File List
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Machine Learning with Python Association Rules/Exercises_Link - OneHack.us.txt 121 B
Machine Learning with Python Logistic Regression/Exercises_Link.txt 123 B
$10 ChatGPT for 1 Year & More.txt 252 B
Machine Learning with Python k-Means Clustering/description.html 1006 B
Machine Learning and AI Foundations Causal Inference and Modeling/description.html 1015 B
Machine Learning with Python Decision Trees - OneHack.us/description.html 1.06 KB
Machine Learning and AI Foundations Decision Trees with KNIME/description.html 1.07 KB
Deep Learning Model Optimization and Tuning/description.html 1.14 KB
Deep Learning Model Optimization and Tuning/6 - Conclusion/1. Continuing your deep learning journey.srt 1.21 KB
Machine Learning with Python Association Rules/description.html 1.23 KB
Machine Learning with Python Decision Trees - OneHack.us/0 - Introduction/1. Making decisions with Python.srt 1.27 KB
Machine Learning with Python k-Means Clustering/0 - Introduction/1. Getting started with Python and k-means clustering.srt 1.27 KB
Machine Learning and AI Foundations Prediction, Causation, and Statistical Inference/description.html 1.3 KB
Machine Learning with Python Logistic Regression/description.html 1.34 KB
Deep Learning Model Optimization and Tuning/5 - Model Tuning Exercise/4. Tuning backpropagation.srt 1.34 KB
Deep Learning Model Optimization and Tuning/0 - Introduction/1. Optimizing neural networks.srt 1.36 KB
Deep Learning Model Optimization and Tuning/4 - Overfitting Management/3. Regularization experiment.srt 1.37 KB
Deep Learning Model Optimization and Tuning/4 - Overfitting Management/2. Regularization.srt 1.37 KB
Deep Learning Model Optimization and Tuning/5 - Model Tuning Exercise/5. Avoiding overfitting.srt 1.4 KB
Deep Learning Model Optimization and Tuning/4 - Overfitting Management/5. Dropout experiment.srt 1.52 KB
Deep Learning Model Optimization and Tuning/5 - Model Tuning Exercise/2. Acquire and process data.srt 1.53 KB
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 1.55 KB
Machine Learning and AI Foundations Decision Trees with KNIME/0 - Introduction/2. What you should know.srt 1.56 KB
Machine Learning and AI Foundations Producing Explainable AI (XAI) and Interpretable Machine Learning Solutions/0 - Introduction/3. What you should know.srt 1.59 KB
Machine Learning and AI Foundations Decision Trees with KNIME/5 - Conclusion/1. Next steps.srt 1.63 KB
Machine Learning and AI Foundations Prediction, Causation, and Statistical Inference/8 - Conclusion/1. Review.srt 1.67 KB
Machine Learning with Python Logistic Regression/0 - Introduction/1. Classifying data with logistic regression.srt 1.76 KB
Deep Learning Model Optimization and Tuning/4 - Overfitting Management/4. Dropouts.srt 1.81 KB
Machine Learning with Python Association Rules/0 - Introduction/1. Association rule mining.srt 1.86 KB
Machine Learning with Python Logistic Regression/0 - Introduction/2. What you should know.srt 1.88 KB
Machine Learning and AI Foundations Decision Trees with KNIME/4 - Introducing Regression Trees/1. MPG data set.srt 1.91 KB
Deep Learning Model Optimization and Tuning/3 - Tuning Back Propagation/6. Learning rate experiment.srt 1.93 KB
Machine Learning with Python Association Rules/0 - Introduction/2. What you should know.srt 1.94 KB
Machine Learning with Python Decision Trees - OneHack.us/0 - Introduction/2. What you should know.srt 1.98 KB
Deep Learning Model Optimization and Tuning/5 - Model Tuning Exercise/3. Tuning the network.srt 1.99 KB
Machine Learning and AI Foundations Prediction, Causation, and Statistical Inference/4 - Prediction and Proof in Statistics/2. p-value review.srt 1.99 KB
Machine Learning with Python k-Means Clustering/0 - Introduction/2. What you should know.srt 1.99 KB
Machine Learning and AI Foundations Decision Trees with KNIME/3 - Introducing Classification Trees/7. Evaluating the accuracy of your CART tree.srt 2.03 KB
Deep Learning Model Optimization and Tuning/3 - Tuning Back Propagation/5. Learning rate.srt 2.04 KB
Machine Learning with Python k-Means Clustering/0 - Introduction/3. The tools you need.srt 2.04 KB
Machine Learning with Python Decision Trees - OneHack.us/0 - Introduction/3. The tools you need.srt 2.09 KB
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 2.11 KB
Machine Learning with Python Association Rules/0 - Introduction/3. Using the exercise files.srt 2.12 KB
Machine Learning and AI Foundations Decision Trees with KNIME/0 - Introduction/1. The basics of decision trees.srt 2.13 KB
Machine Learning and AI Foundations Producing Explainable AI (XAI) and Interpretable Machine Learning Solutions/0 - Introduction/2. Target audience.srt 2.14 KB
Machine Learning with Python Logistic Regression/0 - Introduction/3. Using the exercise files.srt 2.15 KB
Deep Learning Model Optimization and Tuning/3 - Tuning Back Propagation/4. Optimizer experiment.srt 2.17 KB
Machine Learning and AI Foundations Prediction, Causation, and Statistical Inference/0 - Introduction/1. Prediction, causation, and statistical inference.srt 2.2 KB
Machine Learning and AI Foundations Decision Trees with KNIME/0 - Introduction/3. How to use the practice files.srt 2.21 KB
Deep Learning Model Optimization and Tuning/5 - Model Tuning Exercise/6. Building the final model.srt 2.25 KB
Machine Learning and AI Foundations Decision Trees with KNIME/2 - Introducing the C5.0 Algorithm/8. How C4.5 handles continuous variables.srt 2.32 KB
Machine Learning and AI Foundations Causal Inference and Modeling/2 - Conditional Probability and Bayes' Theorem/7. Challenge Conditional probability and Bayes' theorem.srt 2.39 KB
Machine Learning and AI Foundations Causal Inference and Modeling/0 - Introduction/2. What you should know.srt 2.44 KB
Machine Learning with Python Decision Trees - OneHack.us/0 - Introduction/4. Using the exercise files.srt 2.54 KB
Deep Learning Model Optimization and Tuning/3 - Tuning Back Propagation/3. Optimizers.srt 2.54 KB
Deep Learning Model Optimization and Tuning/1 - Introduction to Deep Learning Optimization/3. An ANN model.srt 2.54 KB
Deep Learning Model Optimization and Tuning/1 - Introduction to Deep Learning Optimization/4. Model optimization and tuning.srt 2.55 KB
Machine Learning and AI Foundations Prediction, Causation, and Statistical Inference/4 - Prediction and Proof in Statistics/5. Challenge Evaluate significant finding.srt 2.58 KB
Machine Learning and AI Foundations Decision Trees with KNIME/3 - Introducing Classification Trees/5. How CART handles nominal variables.srt 2.62 KB
Machine Learning with Python k-Means Clustering/0 - Introduction/4. Using the exercise files.srt 2.67 KB
Machine Learning and AI Foundations Causal Inference and Modeling/0 - Introduction/1. Thinking about causality.srt 2.67 KB
Deep Learning Model Optimization and Tuning/1 - Introduction to Deep Learning Optimization/1. What is deep learning.srt 2.73 KB
Machine Learning and AI Foundations Prediction, Causation, and Statistical Inference/3 - Correlation Does Not Imply Causation/4. Challenge What is causing what.srt 2.79 KB
Machine Learning with Python Logistic Regression/2 - Logistic Regression/4. Why and when to use logistic regression.srt 2.87 KB
Machine Learning and AI Foundations Causal Inference and Modeling/1 - Experimental Design and Statistical Controls/4. Double blind studies.srt 2.9 KB
Deep Learning Model Optimization and Tuning/2 - Tuning the Deep Learning Network/6. Initializing weights.srt 2.91 KB
Machine Learning and AI Foundations Causal Inference and Modeling/3 - Prediction and Proof with Bayesian statistics/5. Challenge JASP.srt 2.91 KB
Machine Learning with Python Decision Trees - OneHack.us/4 - Conclusion/1. Next steps with decision trees.srt 2.96 KB
Machine Learning with Python k-Means Clustering/3 - Conclusion/1. Next steps.srt 2.98 KB
Deep Learning Model Optimization and Tuning/3 - Tuning Back Propagation/2. Batch normalization.srt 3.15 KB
Deep Learning Model Optimization and Tuning/4 - Overfitting Management/1. Overfitting in ANNs.srt 3.27 KB
Machine Learning and AI Foundations Decision Trees with KNIME/2 - Introducing the C5.0 Algorithm/9. Equal size sampling.srt 3.32 KB
Machine Learning and AI Foundations Prediction, Causation, and Statistical Inference/1 - What Is a Casual Model/3. What is a causal model.srt 3.32 KB
Machine Learning with Python Logistic Regression/4 - Conclusion/1. Next steps.srt 3.33 KB
Deep Learning Model Optimization and Tuning/2 - Tuning the Deep Learning Network/3. Hidden layers tuning.srt 3.33 KB
Deep Learning Model Optimization and Tuning/2 - Tuning the Deep Learning Network/1. Epoch and batch size tuning.srt 3.38 KB
Deep Learning Model Optimization and Tuning/1 - Introduction to Deep Learning Optimization/6. Experiment setups for the course.srt 3.39 KB
Deep Learning Model Optimization and Tuning/2 - Tuning the Deep Learning Network/5. Choosing activation functions.srt 3.41 KB
Machine Learning with Python Association Rules/3 - Conclusion/1. Next steps.srt 3.44 KB
Machine Learning and AI Foundations Causal Inference and Modeling/1 - Experimental Design and Statistical Controls/9. Challenge Moderation, mediation, or a third variable.srt 3.45 KB
Deep Learning Model Optimization and Tuning/0 - Introduction/3. Setting up exercise files.srt 3.47 KB
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 3.49 KB
Deep Learning Model Optimization and Tuning/2 - Tuning the Deep Learning Network/4. Determining nodes in a layer.srt 3.53 KB
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 3.55 KB
Machine Learning and AI Foundations Decision Trees with KNIME/4 - Introducing Regression Trees/9. Accuracy.srt 3.57 KB
Machine Learning and AI Foundations Causal Inference and Modeling/5 - Causal Modeling with Bayesian Networks/2. Downloading BayesiaLab and resources.srt 3.58 KB
Machine Learning and AI Foundations Decision Trees with KNIME/4 - Introducing Regression Trees/3. The math behind regression trees.srt 3.59 KB
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
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 3.61 KB
Machine Learning and AI Foundations Decision Trees with KNIME/3 - Introducing Classification Trees/6. A quick look at the complete CART tree.srt 3.63 KB
Machine Learning and AI Foundations Decision Trees with KNIME/2 - Introducing the C5.0 Algorithm/7. How C4.5 handles nominal variables.srt 3.64 KB
Machine Learning and AI Foundations Prediction, Causation, and Statistical Inference/4 - Prediction and Proof in Statistics/4. Taleb on normality, mediocristan, and extremistan.srt 3.66 KB
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 3.71 KB
Machine Learning and AI Foundations Prediction, Causation, and Statistical Inference/5 - Deduction and Induction/5. Counterfactuals Pearl on induction and causality.srt 3.79 KB
Machine Learning and AI Foundations Decision Trees with KNIME/4 - Introducing Regression Trees/8. Line plot.srt 3.83 KB
Machine Learning and AI Foundations Causal Inference and Modeling/2 - Conditional Probability and Bayes' Theorem/8. Solution Conditional probability and Bayes' theorem.srt 3.95 KB
Machine Learning and AI Foundations Decision Trees with KNIME/3 - Introducing Classification Trees/2. What is the Gini coefficient.srt 3.97 KB
Machine Learning with Python Association Rules/1 - Association Rules/6. Why and when to use association rules.srt 4.05 KB
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 4.18 KB
Deep Learning Model Optimization and Tuning/3 - Tuning Back Propagation/1. Vanishing and exploding gradients.srt 4.24 KB
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 4.33 KB
Machine Learning and AI Foundations Causal Inference and Modeling/1 - Experimental Design and Statistical Controls/6. Judea Pearl Problems with control variables.srt 4.35 KB
Machine Learning and AI Foundations Causal Inference and Modeling/4 - Causal Modeling with Structural Equation Modeling (SEM)/2. Introducing path analysis and SEM.srt 4.35 KB
Deep Learning Model Optimization and Tuning/1 - Introduction to Deep Learning Optimization/2. Review of artificial neural networks.srt 4.36 KB
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 4.37 KB
Machine Learning and AI Foundations Causal Inference and Modeling/6 - Conclusion/1. Taking causality further.srt 4.39 KB
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
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
Machine Learning and AI Foundations Causal Inference and Modeling/4 - Causal Modeling with Structural Equation Modeling (SEM)/5. Latent variables in SEM.srt 4.48 KB
Machine Learning and AI Foundations Decision Trees with KNIME/4 - Introducing Regression Trees/7. KNIME's missing data options for regression trees.srt 4.49 KB
Machine Learning and AI Foundations Decision Trees with KNIME/3 - Introducing Classification Trees/4. Changing the settings in KNIME.srt 4.52 KB
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 4.54 KB
Deep Learning Model Optimization and Tuning/0 - Introduction/2. Prerequisites for the course.srt 4.58 KB
Machine Learning with Python k-Means Clustering/1 - Understanding K-Means Clustering/4. Why and when to use k-means clustering.srt 4.61 KB
Machine Learning and AI Foundations Decision Trees with KNIME/2 - Introducing the C5.0 Algorithm/4. The Give Me Some Credit data set.srt 4.63 KB
Machine Learning and AI Foundations Decision Trees with KNIME/2 - Introducing the C5.0 Algorithm/6. KNIME settings for C4.5.srt 4.89 KB
Machine Learning and AI Foundations Decision Trees with KNIME/1 - Introducing Decision Trees/1. What is a decision tree.srt 4.93 KB
Machine Learning and AI Foundations Causal Inference and Modeling/1 - Experimental Design and Statistical Controls/1. The investigator, the jury, and the judge.srt 4.96 KB
Machine Learning with Python Decision Trees - OneHack.us/1 - Decision Trees/6. Why and when to use a decision tree.srt 4.99 KB
Machine Learning and AI Foundations Causal Inference and Modeling/5 - Causal Modeling with Bayesian Networks/5. Bayesian Networks Black Swan case study.srt 5.02 KB
Deep Learning Model Optimization and Tuning/2 - Tuning the Deep Learning Network/2. Epoch and batch size experiment.srt 5.05 KB
Deep Learning Model Optimization and Tuning/1 - Introduction to Deep Learning Optimization/5. The deep learning tuning process.srt 5.22 KB
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 5.25 KB
Machine Learning and AI Foundations Decision Trees with KNIME/4 - Introducing Regression Trees/6. Closer look at a full regression tree.srt 5.26 KB
Machine Learning with Python Logistic Regression/1 - Regression/1. What is regression.srt 5.29 KB
Machine Learning and AI Foundations Causal Inference and Modeling/3 - Prediction and Proof with Bayesian statistics/3. Google Optimize.srt 5.39 KB
Machine Learning and AI Foundations Decision Trees with KNIME/4 - Introducing Regression Trees/5. Ordinal variable handling.srt 5.43 KB
Machine Learning and AI Foundations Causal Inference and Modeling/2 - Conditional Probability and Bayes' Theorem/2. Enigma and uncertainty.srt 5.65 KB
Machine Learning and AI Foundations Causal Inference and Modeling/1 - Experimental Design and Statistical Controls/10. Solution Moderation, mediation, or a third variable.srt 5.71 KB
Machine Learning with Python k-Means Clustering/2 - Segmenting Data with K-Means Clustering/2. How to evaluate and visualize clusters in Python.srt 5.74 KB
Machine Learning and AI Foundations Decision Trees with KNIME/1 - Introducing Decision Trees/5. An overview of decision tree algorithms.srt 5.75 KB
Machine Learning and AI Foundations Prediction, Causation, and Statistical Inference/5 - Deduction and Induction/2. Hume on induction.srt 5.81 KB
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 5.81 KB
Machine Learning and AI Foundations Decision Trees with KNIME/3 - Introducing Classification Trees/1. Introducing Leo Breiman and CART.srt 5.92 KB
Machine Learning and AI Foundations Decision Trees with KNIME/1 - Introducing Decision Trees/3. Introducing KNIME.srt 6.02 KB
Machine Learning with Python k-Means Clustering/1 - Understanding K-Means Clustering/2. What is k-means clustering.srt 6.1 KB
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
Machine Learning and AI Foundations Causal Inference and Modeling/4 - Causal Modeling with Structural Equation Modeling (SEM)/4. Myths about SEM.srt 6.21 KB
Machine Learning and AI Foundations Causal Inference and Modeling/3 - Prediction and Proof with Bayesian statistics/4. Bayes and rare events.srt 6.22 KB
Machine Learning and AI Foundations Causal Inference and Modeling/5 - Causal Modeling with Bayesian Networks/3. Introducing BayesiaLab Hair and eye color.srt 6.27 KB
Machine Learning with Python Logistic Regression/1 - Regression/2. The anatomy of a regression model.srt 6.28 KB
Machine Learning and AI Foundations Decision Trees with KNIME/4 - Introducing Regression Trees/2. The regression tree prebuilt example.srt 6.32 KB
Machine Learning and AI Foundations Causal Inference and Modeling/3 - Prediction and Proof with Bayesian statistics/6. Solution JASP.srt 6.36 KB
Machine Learning and AI Foundations Causal Inference and Modeling/4 - Causal Modeling with Structural Equation Modeling (SEM)/1. Sewell Wright.srt 6.46 KB
Machine Learning and AI Foundations Decision Trees with KNIME/4 - Introducing Regression Trees/4. How RT handles nominal variables.srt 6.47 KB
Machine Learning and AI Foundations Prediction, Causation, and Statistical Inference/5 - Deduction and Induction/4. Taleb on induction.srt 6.49 KB
Machine Learning and AI Foundations Causal Inference and Modeling/2 - Conditional Probability and Bayes' Theorem/5. Wordle, bans, and bits.srt 6.53 KB
Machine Learning and AI Foundations Prediction, Causation, and Statistical Inference/4 - Prediction and Proof in Statistics/3. Hypothesis testing checklist.srt 6.54 KB
Machine Learning with Python Decision Trees - OneHack.us/2 - Working with Classification Trees/2. How to visualize a classification tree in Python.srt 6.56 KB
Machine Learning and AI Foundations Causal Inference and Modeling/2 - Conditional Probability and Bayes' Theorem/6. Wordle and Bayes' theorem.srt 6.58 KB
Machine Learning with Python Association Rules/1 - Association Rules/1. What are association rules.srt 6.63 KB
Machine Learning and AI Foundations Causal Inference and Modeling/5 - Causal Modeling with Bayesian Networks/1. Judea Pearl and the causal revolution.srt 6.64 KB
Machine Learning and AI Foundations Prediction, Causation, and Statistical Inference/5 - Deduction and Induction/3. Popper on induction and falsification.srt 6.65 KB
Machine Learning and AI Foundations Prediction, Causation, and Statistical Inference/5 - Deduction and Induction/1. What are induction and deduction.srt 6.67 KB
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 6.68 KB
Machine Learning with Python Association Rules/1 - Association Rules/3. The Apriori algorithm.srt 6.76 KB
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 6.76 KB
Machine Learning with Python Logistic Regression/2 - Logistic Regression/2. Making predictions with logistic regression.srt 6.79 KB
Machine Learning and AI Foundations Causal Inference and Modeling/2 - Conditional Probability and Bayes' Theorem/4. Wordle and conditional probability.srt 6.8 KB
Deep Learning Model Optimization and Tuning/5 - Model Tuning Exercise/1. Tuning exercise Problem statement.srt 6.83 KB
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
Machine Learning and AI Foundations Causal Inference and Modeling/3 - Prediction and Proof with Bayesian statistics/1. Contrasting frequentist statistics and Bayesian statistics.srt 6.99 KB
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
Machine Learning and AI Foundations Prediction, Causation, and Statistical Inference/6 - Prediction and Proof in Data Mining/2. TrainTest What can go wrong.srt 7.24 KB
Machine Learning with Python Decision Trees - OneHack.us/1 - Decision Trees/1. What is a decision tree.srt 7.25 KB
Machine Learning with Python k-Means Clustering/Ex_Files_ML_with_Python_k_Means_Clustering.zip 7.32 KB
Machine Learning and AI Foundations Prediction, Causation, and Statistical Inference/1 - What Is a Casual Model/1. Lady tasting tea.srt 7.38 KB
Machine Learning and AI Foundations Prediction, Causation, and Statistical Inference/3 - Correlation Does Not Imply Causation/2. Pearson on correlation and causation.srt 7.41 KB
Machine Learning and AI Foundations Prediction, Causation, and Statistical Inference/7 - The Two Cultures Contrasting Statistics and Data Mining/2. Explain vs. predict.srt 7.44 KB
Machine Learning and AI Foundations Prediction, Causation, and Statistical Inference/3 - Correlation Does Not Imply Causation/3. Correlation and regression.srt 7.5 KB
Machine Learning with Python Logistic Regression/3 - Classifying Data with Logistic Regression/3. How to build a logistic regression model in Python.srt 7.66 KB
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 7.83 KB
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
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 KB
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 8.03 KB
Machine Learning and AI Foundations Decision Trees with KNIME/3 - Introducing Classification Trees/3. How CART handles missing data using surrogates.srt 8.04 KB
Machine Learning and AI Foundations Causal Inference and Modeling/1 - Experimental Design and Statistical Controls/2. Fisher and experiments.srt 8.06 KB
Machine Learning with Python k-Means Clustering/1 - Understanding K-Means Clustering/1. What is clustering.srt 8.1 KB
Machine Learning and AI Foundations Decision Trees with KNIME/1 - Introducing Decision Trees/2. The pros and cons of decision trees.srt 8.11 KB
Machine Learning with Python Decision Trees - OneHack.us/3 - Working with Regression Trees/2. How to visualize a regression tree in Python.srt 8.15 KB
Machine Learning with Python Decision Trees - OneHack.us/3 - Working with Regression Trees/3. How to prune a regression tree in Python.srt 8.24 KB
Machine Learning with Python Decision Trees - OneHack.us/1 - Decision Trees/4. How is a regression tree built.srt 8.33 KB
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 8.42 KB
Machine Learning and AI Foundations Prediction, Causation, and Statistical Inference/6 - Prediction and Proof in Data Mining/1. Data mining vs. data dredging.srt 8.54 KB
Machine Learning and AI Foundations Decision Trees with KNIME/2 - Introducing the C5.0 Algorithm/12. When to turn off pruning.srt 8.61 KB
Machine Learning and AI Foundations Causal Inference and Modeling/2 - Conditional Probability and Bayes' Theorem/1. Turing, Enigma, and CAPTCHA.srt 8.65 KB
Machine Learning with Python Logistic Regression/1 - Regression/3. Common types of regression.srt 8.81 KB
Machine Learning and AI Foundations Decision Trees with KNIME/2 - Introducing the C5.0 Algorithm/5. Working with the prebuilt example.srt 8.82 KB
Machine Learning with Python Decision Trees - OneHack.us/1 - Decision Trees/3. How do classification trees measure impurity.srt 8.84 KB
Machine Learning with Python Decision Trees - OneHack.us/2 - Working with Classification Trees/1. How to build a classification tree in Python.srt 8.91 KB
Machine Learning and AI Foundations Decision Trees with KNIME/2 - Introducing the C5.0 Algorithm/2. Understanding the entropy calculation.srt 9.15 KB
Machine Learning with Python Logistic Regression/3 - Classifying Data with Logistic Regression/2. How to prepare data for logistic regression in Python.srt 9.29 KB
Machine Learning and AI Foundations Causal Inference and Modeling/5 - Causal Modeling with Bayesian Networks/4. Introduction to causal modeling with Bayesian networks.srt 9.35 KB
Machine Learning with Python Decision Trees - OneHack.us/1 - Decision Trees/2. How is a classification tree built.srt 9.47 KB
Machine Learning with Python Association Rules/0 - Introduction/4. Using GitHub Codespaces with this course.srt 9.51 KB
Machine Learning with Python Logistic Regression/2 - Logistic Regression/1. What is logistic regression.srt 9.81 KB
Machine Learning and AI Foundations Causal Inference and Modeling/1 - Experimental Design and Statistical Controls/7. Moderation, mediation, and lurking variables.srt 9.85 KB
Machine Learning and AI Foundations Prediction, Causation, and Statistical Inference/4 - Prediction and Proof in Statistics/6. Solution Evaluate significant finding.srt 9.93 KB
Machine Learning and AI Foundations Prediction, Causation, and Statistical Inference/3 - Correlation Does Not Imply Causation/1. What is a strong correlation.srt 10.24 KB
Machine Learning and AI Foundations Decision Trees with KNIME/1 - Introducing Decision Trees/4. A quick review of machine learning basics with examples.srt 10.36 KB
Machine Learning with Python Association Rules/1 - Association Rules/2. Frequent itemset generation.srt 10.42 KB
Machine Learning with Python Logistic Regression/0 - Introduction/4. Using GitHub Codespaces with this course.srt 10.56 KB
Machine Learning with Python Logistic Regression/2 - Logistic Regression/3. Interpreting the coefficients of logistic regression.srt 10.71 KB
Machine Learning with Python Decision Trees - OneHack.us/Ex_Files_Machine_Learning_with_Python_Decision_Trees.zip 10.8 KB
Machine Learning with Python Association Rules/1 - Association Rules/4. The FP-Growth algorithm.srt 10.91 KB
Machine Learning with Python Decision Trees - OneHack.us/1 - Decision Trees/5. How to prune a decision tree.srt 10.99 KB
Machine Learning with Python Association Rules/2 - Discovering Patterns with Association Rules/2. How to generate frequent itemsets.srt 11.03 KB
Machine Learning with Python Decision Trees - OneHack.us/3 - Working with Regression Trees/1. How to build a regression tree in Python.srt 11.04 KB
Machine Learning with Python Association Rules/1 - Association Rules/5. Evaluating association rules.srt 11.54 KB
Machine Learning and AI Foundations Prediction, Causation, and Statistical Inference/3 - Correlation Does Not Imply Causation/5. Solution What is causing what.srt 11.73 KB
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 11.79 KB
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
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
Machine Learning and AI Foundations Causal Inference and Modeling/2 - Conditional Probability and Bayes' Theorem/3. Developing an intuition for Bayes with Wordle.srt 12.59 KB
Machine Learning with Python Logistic Regression/3 - Classifying Data with Logistic Regression/4. How to interpret a logistic regression model in Python.srt 12.74 KB
Machine Learning with Python k-Means Clustering/1 - Understanding K-Means Clustering/3. Choosing the right number of clusters.srt 12.86 KB
Machine Learning and AI Foundations Prediction, Causation, and Statistical Inference/4 - Prediction and Proof in Statistics/1. Using probability to measure uncertainty.srt 13.03 KB
Machine Learning with Python Association Rules/2 - Discovering Patterns with Association Rules/3. How to create association rules.srt 13.26 KB
Machine Learning and AI Foundations Causal Inference and Modeling/1 - Experimental Design and Statistical Controls/8. Simpson's paradox.srt 13.67 KB
Machine Learning with Python Association Rules/2 - Discovering Patterns with Association Rules/4. How to evaluate association rules.srt 15.65 KB
Machine Learning and AI Foundations Causal Inference and Modeling/1 - Experimental Design and Statistical Controls/5. Control variables (ANCOVA).srt 15.74 KB
Machine Learning with Python Logistic Regression/3 - Classifying Data with Logistic Regression/1. How to explore data for logistic regression in Python.srt 19.33 KB
Machine Learning and AI Foundations Causal Inference and Modeling/3 - Prediction and Proof with Bayesian statistics/2. Bayesian T-Test with JASP.srt 19.54 KB
Machine Learning and AI Foundations Prediction, Causation, and Statistical Inference/Ex_Files_ML_and_AI_Foundations.zip 138.06 KB
Machine Learning and AI Foundations Causal Inference and Modeling/Ex_Files_ML_and_AI_Foundations_Causal_Inf_Modeling.zip 179.83 KB
Deep Learning Model Optimization and Tuning/Ex_Files_Deep_Learning_Model_Optimization_Tuning.zip 725.95 KB
Machine Learning and AI Foundations Decision Trees with KNIME/5 - Conclusion/1. Next steps.mp4 1.71 MB
Deep Learning Model Optimization and Tuning/4 - Overfitting Management/2. Regularization.mp4 1.77 MB
Machine Learning with Python k-Means Clustering/0 - Introduction/3. The tools you need.mp4 1.78 MB
Deep Learning Model Optimization and Tuning/4 - Overfitting Management/4. Dropouts.mp4 1.84 MB
Machine Learning and AI Foundations Decision Trees with KNIME/0 - Introduction/2. What you should know.mp4 1.96 MB
Machine Learning with Python Decision Trees - OneHack.us/0 - Introduction/3. The tools you need.mp4 1.96 MB
Machine Learning with Python k-Means Clustering/0 - Introduction/2. What you should know.mp4 2.04 MB
Deep Learning Model Optimization and Tuning/6 - Conclusion/1. Continuing your deep learning journey.mp4 2.13 MB
Machine Learning with Python Association Rules/0 - Introduction/2. What you should know.mp4 2.17 MB
Machine Learning with Python Logistic Regression/0 - Introduction/2. What you should know.mp4 2.24 MB
Machine Learning with Python Decision Trees - OneHack.us/0 - Introduction/2. What you should know.mp4 2.25 MB
Machine Learning and AI Foundations Decision Trees with KNIME/Ex_Files_ML_and_AI_Foundations_Decision_Trees_KNIME.zip 2.3 MB
Machine Learning and AI Foundations Producing Explainable AI (XAI) and Interpretable Machine Learning Solutions/0 - Introduction/3. What you should know.mp4 2.31 MB
Deep Learning Model Optimization and Tuning/4 - Overfitting Management/3. Regularization experiment.mp4 2.41 MB
Deep Learning Model Optimization and Tuning/3 - Tuning Back Propagation/5. Learning rate.mp4 2.43 MB
Deep Learning Model Optimization and Tuning/3 - Tuning Back Propagation/3. Optimizers.mp4 2.76 MB
Deep Learning Model Optimization and Tuning/5 - Model Tuning Exercise/5. Avoiding overfitting.mp4 2.94 MB
Machine Learning and AI Foundations Producing Explainable AI (XAI) and Interpretable Machine Learning Solutions/0 - Introduction/2. Target audience.mp4 3.02 MB
Deep Learning Model Optimization and Tuning/5 - Model Tuning Exercise/4. Tuning backpropagation.mp4 3.08 MB
Machine Learning with Python Decision Trees - OneHack.us/4 - Conclusion/1. Next steps with decision trees.mp4 3.14 MB
Machine Learning and AI Foundations Causal Inference and Modeling/0 - Introduction/2. What you should know.mp4 3.22 MB
Machine Learning with Python k-Means Clustering/3 - Conclusion/1. Next steps.mp4 3.24 MB
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
Deep Learning Model Optimization and Tuning/1 - Introduction to Deep Learning Optimization/3. An ANN model.mp4 3.35 MB
Deep Learning Model Optimization and Tuning/1 - Introduction to Deep Learning Optimization/1. What is deep learning.mp4 3.38 MB
Machine Learning and AI Foundations Decision Trees with KNIME/3 - Introducing Classification Trees/7. Evaluating the accuracy of your CART tree.mp4 3.41 MB
Machine Learning and AI Foundations Prediction, Causation, and Statistical Inference/4 - Prediction and Proof in Statistics/2. p-value review.mp4 3.42 MB
Deep Learning Model Optimization and Tuning/4 - Overfitting Management/5. Dropout experiment.mp4 3.42 MB
Machine Learning and AI Foundations Prediction, Causation, and Statistical Inference/8 - Conclusion/1. Review.mp4 3.45 MB
Deep Learning Model Optimization and Tuning/1 - Introduction to Deep Learning Optimization/4. Model optimization and tuning.mp4 3.45 MB
Deep Learning Model Optimization and Tuning/4 - Overfitting Management/1. Overfitting in ANNs.mp4 3.54 MB
Machine Learning with Python Association Rules/0 - Introduction/3. Using the exercise files.mp4 3.55 MB
Deep Learning Model Optimization and Tuning/2 - Tuning the Deep Learning Network/1. Epoch and batch size tuning.mp4 3.62 MB
Machine Learning with Python Association Rules/3 - Conclusion/1. Next steps.mp4 3.67 MB
Deep Learning Model Optimization and Tuning/5 - Model Tuning Exercise/2. Acquire and process data.mp4 3.71 MB
Machine Learning with Python Logistic Regression/4 - Conclusion/1. Next steps.mp4 3.8 MB
Machine Learning and AI Foundations Causal Inference and Modeling/2 - Conditional Probability and Bayes' Theorem/7. Challenge Conditional probability and Bayes' theorem.mp4 3.82 MB
Deep Learning Model Optimization and Tuning/5 - Model Tuning Exercise/3. Tuning the network.mp4 3.86 MB
Machine Learning with Python Decision Trees - OneHack.us/0 - Introduction/1. Making decisions with Python.mp4 3.88 MB
Deep Learning Model Optimization and Tuning/5 - Model Tuning Exercise/6. Building the final model.mp4 3.95 MB
Machine Learning and AI Foundations Decision Trees with KNIME/4 - Introducing Regression Trees/3. The math behind regression trees.mp4 4 MB
Deep Learning Model Optimization and Tuning/3 - Tuning Back Propagation/6. Learning rate experiment.mp4 4.11 MB
Machine Learning with Python k-Means Clustering/0 - Introduction/1. Getting started with Python and k-means clustering.mp4 4.11 MB
Machine Learning and AI Foundations Decision Trees with KNIME/2 - Introducing the C5.0 Algorithm/8. How C4.5 handles continuous variables.mp4 4.18 MB
Machine Learning with Python Logistic Regression/0 - Introduction/3. Using the exercise files.mp4 4.36 MB
Machine Learning and AI Foundations Decision Trees with KNIME/4 - Introducing Regression Trees/1. MPG data set.mp4 4.52 MB
Machine Learning and AI Foundations Decision Trees with KNIME/0 - Introduction/3. How to use the practice files.mp4 4.52 MB
Deep Learning Model Optimization and Tuning/3 - Tuning Back Propagation/4. Optimizer experiment.mp4 4.59 MB
Machine Learning and AI Foundations Decision Trees with KNIME/3 - Introducing Classification Trees/5. How CART handles nominal variables.mp4 4.61 MB
Deep Learning Model Optimization and Tuning/0 - Introduction/2. Prerequisites for the course.mp4 4.69 MB
Deep Learning Model Optimization and Tuning/0 - Introduction/1. Optimizing neural networks.mp4 4.72 MB
Machine Learning and AI Foundations Prediction, Causation, and Statistical Inference/4 - Prediction and Proof in Statistics/5. Challenge Evaluate significant finding.mp4 4.77 MB
Deep Learning Model Optimization and Tuning/2 - Tuning the Deep Learning Network/6. Initializing weights.mp4 4.79 MB
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 4.97 MB
Machine Learning and AI Foundations Prediction, Causation, and Statistical Inference/5 - Deduction and Induction/5. Counterfactuals Pearl on induction and causality.mp4 5.06 MB
Deep Learning Model Optimization and Tuning/3 - Tuning Back Propagation/1. Vanishing and exploding gradients.mp4 5.19 MB
Machine Learning and AI Foundations Causal Inference and Modeling/6 - Conclusion/1. Taking causality further.mp4 5.2 MB
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
Machine Learning and AI Foundations Prediction, Causation, and Statistical Inference/3 - Correlation Does Not Imply Causation/4. Challenge What is causing what.mp4 5.36 MB
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 5.37 MB
Machine Learning and AI Foundations Causal Inference and Modeling/1 - Experimental Design and Statistical Controls/4. Double blind studies.mp4 5.37 MB
Deep Learning Model Optimization and Tuning/2 - Tuning the Deep Learning Network/3. Hidden layers tuning.mp4 5.49 MB
Deep Learning Model Optimization and Tuning/1 - Introduction to Deep Learning Optimization/2. Review of artificial neural networks.mp4 5.62 MB
Deep Learning Model Optimization and Tuning/2 - Tuning the Deep Learning Network/5. Choosing activation functions.mp4 5.63 MB
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 5.68 MB
Deep Learning Model Optimization and Tuning/2 - Tuning the Deep Learning Network/4. Determining nodes in a layer.mp4 5.75 MB
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
Deep Learning Model Optimization and Tuning/0 - Introduction/3. Setting up exercise files.mp4 5.94 MB
Machine Learning and AI Foundations Decision Trees with KNIME/2 - Introducing the C5.0 Algorithm/3. How C4.5 handles missing data.mp4 5.97 MB
Machine Learning and AI Foundations Causal Inference and Modeling/3 - Prediction and Proof with Bayesian statistics/5. Challenge JASP.mp4 6.02 MB
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 6.08 MB
Machine Learning and AI Foundations Prediction, Causation, and Statistical Inference/0 - Introduction/1. Prediction, causation, and statistical inference.mp4 6.12 MB
Machine Learning and AI Foundations Prediction, Causation, and Statistical Inference/1 - What Is a Casual Model/3. What is a causal model.mp4 6.13 MB
Machine Learning with Python Logistic Regression/2 - Logistic Regression/4. Why and when to use logistic regression.mp4 6.16 MB
Machine Learning and AI Foundations Causal Inference and Modeling/2 - Conditional Probability and Bayes' Theorem/8. Solution Conditional probability and Bayes' theorem.mp4 6.21 MB
Deep Learning Model Optimization and Tuning/1 - Introduction to Deep Learning Optimization/5. The deep learning tuning process.mp4 6.23 MB
Machine Learning with Python Logistic Regression/0 - Introduction/1. Classifying data with logistic regression.mp4 6.31 MB
Machine Learning and AI Foundations Decision Trees with KNIME/2 - Introducing the C5.0 Algorithm/9. Equal size sampling.mp4 6.42 MB
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 6.44 MB
Deep Learning Model Optimization and Tuning/3 - Tuning Back Propagation/2. Batch normalization.mp4 6.55 MB
Machine Learning and AI Foundations Causal Inference and Modeling/4 - Causal Modeling with Structural Equation Modeling (SEM)/2. Introducing path analysis and SEM.mp4 6.55 MB
Machine Learning and AI Foundations Decision Trees with KNIME/4 - Introducing Regression Trees/9. Accuracy.mp4 6.59 MB
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 6.72 MB
Machine Learning with Python k-Means Clustering/1 - Understanding K-Means Clustering/2. What is k-means clustering.mp4 6.73 MB
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 6.87 MB
Machine Learning and AI Foundations Decision Trees with KNIME/3 - Introducing Classification Trees/2. What is the Gini coefficient.mp4 6.95 MB
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 6.98 MB
Machine Learning and AI Foundations Decision Trees with KNIME/3 - Introducing Classification Trees/6. A quick look at the complete CART tree.mp4 7.15 MB
Machine Learning and AI Foundations Decision Trees with KNIME/0 - Introduction/1. The basics of decision trees.mp4 7.18 MB
Machine Learning and AI Foundations Decision Trees with KNIME/1 - Introducing Decision Trees/1. What is a decision tree.mp4 7.2 MB
Machine Learning and AI Foundations Causal Inference and Modeling/4 - Causal Modeling with Structural Equation Modeling (SEM)/3. SEM example Intention.mp4 7.28 MB
Machine Learning and AI Foundations Causal Inference and Modeling/4 - Causal Modeling with Structural Equation Modeling (SEM)/5. Latent variables in SEM.mp4 7.31 MB
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
Machine Learning and AI Foundations Decision Trees with KNIME/4 - Introducing Regression Trees/7. KNIME's missing data options for regression trees.mp4 7.67 MB
Machine Learning with Python k-Means Clustering/0 - Introduction/4. Using the exercise files.mp4 7.69 MB
Machine Learning and AI Foundations Prediction, Causation, and Statistical Inference/4 - Prediction and Proof in Statistics/3. Hypothesis testing checklist.mp4 7.71 MB
Machine Learning and AI Foundations Decision Trees with KNIME/3 - Introducing Classification Trees/4. Changing the settings in KNIME.mp4 7.82 MB
Machine Learning with Python Association Rules/0 - Introduction/1. Association rule mining.mp4 7.83 MB
Machine Learning with Python Decision Trees - OneHack.us/0 - Introduction/4. Using the exercise files.mp4 7.84 MB
Machine Learning and AI Foundations Decision Trees with KNIME/4 - Introducing Regression Trees/8. Line plot.mp4 7.93 MB
Machine Learning and AI Foundations Decision Trees with KNIME/2 - Introducing the C5.0 Algorithm/4. The Give Me Some Credit data set.mp4 7.94 MB
Machine Learning and AI Foundations Causal Inference and Modeling/2 - Conditional Probability and Bayes' Theorem/4. Wordle and conditional probability.mp4 8.05 MB
Machine Learning and AI Foundations Causal Inference and Modeling/2 - Conditional Probability and Bayes' Theorem/6. Wordle and Bayes' theorem.mp4 8.34 MB
Machine Learning and AI Foundations Causal Inference and Modeling/0 - Introduction/1. Thinking about causality.mp4 8.44 MB
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 8.52 MB
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
Machine Learning and AI Foundations Decision Trees with KNIME/2 - Introducing the C5.0 Algorithm/6. KNIME settings for C4.5.mp4 8.62 MB
Deep Learning Model Optimization and Tuning/1 - Introduction to Deep Learning Optimization/6. Experiment setups for the course.mp4 8.95 MB
Machine Learning and AI Foundations Decision Trees with KNIME/4 - Introducing Regression Trees/6. Closer look at a full regression tree.mp4 9.08 MB
Deep Learning Model Optimization and Tuning/5 - Model Tuning Exercise/1. Tuning exercise Problem statement.mp4 9.13 MB
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 9.23 MB
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 9.29 MB
Machine Learning and AI Foundations Causal Inference and Modeling/1 - Experimental Design and Statistical Controls/10. Solution Moderation, mediation, or a third variable.mp4 9.46 MB
Machine Learning and AI Foundations Causal Inference and Modeling/4 - Causal Modeling with Structural Equation Modeling (SEM)/4. Myths about SEM.mp4 9.58 MB
Machine Learning with Python Decision Trees - OneHack.us/1 - Decision Trees/1. What is a decision tree.mp4 9.6 MB
Machine Learning and AI Foundations Causal Inference and Modeling/1 - Experimental Design and Statistical Controls/6. Judea Pearl Problems with control variables.mp4 9.68 MB
Machine Learning and AI Foundations Decision Trees with KNIME/3 - Introducing Classification Trees/3. How CART handles missing data using surrogates.mp4 9.77 MB
Deep Learning Model Optimization and Tuning/2 - Tuning the Deep Learning Network/2. Epoch and batch size experiment.mp4 9.86 MB
Machine Learning with Python k-Means Clustering/1 - Understanding K-Means Clustering/4. Why and when to use k-means clustering.mp4 10.04 MB
Machine Learning with Python Logistic Regression/1 - Regression/2. The anatomy of a regression model.mp4 10.06 MB
Machine Learning and AI Foundations Decision Trees with KNIME/1 - Introducing Decision Trees/2. The pros and cons of decision trees.mp4 10.06 MB
Machine Learning and AI Foundations Decision Trees with KNIME/4 - Introducing Regression Trees/5. Ordinal variable handling.mp4 10.08 MB
Machine Learning and AI Foundations Prediction, Causation, and Statistical Inference/6 - Prediction and Proof in Data Mining/2. TrainTest What can go wrong.mp4 10.14 MB
Machine Learning and AI Foundations Prediction, Causation, and Statistical Inference/5 - Deduction and Induction/4. Taleb on induction.mp4 10.17 MB
Machine Learning and AI Foundations Prediction, Causation, and Statistical Inference/5 - Deduction and Induction/3. Popper on induction and falsification.mp4 10.21 MB
Machine Learning with Python Logistic Regression/1 - Regression/1. What is regression.mp4 10.24 MB
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 10.46 MB
Machine Learning and AI Foundations Causal Inference and Modeling/5 - Causal Modeling with Bayesian Networks/3. Introducing BayesiaLab Hair and eye color.mp4 10.49 MB
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 10.53 MB
Machine Learning and AI Foundations Causal Inference and Modeling/2 - Conditional Probability and Bayes' Theorem/5. Wordle, bans, and bits.mp4 10.62 MB
Machine Learning and AI Foundations Causal Inference and Modeling/1 - Experimental Design and Statistical Controls/1. The investigator, the jury, and the judge.mp4 10.63 MB
Machine Learning with Python k-Means Clustering/2 - Segmenting Data with K-Means Clustering/2. How to evaluate and visualize clusters in Python.mp4 10.67 MB
Machine Learning with Python Logistic Regression/2 - Logistic Regression/2. Making predictions with logistic regression.mp4 10.78 MB
Machine Learning and AI Foundations Causal Inference and Modeling/5 - Causal Modeling with Bayesian Networks/2. Downloading BayesiaLab and resources.mp4 10.88 MB
Machine Learning and AI Foundations Prediction, Causation, and Statistical Inference/5 - Deduction and Induction/2. Hume on induction.mp4 10.95 MB
Machine Learning and AI Foundations Decision Trees with KNIME/4 - Introducing Regression Trees/4. How RT handles nominal variables.mp4 11.11 MB
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
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 11.24 MB
Machine Learning with Python Decision Trees - OneHack.us/2 - Working with Classification Trees/2. How to visualize a classification tree in Python.mp4 11.27 MB
Machine Learning with Python k-Means Clustering/1 - Understanding K-Means Clustering/1. What is clustering.mp4 11.54 MB
Machine Learning and AI Foundations Decision Trees with KNIME/3 - Introducing Classification Trees/1. Introducing Leo Breiman and CART.mp4 11.64 MB
Machine Learning and AI Foundations Decision Trees with KNIME/2 - Introducing the C5.0 Algorithm/2. Understanding the entropy calculation.mp4 11.72 MB
Machine Learning and AI Foundations Causal Inference and Modeling/3 - Prediction and Proof with Bayesian statistics/3. Google Optimize.mp4 11.73 MB
Machine Learning with Python Decision Trees - OneHack.us/1 - Decision Trees/4. How is a regression tree built.mp4 11.79 MB
Machine Learning and AI Foundations Prediction, Causation, and Statistical Inference/7 - The Two Cultures Contrasting Statistics and Data Mining/1. The Two Cultures.mp4 11.98 MB
Machine Learning and AI Foundations Decision Trees with KNIME/4 - Introducing Regression Trees/2. The regression tree prebuilt example.mp4 11.99 MB
Machine Learning and AI Foundations Causal Inference and Modeling/3 - Prediction and Proof with Bayesian statistics/6. Solution JASP.mp4 12.14 MB
Machine Learning with Python Association Rules/1 - Association Rules/6. Why and when to use association rules.mp4 12.21 MB
Machine Learning and AI Foundations Prediction, Causation, and Statistical Inference/7 - The Two Cultures Contrasting Statistics and Data Mining/2. Explain vs. predict.mp4 12.33 MB
Machine Learning with Python Decision Trees - OneHack.us/3 - Working with Regression Trees/2. How to visualize a regression tree in Python.mp4 12.38 MB
Machine Learning with Python Decision Trees - OneHack.us/1 - Decision Trees/2. How is a classification tree built.mp4 12.4 MB
Machine Learning and AI Foundations Prediction, Causation, and Statistical Inference/3 - Correlation Does Not Imply Causation/3. Correlation and regression.mp4 12.47 MB
Machine Learning and AI Foundations Decision Trees with KNIME/1 - Introducing Decision Trees/5. An overview of decision tree algorithms.mp4 12.49 MB
Machine Learning with Python Logistic Regression/2 - Logistic Regression/1. What is logistic regression.mp4 12.54 MB
Machine Learning and AI Foundations Prediction, Causation, and Statistical Inference/6 - Prediction and Proof in Data Mining/1. Data mining vs. data dredging.mp4 12.58 MB
Machine Learning with Python Decision Trees - OneHack.us/2 - Working with Classification Trees/3. How to prune a classification tree in Python.mp4 12.68 MB
Machine Learning and AI Foundations Decision Trees with KNIME/1 - Introducing Decision Trees/3. Introducing KNIME.mp4 12.78 MB
Machine Learning with Python Decision Trees - OneHack.us/1 - Decision Trees/3. How do classification trees measure impurity.mp4 12.86 MB
Machine Learning and AI Foundations Prediction, Causation, and Statistical Inference/1 - What Is a Casual Model/1. Lady tasting tea.mp4 12.87 MB
Machine Learning and AI Foundations Prediction, Causation, and Statistical Inference/4 - Prediction and Proof in Statistics/4. Taleb on normality, mediocristan, and extremistan.mp4 12.93 MB
Machine Learning and AI Foundations Prediction, Causation, and Statistical Inference/4 - Prediction and Proof in Statistics/6. Solution Evaluate significant finding.mp4 13.03 MB
Machine Learning and AI Foundations Causal Inference and Modeling/3 - Prediction and Proof with Bayesian statistics/1. Contrasting frequentist statistics and Bayesian statistics.mp4 13.06 MB
Machine Learning and AI Foundations Causal Inference and Modeling/2 - Conditional Probability and Bayes' Theorem/3. Developing an intuition for Bayes with Wordle.mp4 13.12 MB
Machine Learning with Python Logistic Regression/2 - Logistic Regression/3. Interpreting the coefficients of logistic regression.mp4 13.4 MB
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 13.69 MB
Machine Learning with Python Decision Trees - OneHack.us/1 - Decision Trees/6. Why and when to use a decision tree.mp4 13.7 MB
Machine Learning with Python Association Rules/1 - Association Rules/1. What are association rules.mp4 13.78 MB
Machine Learning and AI Foundations Causal Inference and Modeling/5 - Causal Modeling with Bayesian Networks/5. Bayesian Networks Black Swan case study.mp4 14.55 MB
Machine Learning and AI Foundations Prediction, Causation, and Statistical Inference/5 - Deduction and Induction/1. What are induction and deduction.mp4 14.63 MB
Machine Learning and AI Foundations Causal Inference and Modeling/1 - Experimental Design and Statistical Controls/7. Moderation, mediation, and lurking variables.mp4 15.06 MB
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 15.06 MB
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 15.07 MB
Machine Learning with Python Decision Trees - OneHack.us/3 - Working with Regression Trees/3. How to prune a regression tree in Python.mp4 15.65 MB
Machine Learning with Python Association Rules/1 - Association Rules/3. The Apriori algorithm.mp4 15.67 MB
Machine Learning with Python Decision Trees - OneHack.us/2 - Working with Classification Trees/1. How to build a classification tree in Python.mp4 15.71 MB
Machine Learning and AI Foundations Decision Trees with KNIME/2 - Introducing the C5.0 Algorithm/5. Working with the prebuilt example.mp4 15.95 MB
Machine Learning and AI Foundations Causal Inference and Modeling/5 - Causal Modeling with Bayesian Networks/4. Introduction to causal modeling with Bayesian networks.mp4 16.13 MB
Machine Learning with Python Logistic Regression/1 - Regression/3. Common types of regression.mp4 16.34 MB
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 16.44 MB
Machine Learning and AI Foundations Decision Trees with KNIME/2 - Introducing the C5.0 Algorithm/12. When to turn off pruning.mp4 16.45 MB
Machine Learning with Python Association Rules/1 - Association Rules/2. Frequent itemset generation.mp4 16.87 MB
Machine Learning and AI Foundations Causal Inference and Modeling/3 - Prediction and Proof with Bayesian statistics/4. Bayes and rare events.mp4 16.98 MB
Machine Learning and AI Foundations Causal Inference and Modeling/2 - Conditional Probability and Bayes' Theorem/2. Enigma and uncertainty.mp4 17.07 MB
Machine Learning with Python k-Means Clustering/1 - Understanding K-Means Clustering/3. Choosing the right number of clusters.mp4 17.38 MB
Machine Learning with Python Logistic Regression/3 - Classifying Data with Logistic Regression/3. How to build a logistic regression model in Python.mp4 17.76 MB
Machine Learning and AI Foundations Causal Inference and Modeling/4 - Causal Modeling with Structural Equation Modeling (SEM)/1. Sewell Wright.mp4 18.25 MB
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 18.34 MB
Machine Learning with Python Decision Trees - OneHack.us/1 - Decision Trees/5. How to prune a decision tree.mp4 19.08 MB
Machine Learning with Python Decision Trees - OneHack.us/3 - Working with Regression Trees/1. How to build a regression tree in Python.mp4 20.14 MB
Machine Learning and AI Foundations Decision Trees with KNIME/1 - Introducing Decision Trees/4. A quick review of machine learning basics with examples.mp4 20.28 MB
Machine Learning and AI Foundations Causal Inference and Modeling/1 - Experimental Design and Statistical Controls/2. Fisher and experiments.mp4 20.57 MB
Machine Learning with Python Association Rules/1 - Association Rules/5. Evaluating association rules.mp4 21.08 MB
Machine Learning and AI Foundations Prediction, Causation, and Statistical Inference/3 - Correlation Does Not Imply Causation/5. Solution What is causing what.mp4 21.13 MB
Machine Learning and AI Foundations Prediction, Causation, and Statistical Inference/3 - Correlation Does Not Imply Causation/1. What is a strong correlation.mp4 21.22 MB
Machine Learning with Python Association Rules/0 - Introduction/4. Using GitHub Codespaces with this course.mp4 21.56 MB
Machine Learning with Python Logistic Regression/0 - Introduction/4. Using GitHub Codespaces with this course.mp4 21.57 MB
Machine Learning with Python Logistic Regression/3 - Classifying Data with Logistic Regression/2. How to prepare data for logistic regression in Python.mp4 21.87 MB
Machine Learning and AI Foundations Prediction, Causation, and Statistical Inference/4 - Prediction and Proof in Statistics/1. Using probability to measure uncertainty.mp4 22.24 MB
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 23.64 MB
Machine Learning and AI Foundations Causal Inference and Modeling/1 - Experimental Design and Statistical Controls/5. Control variables (ANCOVA).mp4 23.78 MB
Machine Learning and AI Foundations Causal Inference and Modeling/2 - Conditional Probability and Bayes' Theorem/1. Turing, Enigma, and CAPTCHA.mp4 24.06 MB
Machine Learning and AI Foundations Causal Inference and Modeling/1 - Experimental Design and Statistical Controls/8. Simpson's paradox.mp4 26.04 MB
Machine Learning with Python Association Rules/1 - Association Rules/4. The FP-Growth algorithm.mp4 26.47 MB
Machine Learning with Python Association Rules/2 - Discovering Patterns with Association Rules/1. How to collect data for association rule mining.mp4 27.42 MB
Machine Learning with Python Logistic Regression/3 - Classifying Data with Logistic Regression/4. How to interpret a logistic regression model in Python.mp4 28.28 MB
Machine Learning with Python Association Rules/2 - Discovering Patterns with Association Rules/2. How to generate frequent itemsets.mp4 31.1 MB
Machine Learning and AI Foundations Causal Inference and Modeling/3 - Prediction and Proof with Bayesian statistics/2. Bayesian T-Test with JASP.mp4 33.61 MB
Machine Learning with Python Logistic Regression/3 - Classifying Data with Logistic Regression/1. How to explore data for logistic regression in Python.mp4 36.13 MB
Machine Learning and AI Foundations Causal Inference and Modeling/1 - Experimental Design and Statistical Controls/3. John Snow and natural experiments.mp4 36.75 MB
Machine Learning with Python Association Rules/2 - Discovering Patterns with Association Rules/3. How to create association rules.mp4 43.01 MB
Machine Learning with Python Association Rules/2 - Discovering Patterns with Association Rules/4. How to evaluate association rules.mp4 44.03 MB
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