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

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