating association rules.srt 11.5 KB
  210. 5. Solution What is causing what.srt 11.7 KB
  211. 1. How to segment data with k-means clustering in Python.srt 11.8 KB
  212. 1. How to collect data for association rule mining.srt 11.8 KB
  213. 3. John Snow and natural experiments.srt 12.2 KB
  214. 3. Developing an intuition for Bayes with Wordle.srt 12.6 KB
  215. 4. How to interpret a logistic regression model in Python.srt 12.7 KB
  216. 3. Choosing the right number of clusters.srt 12.9 KB
  217. 1. Using probability to measure uncertainty.srt 13.0 KB
  218. 3. How to create association rules.srt 13.3 KB
  219. 8. Simpson's paradox.srt 13.7 KB
  220. 4. How to evaluate association rules.srt 15.6 KB
  221. 5. Control variables (ANCOVA).srt 15.7 KB
  222. 1. How to explore data for logistic regression in Python.srt 19.3 KB
  223. 2. Bayesian T-Test with JASP.srt 19.5 KB
  224. Ex_Files_ML_and_AI_Foundations.zip 138.1 KB
  225. Ex_Files_ML_and_AI_Foundations_Causal_Inf_Modeling.zip 179.8 KB
  226. Ex_Files_Deep_Learning_Model_Optimization_Tuning.zip 725.9 KB
  227. 1. Next steps.mp4 1.7 MB
  228. 2. Regularization.mp4 1.8 MB
  229. 3. The tools you need.mp4 1.8 MB
  230. 4. Dropouts.mp4 1.8 MB
  231. 2. What you should know.mp4 2.0 MB
  232. 3. The tools you need.mp4 2.0 MB
  233. 2. What you should know.mp4 2.0 MB
  234. 1. Continuing your deep learning journey.mp4 2.1 MB
  235. 2. What you should know.mp4 2.2 MB
  236. 2. What you should know.mp4 2.2 MB
  237. 2. What you should know.mp4 2.3 MB
  238. Ex_Files_ML_and_AI_Foundations_Decision_Trees_KNIME.zip 2.3 MB
  239. 3. What you should know.mp4 2.3 MB
  240. 3. Regularization experiment.mp4 2.4 MB
  241. 5. Learning rate.mp4 2.4 MB
  242. 3. Optimizers.mp4 2.8 MB
  243. 5. Avoiding overfitting.mp4 2.9 MB
  244. 2. Target audience.mp4 3.0 MB
  245. 4. Tuning backpropagation.mp4 3.1 MB
  246. 1. Next steps with decision trees.mp4 3.1 MB
  247. 2. What you should know.mp4 3.2 MB
  248. 1. Next steps.mp4 3.2 MB
  249. 2. Why causation matters in a business setting.mp4 3.3 MB
  250. 3. An ANN model.mp4 3.4 MB
  251. 1. What is deep learning.mp4 3.4 MB
  252. 7. Evaluating the accuracy of your CART tree.mp4 3.4 MB
  253. 2. p-value review.mp4 3.4 MB
  254. 5. Dropout experiment.mp4 3.4 MB
  255. 1. Review.mp4 3.4 MB
  256. 4. Model optimization and tuning.mp4 3.5 MB
  257. 1. Overfitting in ANNs.mp4 3.5 MB
  258. 3. Using the exercise files.mp4 3.5 MB
  259. 1. Epoch and batch size tuning.mp4 3.6 MB
  260. 1. Next steps.mp4 3.7 MB
  261. 2. Acquire and process data.mp4 3.7 MB
  262. 1. Next steps.mp4 3.8 MB
  263. 7. Challenge Conditional probability and Bayes' theorem.mp4 3.8 MB
  264. 3. Tuning the network.mp4 3.9 MB
  265. 1. Making decisions with Python.mp4 3.9 MB
  266. 6. Building the final model.mp4 4.0 MB
  267. 3. The math behind regression trees.mp4 4.0 MB
  268. 6. Learning rate experiment.mp4 4.1 MB
  269. 1. Getting started with Python and k-means clustering.mp4 4.1 MB
  270. 8. How C4.5 handles continuous variables.mp4 4.2 MB
  271. 3. Using the exercise files.mp4 4.4 MB
  272. 1. MPG data set.mp4 4.5 MB
  273. 3. How to use the practice files.mp4 4.5 MB
  274. 4. Optimizer experiment.mp4 4.6 MB
  275. 5. How CART handles nominal variables.mp4 4.6 MB
  276. 2. Prerequisites for the course.mp4 4.7 MB
  277. 1. Optimizing neural networks.mp4 4.7 MB
  278. 5. Challenge Evaluate significant finding.mp4 4.8 MB
  279. 6. Initializing weights.mp4 4.8 MB
  280. 1. Exploring the world of explainable AI and interpretable machine learning.mp4 5.0 MB
  281. 5. Counterfactuals Pearl on induction and causality.mp4 5.1 MB
  282. 1. Vanishing and exploding gradients.mp4 5.2 MB
  283. 1. Taking causality further.mp4 5.2 MB
  284. 5. Local and global explanations.mp4 5.3 MB
  285. 4. Challenge What is causing what.mp4 5.4 MB
  286. 7. KNIME support of global and local explanations.mp4 5.4 MB
  287. 4. Double blind studies.mp4 5.4 MB
  288. 3. Hidden layers tuning.mp4 5.5 MB
  289. 2. Review of artificial neural networks.mp4 5.6 MB
  290. 5. Choosing activation functions.mp4 5.6 MB
  291. 1. Ross Quinlan, ID3, C4.5, and C5.0.mp4 5.7 MB
  292. 4. Determining nodes in a layer.mp4 5.8 MB
  293. 9. Challenge Moderation, mediation, or a third variable.mp4 5.9 MB
  294. 3. Setting up exercise files.mp4 5.9 MB
  295. 3. How C4.5 handles missing data.mp4 6.0 MB
  296. 5. Challenge JASP.mp4 6.0 MB
  297. 3. AB testing during the evaluation phase.mp4 6.1 MB
  298. 1. Prediction, causation, and statistical inference.mp4 6.1 MB
  299. 3. What is a causal model.mp4 6.1 MB
  300. 4. Why and when to use logistic regression.mp4 6.2 MB
  301. 8. Solution Conditional probability and Bayes' theorem.mp4 6.2 MB
  302. 5. The deep learning tuning process.mp4 6.2 MB
  303. 1. Classifying data with logistic regression.mp4 6.3 MB
  304. 9. Equal size sampling.mp4 6.4 MB
  305. 10. A quick look at the complete C4.5 tree.mp4 6.4 MB
  306. 2. Batch normalization.mp4 6.5 MB
  307. 2. Introducing path analysis and SEM.mp4 6.6 MB
  308. 9. Accuracy.mp4 6.6 MB
  309. 6. Finding direction of causality with SEM (PSAT).mp4 6.7 MB
  310. 2. What is k-means clustering.mp4 6.7 MB
  311. 1. Skepticism about data Truman 1948 Election Poll.mp4 6.9 MB
  312. 2. What is the Gini coefficient.mp4 7.0 MB
  313. 6. XAI for debugging models.mp4 7.0 MB
  314. 6. A quick look at the complete CART tree.mp4 7.2 MB
  315. 1. The basics of decision trees.mp4 7.2 MB
  316. 1. What is a decision tree.mp4 7.2 MB
  317. 3. SEM example Intention.mp4 7.3 MB
  318. 5. Latent variables in SEM.mp4 7.3 MB
  319. 7. How C4.5 handles nominal variables.mp4 7.4 MB
  320. 7. KNIME's missing data options for regression trees.mp4 7.7 MB
  321. 4. Using the exercise files.mp4 7.7 MB
  322. 3. Hypothesis testing checklist.mp4 7.7 MB
  323. 4. Changing the settings in KNIME.mp4 7.8 MB
  324. 1. Association rule mining.mp4 7.8 MB
  325. 4. Using the exercise files.mp4 7.8 MB
  326. 8. Line plot.mp4 7.9 MB
  327. 4. The Give Me Some Credit data set.mp4 7.9 MB
  328. 4. Wordle and conditional probability.mp4 8.1 MB
  329. 6. Wordle and Bayes' theorem.mp4 8.3 MB
  330. 1. Thinking about causality.mp4 8.4 MB
  331. 3. Skepticism about causes Is X really causing Y.mp4 8.5 MB
  332. 1. Judea Pearl and the causal revolution.mp4 8.6 MB
  333. 6. KNIME settings for C4.5.mp4 8.6 MB
  334. 6. Experiment setups for the course.mp4 8.9 MB
  335. 6. Closer look at a full regression tree.mp4 9.1 MB
  336. 1. Tuning exercise Problem statement.mp4 9.1 MB
  337. 2. Variable importance and reason codes.mp4 9.2 MB
  338. 11. Evaluating the accuracy of your C4.5 tree.mp4 9.3 MB
  339. 10. Solution Moderation, mediation, or a third variable.mp4 9.5 MB
  340. 4. Myths about SEM.mp4 9.6 MB
  341. 1. What is a decision tree.mp4 9.6 MB
  342. 6. Judea Pearl Problems with control variables.mp4 9.7 MB
  343. 3. How CART handles missing data using surrogates.mp4 9.8 MB
  344. 2. Epoch and batch size experiment.mp4 9.9 MB
  345. 4. Why and when to use k-means clustering.mp4 10.0 MB
  346. 2. The anatomy of a regression model.mp4 10.1 MB
  347. 2. The pros and cons of decision trees.mp4 10.1 MB
  348. 5. Ordinal variable handling.mp4 10.1 MB
  349. 2. TrainTest What can go wrong.mp4 10.1 MB
  350. 4. Taleb on induction.mp4 10.2 MB
  351. 3. Popper on induction and falsification.mp4 10.2 MB
  352. 1. What is regression.mp4 10.2 MB
  353. 3. Comparing IML and XAI.mp4 10.5 MB
  354. 3. Introducing BayesiaLab Hair and eye color.mp4 10.5 MB
  355. 2. Skepticism about results Is that really the best predictor.mp4 10.5 MB
  356. 5. Wordle, bans, and bits.mp4 10.6 MB
  357. 1. The investigator, the jury, and the judge.mp4 10.6 MB
  358. 2. How to evaluate and visualize clusters in Python.mp4 10.7 MB
  359. 2. Making predictions with logistic regression.mp4 10.8 MB
  360. 2. Downloading BayesiaLab and resources.mp4 10.9 MB
  361. 2. Hume on induction.mp4 11.0 MB
  362. 4. How RT handles nominal variables.mp4 11.1 MB
  363. 2. Pearson on correlation and causation.mp4 11.2 MB
  364. 3. Comparing CRISP-DM and the scientific method.mp4 11.2 MB
  365. 2. How to visualize a classification tree in Python.mp4 11.3 MB
  366. 1. What is clustering.mp4 11.5 MB
  367. 1. Introducing Leo Breiman and CART.mp4 11.6 MB
  368. 2. Understanding the entropy calculation.mp4 11.7 MB
  369. 3. Google Optimize.mp4 11.7 MB
  370. 4. How is a regression tree built.mp4 11.8 MB
  371. 1. The Two Cultures.mp4 12.0 MB
  372. 2. The regression tree prebuilt example.mp4 12.0 MB
  373. 6. Solution JASP.mp4 12.1 MB
  374. 6. Why and when to use association rules.mp4 12.2 MB
  375. 2. Explain vs. predict.mp4 12.3 MB
  376. 2. How to visualize a regression tree in Python.mp4 12.4 MB
  377. 2. How is a classification tree built.mp4 12.4 MB
  378. 3. Correlation and regression.mp4 12.5 MB
  379. 5. An overview of decision tree algorithms.mp4 12.5 MB
  380. 1. What is logistic regression.mp4 12.5 MB
  381. 1. Data mining vs. data dredging.mp4 12.6 MB
  382. 3. How to prune a classification tree in Python.mp4 12.7 MB
  383. 3. Introducing KNIME.mp4 12.8 MB
  384. 3. How do classification trees measure impurity.mp4 12.9 MB
  385. 1. Lady tasting tea.mp4 12.9 MB
  386. 4. Taleb on normality, mediocristan, and extremistan.mp4 12.9 MB
  387. 6. Solution Evaluate significant finding.mp4 13.0 MB
  388. 1. Contrasting frequentist statistics and Bayesian statistics.mp4 13.1 MB
  389. 3. Developing an intuition for Bayes with Wordle.mp4 13.1 MB
  390. 3. Interpreting the coefficients of logistic regression.mp4 13.4 MB
  391. 3. How to find the right number of clusters in Python.mp4 13.7 MB
  392. 6. Why and when to use a decision tree.mp4 13.7 MB
  393. 1. What are association rules.mp4 13.8 MB
  394. 5. Bayesian Networks Black Swan case study.mp4 14.5 MB
  395. 1. What are induction and deduction.mp4 14.6 MB
  396. 7. Moderation, mediation, and lurking variables.mp4 15.1 MB
  397. 4. Applying the two methods at work.mp4 15.1 MB
  398. 4. How to interpret the results of k-means clustering in Python.mp4 15.1 MB
  399. 3. How to prune a regression tree in Python.mp4 15.7 MB
  400. 3. The Apriori algorithm.mp4 15.7 MB
  401. 1. How to build a classification tree in Python.mp4 15.7 MB
  402. 5. Working with the prebuilt example.mp4 15.9 MB
  403. 4. Introduction to causal modeling with Bayesian networks.mp4 16.1 MB
  404. 3. Common types of regression.mp4 16.3 MB
  405. 1. Understanding the what and why your models predict.mp4 16.4 MB
  406. 12. When to turn off pruning.mp4 16.4 MB
  407. 2. Frequent itemset generation.mp4 16.9 MB
  408. 4. Bayes and rare events.mp4 17.0 MB
  409. 2. Enigma and uncertainty.mp4 17.1 MB
  410. 3. Choosing the right number of clusters.mp4 17.4 MB
  411. 3. How to build a logistic regression model in Python.mp4 17.8 MB
  412. 1. Sewell Wright.mp4 18.2 MB
  413. 4. Trends in AI making the XAI problem more prominent.mp4 18.3 MB
  414. 5. How to prune a decision tree.mp4 19.1 MB
  415. 1. How to build a regression tree in Python.mp4 20.1 MB
  416. 4. A quick review of machine learning basics with examples.mp4 20.3 MB
  417. 2. Fisher and experiments.mp4 20.6 MB
  418. 5. Evaluating association rules.mp4 21.1 MB
  419. 5. Solution What is causing what.mp4 21.1 MB
  420. 1. What is a strong correlation.mp4 21.2 MB
  421. 4. Using GitHub Codespaces with this course.mp4 21.6 MB
  422. 4. Using GitHub Codespaces with this course.mp4 21.6 MB
  423. 2. How to prepare data for logistic regression in Python.mp4 21.9 MB
  424. 1. Using probability to measure uncertainty.mp4 22.2 MB
  425. 1. How to segment data with k-means clustering in Python.mp4 23.6 MB
  426. 5. Control variables (ANCOVA).mp4 23.8 MB
  427. 1. Turing, Enigma, and CAPTCHA.mp4 24.1 MB
  428. 8. Simpson's paradox.mp4 26.0 MB
  429. 4. The FP-Growth algorithm.mp4 26.5 MB
  430. 1. How to collect data for association rule mining.mp4 27.4 MB
  431. 4. How to interpret a logistic regression model in Python.mp4 28.3 MB
  432. 2. How to generate frequent itemsets.mp4 31.1 MB
  433. 2. Bayesian T-Test with JASP.mp4 33.6 MB
  434. 1. How to explore data for logistic regression in Python.mp4 36.1 MB
  435. 3. John Snow and natural experiments.mp4 36.7 MB
  436. 3. How to create association rules.mp4 43.0 MB
  437. 4. How to evaluate association rules.mp4 44.0 MB

Discussion