| 668D0E57BE129CBEB72FC3AA626D34B769B535B0

Download

Name	0	DOWNLOAD Copy Link
Total Size	2.3 GB
Total Files	437
Last Seen	2024-12-22 00:06
Hash	668D0E57BE129CBEB72FC3AA626D34B769B535B0

/Machine Learning with Python Association Rules/
Exercises_Link - OneHack.us.txt	0.1 KB
description.html	1.3 KB
/Machine Learning with Python Logistic Regression/
Exercises_Link.txt	0.1 KB
description.html	1.4 KB
/
$10 ChatGPT for 1 Year & More.txt	0.3 KB
/Machine Learning with Python k-Means Clustering/
description.html	1.0 KB
Ex_Files_ML_with_Python_k_Means_Clustering.zip	7.5 KB
/Machine Learning and AI Foundations Causal Inference and Modeling/
description.html	1.0 KB
Ex_Files_ML_and_AI_Foundations_Causal_Inf_Modeling.zip	184.1 KB
/Machine Learning with Python Decision Trees - OneHack.us/
description.html	1.1 KB
Ex_Files_Machine_Learning_with_Python_Decision_Trees.zip	11.1 KB
/Machine Learning and AI Foundations Decision Trees with KNIME/
description.html	1.1 KB
Ex_Files_ML_and_AI_Foundations_Decision_Trees_KNIME.zip	2.4 MB
/Deep Learning Model Optimization and Tuning/
description.html	1.2 KB
Ex_Files_Deep_Learning_Model_Optimization_Tuning.zip	743.4 KB
/6 - Conclusion/
1. Continuing your deep learning journey.srt	1.2 KB
1. Continuing your deep learning journey.mp4	2.2 MB
/0 - Introduction/
1. Making decisions with Python.srt	1.3 KB
2. What you should know.srt	2.0 KB
3. The tools you need.srt	2.1 KB
4. Using the exercise files.srt	2.6 KB
3. The tools you need.mp4	2.1 MB
2. What you should know.mp4	2.4 MB
1. Making decisions with Python.mp4	4.1 MB
4. Using the exercise files.mp4	8.2 MB
/0 - Introduction/
1. Getting started with Python and k-means clustering.srt	1.3 KB
2. What you should know.srt	2.0 KB
3. The tools you need.srt	2.1 KB
4. Using the exercise files.srt	2.7 KB
3. The tools you need.mp4	1.9 MB
2. What you should know.mp4	2.1 MB
1. Getting started with Python and k-means clustering.mp4	4.3 MB
4. Using the exercise files.mp4	8.1 MB
/Machine Learning and AI Foundations Prediction, Causation, and Statistical Inference/
description.html	1.3 KB
Ex_Files_ML_and_AI_Foundations.zip	141.4 KB
/.../5 - Model Tuning Exercise/
4. Tuning backpropagation.srt	1.4 KB
5. Avoiding overfitting.srt	1.4 KB
2. Acquire and process data.srt	1.6 KB
3. Tuning the network.srt	2.0 KB
6. Building the final model.srt	2.3 KB
1. Tuning exercise Problem statement.srt	7.0 KB
5. Avoiding overfitting.mp4	3.1 MB
4. Tuning backpropagation.mp4	3.2 MB
2. Acquire and process data.mp4	3.9 MB
3. Tuning the network.mp4	4.0 MB
6. Building the final model.mp4	4.1 MB
1. Tuning exercise Problem statement.mp4	9.6 MB
/0 - Introduction/
1. Optimizing neural networks.srt	1.4 KB
3. Setting up exercise files.srt	3.6 KB
2. Prerequisites for the course.srt	4.7 KB
2. Prerequisites for the course.mp4	4.9 MB
1. Optimizing neural networks.mp4	4.9 MB
3. Setting up exercise files.mp4	6.2 MB
/4 - Overfitting Management/
3. Regularization experiment.srt	1.4 KB
2. Regularization.srt	1.4 KB
5. Dropout experiment.srt	1.6 KB
4. Dropouts.srt	1.9 KB
1. Overfitting in ANNs.srt	3.3 KB
2. Regularization.mp4	1.9 MB
4. Dropouts.mp4	1.9 MB
3. Regularization experiment.mp4	2.5 MB
5. Dropout experiment.mp4	3.6 MB
1. Overfitting in ANNs.mp4	3.7 MB
/0 - Introduction/
1. Exploring the world of explainable AI and interpretable machine learning.srt	1.6 KB
3. What you should know.srt	1.6 KB
2. Target audience.srt	2.2 KB
3. What you should know.mp4	2.4 MB
2. Target audience.mp4	3.2 MB
1. Exploring the world of explainable AI and interpretable machine learning.mp4	5.2 MB
/0 - Introduction/
2. What you should know.srt	1.6 KB
1. The basics of decision trees.srt	2.2 KB
3. How to use the practice files.srt	2.3 KB
2. What you should know.mp4	2.1 MB
3. How to use the practice files.mp4	4.7 MB
1. The basics of decision trees.mp4	7.5 MB
/5 - Conclusion/
1. Next steps.srt	1.7 KB
1. Next steps.mp4	1.8 MB
/8 - Conclusion/
1. Review.srt	1.7 KB
1. Review.mp4	3.6 MB
/0 - Introduction/
1. Classifying data with logistic regression.srt	1.8 KB
2. What you should know.srt	1.9 KB
3. Using the exercise files.srt	2.2 KB
4. Using GitHub Codespaces with this course.srt	10.8 KB
2. What you should know.mp4	2.4 MB
3. Using the exercise files.mp4	4.6 MB
1. Classifying data with logistic regression.mp4	6.6 MB
4. Using GitHub Codespaces with this course.mp4	22.6 MB
/0 - Introduction/
1. Association rule mining.srt	1.9 KB
2. What you should know.srt	2.0 KB
3. Using the exercise files.srt	2.2 KB
4. Using GitHub Codespaces with this course.srt	9.7 KB
2. What you should know.mp4	2.3 MB
3. Using the exercise files.mp4	3.7 MB
1. Association rule mining.mp4	8.2 MB
4. Using GitHub Codespaces with this course.mp4	22.6 MB
/.../4 - Introducing Regression Trees/
1. MPG data set.srt	2.0 KB
9. Accuracy.srt	3.7 KB
3. The math behind regression trees.srt	3.7 KB
8. Line plot.srt	3.9 KB
7. KNIME's missing data options for regression trees.srt	4.6 KB
6. Closer look at a full regression tree.srt	5.4 KB
5. Ordinal variable handling.srt	5.6 KB
2. The regression tree prebuilt example.srt	6.5 KB
4. How RT handles nominal variables.srt	6.6 KB
3. The math behind regression trees.mp4	4.2 MB
1. MPG data set.mp4	4.7 MB
9. Accuracy.mp4	6.9 MB
7. KNIME's missing data options for regression trees.mp4	8.0 MB
8. Line plot.mp4	8.3 MB
6. Closer look at a full regression tree.mp4	9.5 MB
5. Ordinal variable handling.mp4	10.6 MB
4. How RT handles nominal variables.mp4	11.6 MB
2. The regression tree prebuilt example.mp4	12.6 MB
/.../3 - Tuning Back Propagation/
6. Learning rate experiment.srt	2.0 KB
5. Learning rate.srt	2.1 KB
4. Optimizer experiment.srt	2.2 KB
3. Optimizers.srt	2.6 KB
2. Batch normalization.srt	3.2 KB
1. Vanishing and exploding gradients.srt	4.3 KB
5. Learning rate.mp4	2.5 MB
3. Optimizers.mp4	2.9 MB
6. Learning rate experiment.mp4	4.3 MB
4. Optimizer experiment.mp4	4.8 MB
1. Vanishing and exploding gradients.mp4	5.4 MB
2. Batch normalization.mp4	6.9 MB
/.../4 - Prediction and Proof in Statistics/
2. p-value review.srt	2.0 KB
5. Challenge Evaluate significant finding.srt	2.6 KB
4. Taleb on normality, mediocristan, and extremistan.srt	3.7 KB
3. Hypothesis testing checklist.srt	6.7 KB
6. Solution Evaluate significant finding.srt	10.2 KB
1. Using probability to measure uncertainty.srt	13.3 KB
2. p-value review.mp4	3.6 MB
5. Challenge Evaluate significant finding.mp4	5.0 MB
3. Hypothesis testing checklist.mp4	8.1 MB
4. Taleb on normality, mediocristan, and extremistan.mp4	13.6 MB
6. Solution Evaluate significant finding.mp4	13.7 MB
1. Using probability to measure uncertainty.mp4	23.3 MB
/.../3 - Introducing Classification Trees/
7. Evaluating the accuracy of your CART tree.srt	2.1 KB
5. How CART handles nominal variables.srt	2.7 KB
6. A quick look at the complete CART tree.srt	3.7 KB
2. What is the Gini coefficient.srt	4.1 KB
4. Changing the settings in KNIME.srt	4.6 KB
1. Introducing Leo Breiman and CART.srt	6.1 KB
3. How CART handles missing data using surrogates.srt	8.2 KB
7. Evaluating the accuracy of your CART tree.mp4	3.6 MB
5. How CART handles nominal variables.mp4	4.8 MB
2. What is the Gini coefficient.mp4	7.3 MB
6. A quick look at the complete CART tree.mp4	7.5 MB
4. Changing the settings in KNIME.mp4	8.2 MB
3. How CART handles missing data using surrogates.mp4	10.2 MB
1. Introducing Leo Breiman and CART.mp4	12.2 MB
/.../1 - What Is a Casual Model/
2. Why causation matters in a business setting.srt	2.2 KB
3. What is a causal model.srt	3.4 KB
1. Lady tasting tea.srt	7.6 KB
2. Why causation matters in a business setting.mp4	3.5 MB
3. What is a causal model.mp4	6.4 MB
1. Lady tasting tea.mp4	13.5 MB
/0 - Introduction/
1. Prediction, causation, and statistical inference.srt	2.3 KB
1. Prediction, causation, and statistical inference.mp4	6.4 MB
/.../2 - Introducing the C5.0 Algorithm/
8. How C4.5 handles continuous variables.srt	2.4 KB
9. Equal size sampling.srt	3.4 KB
1. Ross Quinlan, ID3, C4.5, and C5.0.srt	3.7 KB
7. How C4.5 handles nominal variables.srt	3.7 KB
10. A quick look at the complete C4.5 tree.srt	4.4 KB
11. Evaluating the accuracy of your C4.5 tree.srt	4.5 KB
3. How C4.5 handles missing data.srt	4.5 KB
4. The Give Me Some Credit data set.srt	4.7 KB
6. KNIME settings for C4.5.srt	5.0 KB
12. When to turn off pruning.srt	8.8 KB
5. Working with the prebuilt example.srt	9.0 KB
2. Understanding the entropy calculation.srt	9.4 KB
8. How C4.5 handles continuous variables.mp4	4.4 MB
1. Ross Quinlan, ID3, C4.5, and C5.0.mp4	6.0 MB
3. How C4.5 handles missing data.mp4	6.3 MB
9. Equal size sampling.mp4	6.7 MB
10. A quick look at the complete C4.5 tree.mp4	6.8 MB
7. How C4.5 handles nominal variables.mp4	7.8 MB
4. The Give Me Some Credit data set.mp4	8.3 MB
6. KNIME settings for C4.5.mp4	9.0 MB
11. Evaluating the accuracy of your C4.5 tree.mp4	9.7 MB
2. Understanding the entropy calculation.mp4	12.3 MB
5. Working with the prebuilt example.mp4	16.7 MB
12. When to turn off pruning.mp4	17.2 MB
/.../2 - Conditional Probability and Bayes' Theorem/
7. Challenge Conditional probability and Bayes' theorem.srt	2.4 KB
8. Solution Conditional probability and Bayes' theorem.srt	4.0 KB
2. Enigma and uncertainty.srt	5.8 KB
5. Wordle, bans, and bits.srt	6.7 KB
6. Wordle and Bayes' theorem.srt	6.7 KB
4. Wordle and conditional probability.srt	7.0 KB
1. Turing, Enigma, and CAPTCHA.srt	8.9 KB
3. Developing an intuition for Bayes with Wordle.srt	12.9 KB
7. Challenge Conditional probability and Bayes' theorem.mp4	4.0 MB
8. Solution Conditional probability and Bayes' theorem.mp4	6.5 MB
4. Wordle and conditional probability.mp4	8.4 MB
6. Wordle and Bayes' theorem.mp4	8.7 MB
5. Wordle, bans, and bits.mp4	11.1 MB
3. Developing an intuition for Bayes with Wordle.mp4	13.8 MB
2. Enigma and uncertainty.mp4	17.9 MB
1. Turing, Enigma, and CAPTCHA.mp4	25.2 MB
/0 - Introduction/
2. What you should know.srt	2.5 KB
1. Thinking about causality.srt	2.7 KB
2. What you should know.mp4	3.4 MB
1. Thinking about causality.mp4	8.8 MB
/.../1 - Introduction to Deep Learning Optimization/
3. An ANN model.srt	2.6 KB
4. Model optimization and tuning.srt	2.6 KB
1. What is deep learning.srt	2.8 KB
6. Experiment setups for the course.srt	3.5 KB
2. Review of artificial neural networks.srt	4.5 KB
5. The deep learning tuning process.srt	5.3 KB
3. An ANN model.mp4	3.5 MB
1. What is deep learning.mp4	3.5 MB
4. Model optimization and tuning.mp4	3.6 MB
2. Review of artificial neural networks.mp4	5.9 MB
5. The deep learning tuning process.mp4	6.5 MB
6. Experiment setups for the course.mp4	9.4 MB
/.../3 - Correlation Does Not Imply Causation/
4. Challenge What is causing what.srt	2.9 KB
2. Pearson on correlation and causation.srt	7.6 KB
3. Correlation and regression.srt	7.7 KB
1. What is a strong correlation.srt	10.5 KB
5. Solution What is causing what.srt	12.0 KB
4. Challenge What is causing what.mp4	5.6 MB
2. Pearson on correlation and causation.mp4	11.7 MB
3. Correlation and regression.mp4	13.1 MB
5. Solution What is causing what.mp4	22.2 MB
1. What is a strong correlation.mp4	22.3 MB
/2 - Logistic Regression/
4. Why and when to use logistic regression.srt	2.9 KB
2. Making predictions with logistic regression.srt	7.0 KB
1. What is logistic regression.srt	10.0 KB
3. Interpreting the coefficients of logistic regression.srt	11.0 KB
4. Why and when to use logistic regression.mp4	6.5 MB
2. Making predictions with logistic regression.mp4	11.3 MB
1. What is logistic regression.mp4	13.2 MB
3. Interpreting the coefficients of logistic regression.mp4	14.1 MB
/.../1 - Experimental Design and Statistical Controls/
4. Double blind studies.srt	3.0 KB
9. Challenge Moderation, mediation, or a third variable.srt	3.5 KB
6. Judea Pearl Problems with control variables.srt	4.5 KB
1. The investigator, the jury, and the judge.srt	5.1 KB
10. Solution Moderation, mediation, or a third variable.srt	5.8 KB
2. Fisher and experiments.srt	8.3 KB
7. Moderation, mediation, and lurking variables.srt	10.1 KB
3. John Snow and natural experiments.srt	12.5 KB
8. Simpson's paradox.srt	14.0 KB
5. Control variables (ANCOVA).srt	16.1 KB
4. Double blind studies.mp4	5.6 MB
9. Challenge Moderation, mediation, or a third variable.mp4	6.2 MB
10. Solution Moderation, mediation, or a third variable.mp4	9.9 MB
6. Judea Pearl Problems with control variables.mp4	10.1 MB
1. The investigator, the jury, and the judge.mp4	11.1 MB
7. Moderation, mediation, and lurking variables.mp4	15.8 MB
2. Fisher and experiments.mp4	21.6 MB
5. Control variables (ANCOVA).mp4	24.9 MB
8. Simpson's paradox.mp4	27.3 MB
3. John Snow and natural experiments.mp4	38.5 MB
/.../2 - Tuning the Deep Learning Network/
6. Initializing weights.srt	3.0 KB
3. Hidden layers tuning.srt	3.4 KB
1. Epoch and batch size tuning.srt	3.5 KB
5. Choosing activation functions.srt	3.5 KB
4. Determining nodes in a layer.srt	3.6 KB
2. Epoch and batch size experiment.srt	5.2 KB
1. Epoch and batch size tuning.mp4	3.8 MB
6. Initializing weights.mp4	5.0 MB
3. Hidden layers tuning.mp4	5.8 MB
5. Choosing activation functions.mp4	5.9 MB
4. Determining nodes in a layer.mp4	6.0 MB
2. Epoch and batch size experiment.mp4	10.3 MB
/.../3 - Prediction and Proof with Bayesian statistics/
5. Challenge JASP.srt	3.0 KB
3. Google Optimize.srt	5.5 KB
4. Bayes and rare events.srt	6.4 KB
6. Solution JASP.srt	6.5 KB
1. Contrasting frequentist statistics and Bayesian statistics.srt	7.2 KB
2. Bayesian T-Test with JASP.srt	20.0 KB
5. Challenge JASP.mp4	6.3 MB
3. Google Optimize.mp4	12.3 MB
6. Solution JASP.mp4	12.7 MB
1. Contrasting frequentist statistics and Bayesian statistics.mp4	13.7 MB
4. Bayes and rare events.mp4	17.8 MB
2. Bayesian T-Test with JASP.mp4	35.2 MB
/4 - Conclusion/
1. Next steps with decision trees.srt	3.0 KB
1. Next steps with decision trees.mp4	3.3 MB
/3 - Conclusion/
1. Next steps.srt	3.1 KB
1. Next steps.mp4	3.4 MB
/4 - Conclusion/
1. Next steps.srt	3.4 KB
1. Next steps.mp4	4.0 MB
/3 - Conclusion/
1. Next steps.srt	3.5 KB
1. Next steps.mp4	3.8 MB
/.../1 - What Are XAI and IML/
2. Variable importance and reason codes.srt	3.6 KB
7. KNIME support of global and local explanations.srt	3.6 KB
6. XAI for debugging models.srt	3.7 KB
5. Local and global explanations.srt	3.8 KB
3. Comparing IML and XAI.srt	6.9 KB
1. Understanding the what and why your models predict.srt	7.1 KB
4. Trends in AI making the XAI problem more prominent.srt	8.6 KB
5. Local and global explanations.mp4	5.6 MB
7. KNIME support of global and local explanations.mp4	5.6 MB
6. XAI for debugging models.mp4	7.3 MB
2. Variable importance and reason codes.mp4	9.7 MB
3. Comparing IML and XAI.mp4	11.0 MB
1. Understanding the what and why your models predict.mp4	17.2 MB
4. Trends in AI making the XAI problem more prominent.mp4	19.2 MB
/.../5 - Causal Modeling with Bayesian Networks/
2. Downloading BayesiaLab and resources.srt	3.7 KB
5. Bayesian Networks Black Swan case study.srt	5.1 KB
3. Introducing BayesiaLab Hair and eye color.srt	6.4 KB
1. Judea Pearl and the causal revolution.srt	6.8 KB
4. Introduction to causal modeling with Bayesian networks.srt	9.6 KB
1. Judea Pearl and the causal revolution.mp4	9.0 MB
3. Introducing BayesiaLab Hair and eye color.mp4	11.0 MB
2. Downloading BayesiaLab and resources.mp4	11.4 MB
5. Bayesian Networks Black Swan case study.mp4	15.3 MB
4. Introduction to causal modeling with Bayesian networks.mp4	16.9 MB
/.../5 - Deduction and Induction/
5. Counterfactuals Pearl on induction and causality.srt	3.9 KB
2. Hume on induction.srt	5.9 KB
4. Taleb on induction.srt	6.6 KB
3. Popper on induction and falsification.srt	6.8 KB
1. What are induction and deduction.srt	6.8 KB
5. Counterfactuals Pearl on induction and causality.mp4	5.3 MB
4. Taleb on induction.mp4	10.7 MB
3. Popper on induction and falsification.mp4	10.7 MB
2. Hume on induction.mp4	11.5 MB
1. What are induction and deduction.mp4	15.3 MB
/1 - Association Rules/
6. Why and when to use association rules.srt	4.2 KB
1. What are association rules.srt	6.8 KB
3. The Apriori algorithm.srt	6.9 KB
2. Frequent itemset generation.srt	10.7 KB
4. The FP-Growth algorithm.srt	11.2 KB
5. Evaluating association rules.srt	11.8 KB
6. Why and when to use association rules.mp4	12.8 MB
1. What are association rules.mp4	14.4 MB
3. The Apriori algorithm.mp4	16.4 MB
2. Frequent itemset generation.mp4	17.7 MB
5. Evaluating association rules.mp4	22.1 MB
4. The FP-Growth algorithm.mp4	27.8 MB
/.../6 - Prediction and Proof in Data Mining/
3. AB testing during the evaluation phase.srt	4.3 KB
2. TrainTest What can go wrong.srt	7.4 KB
1. Data mining vs. data dredging.srt	8.7 KB
3. AB testing during the evaluation phase.mp4	6.4 MB
2. TrainTest What can go wrong.mp4	10.6 MB
1. Data mining vs. data dredging.mp4	13.2 MB
/.../4 - Causal Modeling with Structural Equation Modeling (SEM)/
2. Introducing path analysis and SEM.srt	4.5 KB
5. Latent variables in SEM.srt	4.6 KB
6. Finding direction of causality with SEM (PSAT).srt	5.4 KB
3. SEM example Intention.srt	6.4 KB
4. Myths about SEM.srt	6.4 KB
1. Sewell Wright.srt	6.6 KB
2. Introducing path analysis and SEM.mp4	6.9 MB
6. Finding direction of causality with SEM (PSAT).mp4	7.0 MB
3. SEM example Intention.mp4	7.6 MB
5. Latent variables in SEM.mp4	7.7 MB
4. Myths about SEM.mp4	10.0 MB
1. Sewell Wright.mp4	19.1 MB
/.../2 - Healthy Skepticism about Our Data and Our Results/
1. Skepticism about data Truman 1948 Election Poll.srt	4.5 KB
3. Skepticism about causes Is X really causing Y.srt	4.7 KB
2. Skepticism about results Is that really the best predictor.srt	6.0 KB
1. Skepticism about data Truman 1948 Election Poll.mp4	7.2 MB
3. Skepticism about causes Is X really causing Y.mp4	8.9 MB
2. Skepticism about results Is that really the best predictor.mp4	11.0 MB
/6 - Conclusion/
1. Taking causality further.srt	4.5 KB
1. Taking causality further.mp4	5.5 MB
/.../1 - Understanding K-Means Clustering/
4. Why and when to use k-means clustering.srt	4.7 KB
2. What is k-means clustering.srt	6.2 KB
1. What is clustering.srt	8.3 KB
3. Choosing the right number of clusters.srt	13.2 KB
2. What is k-means clustering.mp4	7.1 MB
4. Why and when to use k-means clustering.mp4	10.5 MB
1. What is clustering.mp4	12.1 MB
3. Choosing the right number of clusters.mp4	18.2 MB
/.../1 - Introducing Decision Trees/
1. What is a decision tree.srt	5.1 KB
5. An overview of decision tree algorithms.srt	5.9 KB
3. Introducing KNIME.srt	6.2 KB
2. The pros and cons of decision trees.srt	8.3 KB
4. A quick review of machine learning basics with examples.srt	10.6 KB
1. What is a decision tree.mp4	7.5 MB
2. The pros and cons of decision trees.mp4	10.5 MB
5. An overview of decision tree algorithms.mp4	13.1 MB
3. Introducing KNIME.mp4	13.4 MB
4. A quick review of machine learning basics with examples.mp4	21.3 MB
/1 - Decision Trees/
6. Why and when to use a decision tree.srt	5.1 KB
1. What is a decision tree.srt	7.4 KB
4. How is a regression tree built.srt	8.5 KB
3. How do classification trees measure impurity.srt	9.1 KB
2. How is a classification tree built.srt	9.7 KB
5. How to prune a decision tree.srt	11.3 KB
1. What is a decision tree.mp4	10.1 MB
4. How is a regression tree built.mp4	12.4 MB
2. How is a classification tree built.mp4	13.0 MB
3. How do classification trees measure impurity.mp4	13.5 MB
6. Why and when to use a decision tree.mp4	14.4 MB
5. How to prune a decision tree.mp4	20.0 MB
/1 - Regression/
1. What is regression.srt	5.4 KB
2. The anatomy of a regression model.srt	6.4 KB
3. Common types of regression.srt	9.0 KB
2. The anatomy of a regression model.mp4	10.5 MB
1. What is regression.mp4	10.7 MB
3. Common types of regression.mp4	17.1 MB
/.../2 - Segmenting Data with K-Means Clustering/
2. How to evaluate and visualize clusters in Python.srt	5.9 KB
4. How to interpret the results of k-means clustering in Python.srt	8.2 KB
3. How to find the right number of clusters in Python.srt	8.2 KB
1. How to segment data with k-means clustering in Python.srt	12.1 KB
2. How to evaluate and visualize clusters in Python.mp4	11.2 MB
3. How to find the right number of clusters in Python.mp4	14.4 MB
4. How to interpret the results of k-means clustering in Python.mp4	15.8 MB
1. How to segment data with k-means clustering in Python.mp4	24.8 MB
/.../2 - Working with Classification Trees/
2. How to visualize a classification tree in Python.srt	6.7 KB
3. How to prune a classification tree in Python.srt	7.3 KB
1. How to build a classification tree in Python.srt	9.1 KB
2. How to visualize a classification tree in Python.mp4	11.8 MB
3. How to prune a classification tree in Python.mp4	13.3 MB
1. How to build a classification tree in Python.mp4	16.5 MB
/.../7 - The Two Cultures Contrasting Statistics and Data Mining/
4. Applying the two methods at work.srt	6.8 KB
2. Explain vs. predict.srt	7.6 KB
3. Comparing CRISP-DM and the scientific method.srt	8.0 KB
1. The Two Cultures.srt	8.1 KB
3. Comparing CRISP-DM and the scientific method.mp4	11.8 MB
1. The Two Cultures.mp4	12.6 MB
2. Explain vs. predict.mp4	12.9 MB
4. Applying the two methods at work.mp4	15.8 MB
/.../3 - Classifying Data with Logistic Regression/
3. How to build a logistic regression model in Python.srt	7.8 KB
2. How to prepare data for logistic regression in Python.srt	9.5 KB
4. How to interpret a logistic regression model in Python.srt	13.0 KB
1. How to explore data for logistic regression in Python.srt	19.8 KB
3. How to build a logistic regression model in Python.mp4	18.6 MB
2. How to prepare data for logistic regression in Python.mp4	22.9 MB
4. How to interpret a logistic regression model in Python.mp4	29.7 MB
1. How to explore data for logistic regression in Python.mp4	37.9 MB
/.../3 - Working with Regression Trees/
2. How to visualize a regression tree in Python.srt	8.3 KB
3. How to prune a regression tree in Python.srt	8.4 KB
1. How to build a regression tree in Python.srt	11.3 KB
2. How to visualize a regression tree in Python.mp4	13.0 MB
3. How to prune a regression tree in Python.mp4	16.4 MB
1. How to build a regression tree in Python.mp4	21.1 MB
/.../2 - Discovering Patterns with Association Rules/
2. How to generate frequent itemsets.srt	11.3 KB
1. How to collect data for association rule mining.srt	12.1 KB
3. How to create association rules.srt	13.6 KB
4. How to evaluate association rules.srt	16.0 KB
1. How to collect data for association rule mining.mp4	28.8 MB
2. How to generate frequent itemsets.mp4	32.6 MB
3. How to create association rules.mp4	45.1 MB
4. How to evaluate association rules.mp4	46.2 MB
Total files 437

Download