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Language:
English
Total Size:
2.1 GB
Info Hash:
668D0E57BE129CBEB72FC3AA626D34B769B535B0
Added By:
Added:
Aug. 26, 2024, 11:11 a.m.
Stats:
|
(Last updated: Nov. 29, 2025, 4:40 a.m.)
| File | Size |
|---|---|
| Exercises_Link - OneHack.us.txt | 121 bytes |
| Exercises_Link.txt | 123 bytes |
| $10 ChatGPT for 1 Year & More.txt | 252 bytes |
| description.html | 1006 bytes |
| description.html | 1015 bytes |
| description.html | 1.1 KB |
| description.html | 1.1 KB |
| description.html | 1.1 KB |
| 1. Continuing your deep learning journey.srt | 1.2 KB |
| description.html | 1.2 KB |
| 1. Making decisions with Python.srt | 1.3 KB |
| 1. Getting started with Python and k-means clustering.srt | 1.3 KB |
| description.html | 1.3 KB |
| description.html | 1.3 KB |
| 4. Tuning backpropagation.srt | 1.3 KB |
| 1. Optimizing neural networks.srt | 1.4 KB |
| 3. Regularization experiment.srt | 1.4 KB |
| 2. Regularization.srt | 1.4 KB |
| 5. Avoiding overfitting.srt | 1.4 KB |
| 5. Dropout experiment.srt | 1.5 KB |
| 2. Acquire and process data.srt | 1.5 KB |
| 1. Exploring the world of explainable AI and interpretable machine learning.srt | 1.6 KB |
| 2. What you should know.srt | 1.6 KB |
| 3. What you should know.srt | 1.6 KB |
| 1. Next steps.srt | 1.6 KB |
| 1. Review.srt | 1.7 KB |
| 1. Classifying data with logistic regression.srt | 1.8 KB |
| 4. Dropouts.srt | 1.8 KB |
| 1. Association rule mining.srt | 1.9 KB |
| 2. What you should know.srt | 1.9 KB |
| 1. MPG data set.srt | 1.9 KB |
| 6. Learning rate experiment.srt | 1.9 KB |
| 2. What you should know.srt | 1.9 KB |
| 2. What you should know.srt | 2.0 KB |
| 3. Tuning the network.srt | 2.0 KB |
| 2. p-value review.srt | 2.0 KB |
| 2. What you should know.srt | 2.0 KB |
| 7. Evaluating the accuracy of your CART tree.srt | 2.0 KB |
| 5. Learning rate.srt | 2.0 KB |
| 3. The tools you need.srt | 2.0 KB |
| 3. The tools you need.srt | 2.1 KB |
| 2. Why causation matters in a business setting.srt | 2.1 KB |
| 3. Using the exercise files.srt | 2.1 KB |
| 1. The basics of decision trees.srt | 2.1 KB |
| 2. Target audience.srt | 2.1 KB |
| 3. Using the exercise files.srt | 2.2 KB |
| 4. Optimizer experiment.srt | 2.2 KB |
| 1. Prediction, causation, and statistical inference.srt | 2.2 KB |
| 3. How to use the practice files.srt | 2.2 KB |
| 6. Building the final model.srt | 2.3 KB |
| 8. How C4.5 handles continuous variables.srt | 2.3 KB |
| 7. Challenge Conditional probability and Bayes' theorem.srt | 2.4 KB |
| 2. What you should know.srt | 2.4 KB |
| 4. Using the exercise files.srt | 2.5 KB |
| 3. Optimizers.srt | 2.5 KB |
| 3. An ANN model.srt | 2.5 KB |
| 4. Model optimization and tuning.srt | 2.5 KB |
| 5. Challenge Evaluate significant finding.srt | 2.6 KB |
| 5. How CART handles nominal variables.srt | 2.6 KB |
| 4. Using the exercise files.srt | 2.7 KB |
| 1. Thinking about causality.srt | 2.7 KB |
| 1. What is deep learning.srt | 2.7 KB |
| 4. Challenge What is causing what.srt | 2.8 KB |
| 4. Why and when to use logistic regression.srt | 2.9 KB |
| 4. Double blind studies.srt | 2.9 KB |
| 6. Initializing weights.srt | 2.9 KB |
| 5. Challenge JASP.srt | 2.9 KB |
| 1. Next steps with decision trees.srt | 3.0 KB |
| 1. Next steps.srt | 3.0 KB |
| 2. Batch normalization.srt | 3.2 KB |
| 1. Overfitting in ANNs.srt | 3.3 KB |
| 9. Equal size sampling.srt | 3.3 KB |
| 3. What is a causal model.srt | 3.3 KB |
| 1. Next steps.srt | 3.3 KB |
| 3. Hidden layers tuning.srt | 3.3 KB |
| 1. Epoch and batch size tuning.srt | 3.4 KB |
| 6. Experiment setups for the course.srt | 3.4 KB |
| 5. Choosing activation functions.srt | 3.4 KB |
| 1. Next steps.srt | 3.4 KB |
| 9. Challenge Moderation, mediation, or a third variable.srt | 3.4 KB |
| 3. Setting up exercise files.srt | 3.5 KB |
| 2. Variable importance and reason codes.srt | 3.5 KB |
| 4. Determining nodes in a layer.srt | 3.5 KB |
| 7. KNIME support of global and local explanations.srt | 3.6 KB |
| 9. Accuracy.srt | 3.6 KB |
| 2. Downloading BayesiaLab and resources.srt | 3.6 KB |
| 3. The math behind regression trees.srt | 3.6 KB |
| 6. XAI for debugging models.srt | 3.6 KB |
| 1. Ross Quinlan, ID3, C4.5, and C5.0.srt | 3.6 KB |
| 6. A quick look at the complete CART tree.srt | 3.6 KB |
| 7. How C4.5 handles nominal variables.srt | 3.6 KB |
| 4. Taleb on normality, mediocristan, and extremistan.srt | 3.7 KB |
| 5. Local and global explanations.srt | 3.7 KB |
| 5. Counterfactuals Pearl on induction and causality.srt | 3.8 KB |
| 8. Line plot.srt | 3.8 KB |
| 8. Solution Conditional probability and Bayes' theorem.srt | 4.0 KB |
| 2. What is the Gini coefficient.srt | 4.0 KB |
| 6. Why and when to use association rules.srt | 4.1 KB |
| 3. AB testing during the evaluation phase.srt | 4.2 KB |
| 1. Vanishing and exploding gradients.srt | 4.2 KB |
| 10. A quick look at the complete C4.5 tree.srt | 4.3 KB |
| 6. Judea Pearl Problems with control variables.srt | 4.4 KB |
| 2. Introducing path analysis and SEM.srt | 4.4 KB |
| 2. Review of artificial neural networks.srt | 4.4 KB |
| 1. Skepticism about data Truman 1948 Election Poll.srt | 4.4 KB |
| 1. Taking causality further.srt | 4.4 KB |
| 11. Evaluating the accuracy of your C4.5 tree.srt | 4.4 KB |
| 3. How C4.5 handles missing data.srt | 4.4 KB |
| 5. Latent variables in SEM.srt | 4.5 KB |
| 7. KNIME's missing data options for regression trees.srt | 4.5 KB |
| 4. Changing the settings in KNIME.srt | 4.5 KB |
| 3. Skepticism about causes Is X really causing Y.srt | 4.5 KB |
| 2. Prerequisites for the course.srt | 4.6 KB |
| 4. Why and when to use k-means clustering.srt | 4.6 KB |
| 4. The Give Me Some Credit data set.srt | 4.6 KB |
| 6. KNIME settings for C4.5.srt | 4.9 KB |
| 1. What is a decision tree.srt | 4.9 KB |
| 1. The investigator, the jury, and the judge.srt | 5.0 KB |
| 6. Why and when to use a decision tree.srt | 5.0 KB |
| 5. Bayesian Networks Black Swan case study.srt | 5.0 KB |
| 2. Epoch and batch size experiment.srt | 5.1 KB |
| 5. The deep learning tuning process.srt | 5.2 KB |
| 6. Finding direction of causality with SEM (PSAT).srt | 5.3 KB |
| 6. Closer look at a full regression tree.srt | 5.3 KB |
| 1. What is regression.srt | 5.3 KB |
| 3. Google Optimize.srt | 5.4 KB |
| 5. Ordinal variable handling.srt | 5.4 KB |
| 2. Enigma and uncertainty.srt | 5.7 KB |
| 10. Solution Moderation, mediation, or a third variable.srt | 5.7 KB |
| 2. How to evaluate and visualize clusters in Python.srt | 5.7 KB |
| 5. An overview of decision tree algorithms.srt | 5.8 KB |
| 2. Hume on induction.srt | 5.8 KB |
| 2. Skepticism about results Is that really the best predictor.srt | 5.8 KB |
| 1. Introducing Leo Breiman and CART.srt | 5.9 KB |
| 3. Introducing KNIME.srt | 6.0 KB |
| 2. What is k-means clustering.srt | 6.1 KB |
| 3. SEM example Intention.srt | 6.2 KB |
| 4. Myths about SEM.srt | 6.2 KB |
| 4. Bayes and rare events.srt | 6.2 KB |
| 3. Introducing BayesiaLab Hair and eye color.srt | 6.3 KB |
| 2. The anatomy of a regression model.srt | 6.3 KB |
| 2. The regression tree prebuilt example.srt | 6.3 KB |
| 6. Solution JASP.srt | 6.4 KB |
| 1. Sewell Wright.srt | 6.5 KB |
| 4. How RT handles nominal variables.srt | 6.5 KB |
| 4. Taleb on induction.srt | 6.5 KB |
| 5. Wordle, bans, and bits.srt | 6.5 KB |
| 3. Hypothesis testing checklist.srt | 6.5 KB |
| 2. How to visualize a classification tree in Python.srt | 6.6 KB |
| 6. Wordle and Bayes' theorem.srt | 6.6 KB |
| 1. What are association rules.srt | 6.6 KB |
| 1. Judea Pearl and the causal revolution.srt | 6.6 KB |
| 3. Popper on induction and falsification.srt | 6.7 KB |
| 1. What are induction and deduction.srt | 6.7 KB |
| 4. Applying the two methods at work.srt | 6.7 KB |
| 3. The Apriori algorithm.srt | 6.8 KB |
| 3. Comparing IML and XAI.srt | 6.8 KB |
| 2. Making predictions with logistic regression.srt | 6.8 KB |
| 4. Wordle and conditional probability.srt | 6.8 KB |
| 1. Tuning exercise Problem statement.srt | 6.8 KB |
| 1. Understanding the what and why your models predict.srt | 6.9 KB |
| 1. Contrasting frequentist statistics and Bayesian statistics.srt | 7.0 KB |
| 3. How to prune a classification tree in Python.srt | 7.1 KB |
| 2. TrainTest What can go wrong.srt | 7.2 KB |
| 1. What is a decision tree.srt | 7.3 KB |
| Ex_Files_ML_with_Python_k_Means_Clustering.zip | 7.3 KB |
| 1. Lady tasting tea.srt | 7.4 KB |
| 2. Pearson on correlation and causation.srt | 7.4 KB |
| 2. Explain vs. predict.srt | 7.4 KB |
| 3. Correlation and regression.srt | 7.5 KB |
| 3. How to build a logistic regression model in Python.srt | 7.7 KB |
| 3. Comparing CRISP-DM and the scientific method.srt | 7.8 KB |
| 1. The Two Cultures.srt | 7.9 KB |
| 4. How to interpret the results of k-means clustering in Python.srt | 8.0 KB |
| 3. How to find the right number of clusters in Python.srt | 8.0 KB |
| 3. How CART handles missing data using surrogates.srt | 8.0 KB |
| 2. Fisher and experiments.srt | 8.1 KB |
| 1. What is clustering.srt | 8.1 KB |
| 2. The pros and cons of decision trees.srt | 8.1 KB |
| 2. How to visualize a regression tree in Python.srt | 8.1 KB |
| 3. How to prune a regression tree in Python.srt | 8.2 KB |
| 4. How is a regression tree built.srt | 8.3 KB |
| 4. Trends in AI making the XAI problem more prominent.srt | 8.4 KB |
| 1. Data mining vs. data dredging.srt | 8.5 KB |
| 12. When to turn off pruning.srt | 8.6 KB |
| 1. Turing, Enigma, and CAPTCHA.srt | 8.6 KB |
| 3. Common types of regression.srt | 8.8 KB |
| 5. Working with the prebuilt example.srt | 8.8 KB |
| 3. How do classification trees measure impurity.srt | 8.8 KB |
| 1. How to build a classification tree in Python.srt | 8.9 KB |
| 2. Understanding the entropy calculation.srt | 9.1 KB |
| 2. How to prepare data for logistic regression in Python.srt | 9.3 KB |
| 4. Introduction to causal modeling with Bayesian networks.srt | 9.4 KB |
| 2. How is a classification tree built.srt | 9.5 KB |
| 4. Using GitHub Codespaces with this course.srt | 9.5 KB |
| 1. What is logistic regression.srt | 9.8 KB |
| 7. Moderation, mediation, and lurking variables.srt | 9.8 KB |
| 6. Solution Evaluate significant finding.srt | 9.9 KB |
| 1. What is a strong correlation.srt | 10.2 KB |
| 4. A quick review of machine learning basics with examples.srt | 10.4 KB |
| 2. Frequent itemset generation.srt | 10.4 KB |
| 4. Using GitHub Codespaces with this course.srt | 10.6 KB |
| 3. Interpreting the coefficients of logistic regression.srt | 10.7 KB |
| Ex_Files_Machine_Learning_with_Python_Decision_Trees.zip | 10.8 KB |
| 4. The FP-Growth algorithm.srt | 10.9 KB |
| 5. How to prune a decision tree.srt | 11.0 KB |
| 2. How to generate frequent itemsets.srt | 11.0 KB |
| 1. How to build a regression tree in Python.srt | 11.0 KB |
| 5. Evaluating association rules.srt | 11.5 KB |
| 5. Solution What is causing what.srt | 11.7 KB |
| 1. How to segment data with k-means clustering in Python.srt | 11.8 KB |
| 1. How to collect data for association rule mining.srt | 11.8 KB |
| 3. John Snow and natural experiments.srt | 12.2 KB |
| 3. Developing an intuition for Bayes with Wordle.srt | 12.6 KB |
| 4. How to interpret a logistic regression model in Python.srt | 12.7 KB |
| 3. Choosing the right number of clusters.srt | 12.9 KB |
| 1. Using probability to measure uncertainty.srt | 13.0 KB |
| 3. How to create association rules.srt | 13.3 KB |
| 8. Simpson's paradox.srt | 13.7 KB |
| 4. How to evaluate association rules.srt | 15.6 KB |
| 5. Control variables (ANCOVA).srt | 15.7 KB |
| 1. How to explore data for logistic regression in Python.srt | 19.3 KB |
| 2. Bayesian T-Test with JASP.srt | 19.5 KB |
| Ex_Files_ML_and_AI_Foundations.zip | 138.1 KB |
| Ex_Files_ML_and_AI_Foundations_Causal_Inf_Modeling.zip | 179.8 KB |
| Ex_Files_Deep_Learning_Model_Optimization_Tuning.zip | 725.9 KB |
| 1. Next steps.mp4 | 1.7 MB |
| 2. Regularization.mp4 | 1.8 MB |
| 3. The tools you need.mp4 | 1.8 MB |
| 4. Dropouts.mp4 | 1.8 MB |
| 2. What you should know.mp4 | 2.0 MB |
| 3. The tools you need.mp4 | 2.0 MB |
| 2. What you should know.mp4 | 2.0 MB |
| 1. Continuing your deep learning journey.mp4 | 2.1 MB |
| 2. What you should know.mp4 | 2.2 MB |
| 2. What you should know.mp4 | 2.2 MB |
| 2. What you should know.mp4 | 2.3 MB |
| Ex_Files_ML_and_AI_Foundations_Decision_Trees_KNIME.zip | 2.3 MB |
| 3. What you should know.mp4 | 2.3 MB |
| 3. Regularization experiment.mp4 | 2.4 MB |
| 5. Learning rate.mp4 | 2.4 MB |
| 3. Optimizers.mp4 | 2.8 MB |
| 5. Avoiding overfitting.mp4 | 2.9 MB |
| 2. Target audience.mp4 | 3.0 MB |
| 4. Tuning backpropagation.mp4 | 3.1 MB |
| 1. Next steps with decision trees.mp4 | 3.1 MB |
| 2. What you should know.mp4 | 3.2 MB |
| 1. Next steps.mp4 | 3.2 MB |
| 2. Why causation matters in a business setting.mp4 | 3.3 MB |
| 3. An ANN model.mp4 | 3.4 MB |
| 1. What is deep learning.mp4 | 3.4 MB |
| 7. Evaluating the accuracy of your CART tree.mp4 | 3.4 MB |
| 2. p-value review.mp4 | 3.4 MB |
| 5. Dropout experiment.mp4 | 3.4 MB |
| 1. Review.mp4 | 3.4 MB |
| 4. Model optimization and tuning.mp4 | 3.5 MB |
| 1. Overfitting in ANNs.mp4 | 3.5 MB |
| 3. Using the exercise files.mp4 | 3.5 MB |
| 1. Epoch and batch size tuning.mp4 | 3.6 MB |
| 1. Next steps.mp4 | 3.7 MB |
| 2. Acquire and process data.mp4 | 3.7 MB |
| 1. Next steps.mp4 | 3.8 MB |
| 7. Challenge Conditional probability and Bayes' theorem.mp4 | 3.8 MB |
| 3. Tuning the network.mp4 | 3.9 MB |
| 1. Making decisions with Python.mp4 | 3.9 MB |
| 6. Building the final model.mp4 | 4.0 MB |
| 3. The math behind regression trees.mp4 | 4.0 MB |
| 6. Learning rate experiment.mp4 | 4.1 MB |
| 1. Getting started with Python and k-means clustering.mp4 | 4.1 MB |
| 8. How C4.5 handles continuous variables.mp4 | 4.2 MB |
| 3. Using the exercise files.mp4 | 4.4 MB |
| 1. MPG data set.mp4 | 4.5 MB |
| 3. How to use the practice files.mp4 | 4.5 MB |
| 4. Optimizer experiment.mp4 | 4.6 MB |
| 5. How CART handles nominal variables.mp4 | 4.6 MB |
| 2. Prerequisites for the course.mp4 | 4.7 MB |
| 1. Optimizing neural networks.mp4 | 4.7 MB |
| 5. Challenge Evaluate significant finding.mp4 | 4.8 MB |
| 6. Initializing weights.mp4 | 4.8 MB |
| 1. Exploring the world of explainable AI and interpretable machine learning.mp4 | 5.0 MB |
| 5. Counterfactuals Pearl on induction and causality.mp4 | 5.1 MB |
| 1. Vanishing and exploding gradients.mp4 | 5.2 MB |
| 1. Taking causality further.mp4 | 5.2 MB |
| 5. Local and global explanations.mp4 | 5.3 MB |
| 4. Challenge What is causing what.mp4 | 5.4 MB |
| 7. KNIME support of global and local explanations.mp4 | 5.4 MB |
| 4. Double blind studies.mp4 | 5.4 MB |
| 3. Hidden layers tuning.mp4 | 5.5 MB |
| 2. Review of artificial neural networks.mp4 | 5.6 MB |
| 5. Choosing activation functions.mp4 | 5.6 MB |
| 1. Ross Quinlan, ID3, C4.5, and C5.0.mp4 | 5.7 MB |
| 4. Determining nodes in a layer.mp4 | 5.8 MB |
| 9. Challenge Moderation, mediation, or a third variable.mp4 | 5.9 MB |
| 3. Setting up exercise files.mp4 | 5.9 MB |
| 3. How C4.5 handles missing data.mp4 | 6.0 MB |
| 5. Challenge JASP.mp4 | 6.0 MB |
| 3. AB testing during the evaluation phase.mp4 | 6.1 MB |
| 1. Prediction, causation, and statistical inference.mp4 | 6.1 MB |
| 3. What is a causal model.mp4 | 6.1 MB |
| 4. Why and when to use logistic regression.mp4 | 6.2 MB |
| 8. Solution Conditional probability and Bayes' theorem.mp4 | 6.2 MB |
| 5. The deep learning tuning process.mp4 | 6.2 MB |
| 1. Classifying data with logistic regression.mp4 | 6.3 MB |
| 9. Equal size sampling.mp4 | 6.4 MB |
| 10. A quick look at the complete C4.5 tree.mp4 | 6.4 MB |
| 2. Batch normalization.mp4 | 6.5 MB |
| 2. Introducing path analysis and SEM.mp4 | 6.6 MB |
| 9. Accuracy.mp4 | 6.6 MB |
| 6. Finding direction of causality with SEM (PSAT).mp4 | 6.7 MB |
| 2. What is k-means clustering.mp4 | 6.7 MB |
| 1. Skepticism about data Truman 1948 Election Poll.mp4 | 6.9 MB |
| 2. What is the Gini coefficient.mp4 | 7.0 MB |
| 6. XAI for debugging models.mp4 | 7.0 MB |
| 6. A quick look at the complete CART tree.mp4 | 7.2 MB |
| 1. The basics of decision trees.mp4 | 7.2 MB |
| 1. What is a decision tree.mp4 | 7.2 MB |
| 3. SEM example Intention.mp4 | 7.3 MB |
| 5. Latent variables in SEM.mp4 | 7.3 MB |
| 7. How C4.5 handles nominal variables.mp4 | 7.4 MB |
| 7. KNIME's missing data options for regression trees.mp4 | 7.7 MB |
| 4. Using the exercise files.mp4 | 7.7 MB |
| 3. Hypothesis testing checklist.mp4 | 7.7 MB |
| 4. Changing the settings in KNIME.mp4 | 7.8 MB |
| 1. Association rule mining.mp4 | 7.8 MB |
| 4. Using the exercise files.mp4 | 7.8 MB |
| 8. Line plot.mp4 | 7.9 MB |
| 4. The Give Me Some Credit data set.mp4 | 7.9 MB |
| 4. Wordle and conditional probability.mp4 | 8.1 MB |
| 6. Wordle and Bayes' theorem.mp4 | 8.3 MB |
| 1. Thinking about causality.mp4 | 8.4 MB |
| 3. Skepticism about causes Is X really causing Y.mp4 | 8.5 MB |
| 1. Judea Pearl and the causal revolution.mp4 | 8.6 MB |
| 6. KNIME settings for C4.5.mp4 | 8.6 MB |
| 6. Experiment setups for the course.mp4 | 8.9 MB |
| 6. Closer look at a full regression tree.mp4 | 9.1 MB |
| 1. Tuning exercise Problem statement.mp4 | 9.1 MB |
| 2. Variable importance and reason codes.mp4 | 9.2 MB |
| 11. Evaluating the accuracy of your C4.5 tree.mp4 | 9.3 MB |
| 10. Solution Moderation, mediation, or a third variable.mp4 | 9.5 MB |
| 4. Myths about SEM.mp4 | 9.6 MB |
| 1. What is a decision tree.mp4 | 9.6 MB |
| 6. Judea Pearl Problems with control variables.mp4 | 9.7 MB |
| 3. How CART handles missing data using surrogates.mp4 | 9.8 MB |
| 2. Epoch and batch size experiment.mp4 | 9.9 MB |
| 4. Why and when to use k-means clustering.mp4 | 10.0 MB |
| 2. The anatomy of a regression model.mp4 | 10.1 MB |
| 2. The pros and cons of decision trees.mp4 | 10.1 MB |
| 5. Ordinal variable handling.mp4 | 10.1 MB |
| 2. TrainTest What can go wrong.mp4 | 10.1 MB |
| 4. Taleb on induction.mp4 | 10.2 MB |
| 3. Popper on induction and falsification.mp4 | 10.2 MB |
| 1. What is regression.mp4 | 10.2 MB |
| 3. Comparing IML and XAI.mp4 | 10.5 MB |
| 3. Introducing BayesiaLab Hair and eye color.mp4 | 10.5 MB |
| 2. Skepticism about results Is that really the best predictor.mp4 | 10.5 MB |
| 5. Wordle, bans, and bits.mp4 | 10.6 MB |
| 1. The investigator, the jury, and the judge.mp4 | 10.6 MB |
| 2. How to evaluate and visualize clusters in Python.mp4 | 10.7 MB |
| 2. Making predictions with logistic regression.mp4 | 10.8 MB |
| 2. Downloading BayesiaLab and resources.mp4 | 10.9 MB |
| 2. Hume on induction.mp4 | 11.0 MB |
| 4. How RT handles nominal variables.mp4 | 11.1 MB |
| 2. Pearson on correlation and causation.mp4 | 11.2 MB |
| 3. Comparing CRISP-DM and the scientific method.mp4 | 11.2 MB |
| 2. How to visualize a classification tree in Python.mp4 | 11.3 MB |
| 1. What is clustering.mp4 | 11.5 MB |
| 1. Introducing Leo Breiman and CART.mp4 | 11.6 MB |
| 2. Understanding the entropy calculation.mp4 | 11.7 MB |
| 3. Google Optimize.mp4 | 11.7 MB |
| 4. How is a regression tree built.mp4 | 11.8 MB |
| 1. The Two Cultures.mp4 | 12.0 MB |
| 2. The regression tree prebuilt example.mp4 | 12.0 MB |
| 6. Solution JASP.mp4 | 12.1 MB |
| 6. Why and when to use association rules.mp4 | 12.2 MB |
| 2. Explain vs. predict.mp4 | 12.3 MB |
| 2. How to visualize a regression tree in Python.mp4 | 12.4 MB |
| 2. How is a classification tree built.mp4 | 12.4 MB |
| 3. Correlation and regression.mp4 | 12.5 MB |
| 5. An overview of decision tree algorithms.mp4 | 12.5 MB |
| 1. What is logistic regression.mp4 | 12.5 MB |
| 1. Data mining vs. data dredging.mp4 | 12.6 MB |
| 3. How to prune a classification tree in Python.mp4 | 12.7 MB |
| 3. Introducing KNIME.mp4 | 12.8 MB |
| 3. How do classification trees measure impurity.mp4 | 12.9 MB |
| 1. Lady tasting tea.mp4 | 12.9 MB |
| 4. Taleb on normality, mediocristan, and extremistan.mp4 | 12.9 MB |
| 6. Solution Evaluate significant finding.mp4 | 13.0 MB |
| 1. Contrasting frequentist statistics and Bayesian statistics.mp4 | 13.1 MB |
| 3. Developing an intuition for Bayes with Wordle.mp4 | 13.1 MB |
| 3. Interpreting the coefficients of logistic regression.mp4 | 13.4 MB |
| 3. How to find the right number of clusters in Python.mp4 | 13.7 MB |
| 6. Why and when to use a decision tree.mp4 | 13.7 MB |
| 1. What are association rules.mp4 | 13.8 MB |
| 5. Bayesian Networks Black Swan case study.mp4 | 14.5 MB |
| 1. What are induction and deduction.mp4 | 14.6 MB |
| 7. Moderation, mediation, and lurking variables.mp4 | 15.1 MB |
| 4. Applying the two methods at work.mp4 | 15.1 MB |
| 4. How to interpret the results of k-means clustering in Python.mp4 | 15.1 MB |
| 3. How to prune a regression tree in Python.mp4 | 15.7 MB |
| 3. The Apriori algorithm.mp4 | 15.7 MB |
| 1. How to build a classification tree in Python.mp4 | 15.7 MB |
| 5. Working with the prebuilt example.mp4 | 15.9 MB |
| 4. Introduction to causal modeling with Bayesian networks.mp4 | 16.1 MB |
| 3. Common types of regression.mp4 | 16.3 MB |
| 1. Understanding the what and why your models predict.mp4 | 16.4 MB |
| 12. When to turn off pruning.mp4 | 16.4 MB |
| 2. Frequent itemset generation.mp4 | 16.9 MB |
| 4. Bayes and rare events.mp4 | 17.0 MB |
| 2. Enigma and uncertainty.mp4 | 17.1 MB |
| 3. Choosing the right number of clusters.mp4 | 17.4 MB |
| 3. How to build a logistic regression model in Python.mp4 | 17.8 MB |
| 1. Sewell Wright.mp4 | 18.2 MB |
| 4. Trends in AI making the XAI problem more prominent.mp4 | 18.3 MB |
| 5. How to prune a decision tree.mp4 | 19.1 MB |
| 1. How to build a regression tree in Python.mp4 | 20.1 MB |
| 4. A quick review of machine learning basics with examples.mp4 | 20.3 MB |
| 2. Fisher and experiments.mp4 | 20.6 MB |
| 5. Evaluating association rules.mp4 | 21.1 MB |
| 5. Solution What is causing what.mp4 | 21.1 MB |
| 1. What is a strong correlation.mp4 | 21.2 MB |
| 4. Using GitHub Codespaces with this course.mp4 | 21.6 MB |
| 4. Using GitHub Codespaces with this course.mp4 | 21.6 MB |
| 2. How to prepare data for logistic regression in Python.mp4 | 21.9 MB |
| 1. Using probability to measure uncertainty.mp4 | 22.2 MB |
| 1. How to segment data with k-means clustering in Python.mp4 | 23.6 MB |
| 5. Control variables (ANCOVA).mp4 | 23.8 MB |
| 1. Turing, Enigma, and CAPTCHA.mp4 | 24.1 MB |
| 8. Simpson's paradox.mp4 | 26.0 MB |
| 4. The FP-Growth algorithm.mp4 | 26.5 MB |
| 1. How to collect data for association rule mining.mp4 | 27.4 MB |
| 4. How to interpret a logistic regression model in Python.mp4 | 28.3 MB |
| 2. How to generate frequent itemsets.mp4 | 31.1 MB |
| 2. Bayesian T-Test with JASP.mp4 | 33.6 MB |
| 1. How to explore data for logistic regression in Python.mp4 | 36.1 MB |
| 3. John Snow and natural experiments.mp4 | 36.7 MB |
| 3. How to create association rules.mp4 | 43.0 MB |
| 4. How to evaluate association rules.mp4 | 44.0 MB |
Name
DL
Uploader
Size
S/L
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947.1 MB
[28
/
9]
2024-08-27
| Uploaded by SunRiseZone | Size 947.1 MB | Health [ 28 /9 ] | Added 2024-08-27 |
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2.8 GB
[67
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10]
2024-05-23
| Uploaded by SunRiseZone | Size 2.8 GB | Health [ 67 /10 ] | Added 2024-05-23 |
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3.4 GB
[49
/
12]
2024-05-20
| Uploaded by SunRiseZone | Size 3.4 GB | Health [ 49 /12 ] | Added 2024-05-20 |
NOTE
SOURCE: LinkedIn Learning Advance Your Skills as a Machine Learning Spe
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