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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LinkedIn Learning Advance Your Skills as a Machine Learning Spe
Exercises_Link - OneHack.us.txt -
Exercises_Link.txt -
$10 ChatGPT for 1 Year & More.txt -
description.html -
description.html -
description.html -
description.html -
description.html -
1. Continuing your deep learning journey.srt -
description.html -
1. Making decisions with Python.srt -
1. Getting started with Python and k-means clustering.srt -
description.html -
description.html -
4. Tuning backpropagation.srt -
1. Optimizing neural networks.srt -
3. Regularization experiment.srt -
2. Regularization.srt -
5. Avoiding overfitting.srt -
5. Dropout experiment.srt -
2. Acquire and process data.srt -
1. Exploring the world of explainable AI and interpretable machine learning.srt -
2. What you should know.srt -
3. What you should know.srt -
1. Next steps.srt -
1. Review.srt -
1. Classifying data with logistic regression.srt -
4. Dropouts.srt -
1. Association rule mining.srt -
2. What you should know.srt -
1. MPG data set.srt -
6. Learning rate experiment.srt -
2. What you should know.srt -
2. What you should know.srt -
3. Tuning the network.srt -
2. p-value review.srt -
2. What you should know.srt -
7. Evaluating the accuracy of your CART tree.srt -
5. Learning rate.srt -
3. The tools you need.srt -
3. The tools you need.srt -
2. Why causation matters in a business setting.srt -
3. Using the exercise files.srt -
1. The basics of decision trees.srt -
2. Target audience.srt -
3. Using the exercise files.srt -
4. Optimizer experiment.srt -
1. Prediction, causation, and statistical inference.srt -
3. How to use the practice files.srt -
6. Building the final model.srt -
8. How C4.5 handles continuous variables.srt -
7. Challenge Conditional probability and Bayes' theorem.srt -
2. What you should know.srt -
4. Using the exercise files.srt -
3. Optimizers.srt -
3. An ANN model.srt -
4. Model optimization and tuning.srt -
5. Challenge Evaluate significant finding.srt -
5. How CART handles nominal variables.srt -
4. Using the exercise files.srt -
1. Thinking about causality.srt -
1. What is deep learning.srt -
4. Challenge What is causing what.srt -
4. Why and when to use logistic regression.srt -
4. Double blind studies.srt -
6. Initializing weights.srt -
5. Challenge JASP.srt -
1. Next steps with decision trees.srt -
1. Next steps.srt -
2. Batch normalization.srt -
1. Overfitting in ANNs.srt -
9. Equal size sampling.srt -
3. What is a causal model.srt -
1. Next steps.srt -
3. Hidden layers tuning.srt -
1. Epoch and batch size tuning.srt -
6. Experiment setups for the course.srt -
5. Choosing activation functions.srt -
1. Next steps.srt -
9. Challenge Moderation, mediation, or a third variable.srt -
3. Setting up exercise files.srt -
2. Variable importance and reason codes.srt -
4. Determining nodes in a layer.srt -
7. KNIME support of global and local explanations.srt -
9. Accuracy.srt -
2. Downloading BayesiaLab and resources.srt -
3. The math behind regression trees.srt -
6. XAI for debugging models.srt -
1. Ross Quinlan, ID3, C4.5, and C5.0.srt -
6. A quick look at the complete CART tree.srt -
7. How C4.5 handles nominal variables.srt -
4. Taleb on normality, mediocristan, and extremistan.srt -
5. Local and global explanations.srt -
5. Counterfactuals Pearl on induction and causality.srt -
8. Line plot.srt -
8. Solution Conditional probability and Bayes' theorem.srt -
2. What is the Gini coefficient.srt -
6. Why and when to use association rules.srt -
3. AB testing during the evaluation phase.srt -
1. Vanishing and exploding gradients.srt -
10. A quick look at the complete C4.5 tree.srt -
6. Judea Pearl Problems with control variables.srt -
2. Introducing path analysis and SEM.srt -
2. Review of artificial neural networks.srt -
1. Skepticism about data Truman 1948 Election Poll.srt -
1. Taking causality further.srt -
11. Evaluating the accuracy of your C4.5 tree.srt -
3. How C4.5 handles missing data.srt -
5. Latent variables in SEM.srt -
7. KNIME's missing data options for regression trees.srt -
4. Changing the settings in KNIME.srt -
3. Skepticism about causes Is X really causing Y.srt -
2. Prerequisites for the course.srt -
4. Why and when to use k-means clustering.srt -
4. The Give Me Some Credit data set.srt -
6. KNIME settings for C4.5.srt -
1. What is a decision tree.srt -
1. The investigator, the jury, and the judge.srt -
6. Why and when to use a decision tree.srt -
5. Bayesian Networks Black Swan case study.srt -
2. Epoch and batch size experiment.srt -
5. The deep learning tuning process.srt -
6. Finding direction of causality with SEM (PSAT).srt -
6. Closer look at a full regression tree.srt -
1. What is regression.srt -
3. Google Optimize.srt -
5. Ordinal variable handling.srt -
2. Enigma and uncertainty.srt -
10. Solution Moderation, mediation, or a third variable.srt -
2. How to evaluate and visualize clusters in Python.srt -
5. An overview of decision tree algorithms.srt -
2. Hume on induction.srt -
2. Skepticism about results Is that really the best predictor.srt -
1. Introducing Leo Breiman and CART.srt -
3. Introducing KNIME.srt -
2. What is k-means clustering.srt -
3. SEM example Intention.srt -
4. Myths about SEM.srt -
4. Bayes and rare events.srt -
3. Introducing BayesiaLab Hair and eye color.srt -
2. The anatomy of a regression model.srt -
2. The regression tree prebuilt example.srt -
6. Solution JASP.srt -
1. Sewell Wright.srt -
4. How RT handles nominal variables.srt -
4. Taleb on induction.srt -
5. Wordle, bans, and bits.srt -
3. Hypothesis testing checklist.srt -
2. How to visualize a classification tree in Python.srt -
6. Wordle and Bayes' theorem.srt -
1. What are association rules.srt -
1. Judea Pearl and the causal revolution.srt -
3. Popper on induction and falsification.srt -
1. What are induction and deduction.srt -
4. Applying the two methods at work.srt -
3. The Apriori algorithm.srt -
3. Comparing IML and XAI.srt -
2. Making predictions with logistic regression.srt -
4. Wordle and conditional probability.srt -
1. Tuning exercise Problem statement.srt -
1. Understanding the what and why your models predict.srt -
1. Contrasting frequentist statistics and Bayesian statistics.srt -
3. How to prune a classification tree in Python.srt -
2. TrainTest What can go wrong.srt -
1. What is a decision tree.srt -
Ex_Files_ML_with_Python_k_Means_Clustering.zip -
1. Lady tasting tea.srt -
2. Pearson on correlation and causation.srt -
2. Explain vs. predict.srt -
3. Correlation and regression.srt -
3. How to build a logistic regression model in Python.srt -
3. Comparing CRISP-DM and the scientific method.srt -
1. The Two Cultures.srt -
4. How to interpret the results of k-means clustering in Python.srt -
3. How to find the right number of clusters in Python.srt -
3. How CART handles missing data using surrogates.srt -
2. Fisher and experiments.srt -
1. What is clustering.srt -
2. The pros and cons of decision trees.srt -
2. How to visualize a regression tree in Python.srt -
3. How to prune a regression tree in Python.srt -
4. How is a regression tree built.srt -
4. Trends in AI making the XAI problem more prominent.srt -
1. Data mining vs. data dredging.srt -
12. When to turn off pruning.srt -
1. Turing, Enigma, and CAPTCHA.srt -
3. Common types of regression.srt -
5. Working with the prebuilt example.srt -
3. How do classification trees measure impurity.srt -
1. How to build a classification tree in Python.srt -
2. Understanding the entropy calculation.srt -
2. How to prepare data for logistic regression in Python.srt -
4. Introduction to causal modeling with Bayesian networks.srt -
2. How is a classification tree built.srt -
4. Using GitHub Codespaces with this course.srt -
1. What is logistic regression.srt -
7. Moderation, mediation, and lurking variables.srt -
6. Solution Evaluate significant finding.srt -
1. What is a strong correlation.srt -
4. A quick review of machine learning basics with examples.srt -
2. Frequent itemset generation.srt -
4. Using GitHub Codespaces with this course.srt -
3. Interpreting the coefficients of logistic regression.srt -
Ex_Files_Machine_Learning_with_Python_Decision_Trees.zip -
4. The FP-Growth algorithm.srt -
5. How to prune a decision tree.srt -
2. How to generate frequent itemsets.srt -
1. How to build a regression tree in Python.srt -
5. Evaluating association rules.srt -
5. Solution What is causing what.srt -
1. How to segment data with k-means clustering in Python.srt -
1. How to collect data for association rule mining.srt -
3. John Snow and natural experiments.srt -
3. Developing an intuition for Bayes with Wordle.srt -
4. How to interpret a logistic regression model in Python.srt -
3. Choosing the right number of clusters.srt -
1. Using probability to measure uncertainty.srt -
3. How to create association rules.srt -
8. Simpson's paradox.srt -
4. How to evaluate association rules.srt -
5. Control variables (ANCOVA).srt -
1. How to explore data for logistic regression in Python.srt -
2. Bayesian T-Test with JASP.srt -
Ex_Files_ML_and_AI_Foundations.zip -
Ex_Files_ML_and_AI_Foundations_Causal_Inf_Modeling.zip -
Ex_Files_Deep_Learning_Model_Optimization_Tuning.zip -
1. Next steps.mp4 -
2. Regularization.mp4 -
3. The tools you need.mp4 -
4. Dropouts.mp4 -
2. What you should know.mp4 -
3. The tools you need.mp4 -
2. What you should know.mp4 -
1. Continuing your deep learning journey.mp4 -
2. What you should know.mp4 -
2. What you should know.mp4 -
2. What you should know.mp4 -
Ex_Files_ML_and_AI_Foundations_Decision_Trees_KNIME.zip -
3. What you should know.mp4 -
3. Regularization experiment.mp4 -
5. Learning rate.mp4 -
3. Optimizers.mp4 -
5. Avoiding overfitting.mp4 -
2. Target audience.mp4 -
4. Tuning backpropagation.mp4 -
1. Next steps with decision trees.mp4 -
2. What you should know.mp4 -
1. Next steps.mp4 -
2. Why causation matters in a business setting.mp4 -
3. An ANN model.mp4 -
1. What is deep learning.mp4 -
7. Evaluating the accuracy of your CART tree.mp4 -
2. p-value review.mp4 -
5. Dropout experiment.mp4 -
1. Review.mp4 -
4. Model optimization and tuning.mp4 -
1. Overfitting in ANNs.mp4 -
3. Using the exercise files.mp4 -
1. Epoch and batch size tuning.mp4 -
1. Next steps.mp4 -
2. Acquire and process data.mp4 -
1. Next steps.mp4 -
7. Challenge Conditional probability and Bayes' theorem.mp4 -
3. Tuning the network.mp4 -
1. Making decisions with Python.mp4 -
6. Building the final model.mp4 -
3. The math behind regression trees.mp4 -
6. Learning rate experiment.mp4 -
1. Getting started with Python and k-means clustering.mp4 -
8. How C4.5 handles continuous variables.mp4 -
3. Using the exercise files.mp4 -
1. MPG data set.mp4 -
3. How to use the practice files.mp4 -
4. Optimizer experiment.mp4 -
5. How CART handles nominal variables.mp4 -
2. Prerequisites for the course.mp4 -
1. Optimizing neural networks.mp4 -
5. Challenge Evaluate significant finding.mp4 -
6. Initializing weights.mp4 -
1. Exploring the world of explainable AI and interpretable machine learning.mp4 -
5. Counterfactuals Pearl on induction and causality.mp4 -
1. Vanishing and exploding gradients.mp4 -
1. Taking causality further.mp4 -
5. Local and global explanations.mp4 -
4. Challenge What is causing what.mp4 -
7. KNIME support of global and local explanations.mp4 -
4. Double blind studies.mp4 -
3. Hidden layers tuning.mp4 -
2. Review of artificial neural networks.mp4 -
5. Choosing activation functions.mp4 -
1. Ross Quinlan, ID3, C4.5, and C5.0.mp4 -
4. Determining nodes in a layer.mp4 -
9. Challenge Moderation, mediation, or a third variable.mp4 -
3. Setting up exercise files.mp4 -
3. How C4.5 handles missing data.mp4 -
5. Challenge JASP.mp4 -
3. AB testing during the evaluation phase.mp4 -
1. Prediction, causation, and statistical inference.mp4 -
3. What is a causal model.mp4 -
4. Why and when to use logistic regression.mp4 -
8. Solution Conditional probability and Bayes' theorem.mp4 -
5. The deep learning tuning process.mp4 -
1. Classifying data with logistic regression.mp4 -
9. Equal size sampling.mp4 -
10. A quick look at the complete C4.5 tree.mp4 -
2. Batch normalization.mp4 -
2. Introducing path analysis and SEM.mp4 -
9. Accuracy.mp4 -
6. Finding direction of causality with SEM (PSAT).mp4 -
2. What is k-means clustering.mp4 -
1. Skepticism about data Truman 1948 Election Poll.mp4 -
2. What is the Gini coefficient.mp4 -
6. XAI for debugging models.mp4 -
6. A quick look at the complete CART tree.mp4 -
1. The basics of decision trees.mp4 -
1. What is a decision tree.mp4 -
3. SEM example Intention.mp4 -
5. Latent variables in SEM.mp4 -
7. How C4.5 handles nominal variables.mp4 -
7. KNIME's missing data options for regression trees.mp4 -
4. Using the exercise files.mp4 -
3. Hypothesis testing checklist.mp4 -
4. Changing the settings in KNIME.mp4 -
1. Association rule mining.mp4 -
4. Using the exercise files.mp4 -
8. Line plot.mp4 -
4. The Give Me Some Credit data set.mp4 -
4. Wordle and conditional probability.mp4 -
6. Wordle and Bayes' theorem.mp4 -
1. Thinking about causality.mp4 -
3. Skepticism about causes Is X really causing Y.mp4 -
1. Judea Pearl and the causal revolution.mp4 -
6. KNIME settings for C4.5.mp4 -
6. Experiment setups for the course.mp4 -
6. Closer look at a full regression tree.mp4 -
1. Tuning exercise Problem statement.mp4 -
2. Variable importance and reason codes.mp4 -
11. Evaluating the accuracy of your C4.5 tree.mp4 -
10. Solution Moderation, mediation, or a third variable.mp4 -
4. Myths about SEM.mp4 -
1. What is a decision tree.mp4 -
6. Judea Pearl Problems with control variables.mp4 -
3. How CART handles missing data using surrogates.mp4 -
2. Epoch and batch size experiment.mp4 -
4. Why and when to use k-means clustering.mp4 -
2. The anatomy of a regression model.mp4 -
2. The pros and cons of decision trees.mp4 -
5. Ordinal variable handling.mp4 -
2. TrainTest What can go wrong.mp4 -
4. Taleb on induction.mp4 -
3. Popper on induction and falsification.mp4 -
1. What is regression.mp4 -
3. Comparing IML and XAI.mp4 -
3. Introducing BayesiaLab Hair and eye color.mp4 -
2. Skepticism about results Is that really the best predictor.mp4 -
5. Wordle, bans, and bits.mp4 -
1. The investigator, the jury, and the judge.mp4 -
2. How to evaluate and visualize clusters in Python.mp4 -
2. Making predictions with logistic regression.mp4 -
2. Downloading BayesiaLab and resources.mp4 -
2. Hume on induction.mp4 -
4. How RT handles nominal variables.mp4 -
2. Pearson on correlation and causation.mp4 -
3. Comparing CRISP-DM and the scientific method.mp4 -
2. How to visualize a classification tree in Python.mp4 -
1. What is clustering.mp4 -
1. Introducing Leo Breiman and CART.mp4 -
2. Understanding the entropy calculation.mp4 -
3. Google Optimize.mp4 -
4. How is a regression tree built.mp4 -
1. The Two Cultures.mp4 -
2. The regression tree prebuilt example.mp4 -
6. Solution JASP.mp4 -
6. Why and when to use association rules.mp4 -
2. Explain vs. predict.mp4 -
2. How to visualize a regression tree in Python.mp4 -
2. How is a classification tree built.mp4 -
3. Correlation and regression.mp4 -
5. An overview of decision tree algorithms.mp4 -
1. What is logistic regression.mp4 -
1. Data mining vs. data dredging.mp4 -
3. How to prune a classification tree in Python.mp4 -
3. Introducing KNIME.mp4 -
3. How do classification trees measure impurity.mp4 -
1. Lady tasting tea.mp4 -
4. Taleb on normality, mediocristan, and extremistan.mp4 -
6. Solution Evaluate significant finding.mp4 -
1. Contrasting frequentist statistics and Bayesian statistics.mp4 -
3. Developing an intuition for Bayes with Wordle.mp4 -
3. Interpreting the coefficients of logistic regression.mp4 -
3. How to find the right number of clusters in Python.mp4 -
6. Why and when to use a decision tree.mp4 -
1. What are association rules.mp4 -
5. Bayesian Networks Black Swan case study.mp4 -
1. What are induction and deduction.mp4 -
7. Moderation, mediation, and lurking variables.mp4 -
4. Applying the two methods at work.mp4 -
4. How to interpret the results of k-means clustering in Python.mp4 -
3. How to prune a regression tree in Python.mp4 -
3. The Apriori algorithm.mp4 -
1. How to build a classification tree in Python.mp4 -
5. Working with the prebuilt example.mp4 -
4. Introduction to causal modeling with Bayesian networks.mp4 -
3. Common types of regression.mp4 -
1. Understanding the what and why your models predict.mp4 -
12. When to turn off pruning.mp4 -
2. Frequent itemset generation.mp4 -
4. Bayes and rare events.mp4 -
2. Enigma and uncertainty.mp4 -
3. Choosing the right number of clusters.mp4 -
3. How to build a logistic regression model in Python.mp4 -
1. Sewell Wright.mp4 -
4. Trends in AI making the XAI problem more prominent.mp4 -
5. How to prune a decision tree.mp4 -
1. How to build a regression tree in Python.mp4 -
4. A quick review of machine learning basics with examples.mp4 -
2. Fisher and experiments.mp4 -
5. Evaluating association rules.mp4 -
5. Solution What is causing what.mp4 -
1. What is a strong correlation.mp4 -
4. Using GitHub Codespaces with this course.mp4 -
4. Using GitHub Codespaces with this course.mp4 -
2. How to prepare data for logistic regression in Python.mp4 -
1. Using probability to measure uncertainty.mp4 -
1. How to segment data with k-means clustering in Python.mp4 -
5. Control variables (ANCOVA).mp4 -
1. Turing, Enigma, and CAPTCHA.mp4 -
8. Simpson's paradox.mp4 -
4. The FP-Growth algorithm.mp4 -
1. How to collect data for association rule mining.mp4 -
4. How to interpret a logistic regression model in Python.mp4 -
2. How to generate frequent itemsets.mp4 -
2. Bayesian T-Test with JASP.mp4 -
1. How to explore data for logistic regression in Python.mp4 -
3. John Snow and natural experiments.mp4 -
3. How to create association rules.mp4 -
4. How to evaluate association rules.mp4 -
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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
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