This is the best Decision Tree video i've found on the internet. I am at med school and learning about ML, this helped me so much, thx !!!

Why no comments? Your video helped me a lot. Thank you.

in which website can we execute it

Dear Michael, thank you for this great explanation! As a newb, it took me some time to realise how to actually get the figure shown (figure.show()). I have another question: how do I get the figure to show the 'names' of my data points? I want to take a closer look at outlier cases, so it would be great if the figure would show their case number. Hope you can help!

Thankssss

thank you, very helpful

Top man. Great video.

thanks

Thank you so much for your video. Would it be possible to adapt this for a 3D-histogram? Thank you so much for your help

Thank you! 🙂

Thanks!!

Wow.. light green brackets on a white background... not very well thought out.

Is it correct that anaconda needs 300 GB space? AWS free tier is 30 GB . The AWS anaconda subscription wants a new instance to have large size EC2.. but I set it (the launch new 🆕 instance from the AWS subscription page) to the free tier and it seems to have anaconda installed. I tried installing anaconda manually on a regular default EC2 but it says it failed to finish because of no space 🌌

I wonder if you could go over the Rolling Linear Regression one more time. Thank you, Mike.

The Best One

ill get this error (base) sathish@SATHISHs-Air ~ % snotebook zsh: no matches found: local[2]

😋 Promo*SM

If you want to learn about image augmentation for deep learning (synthetic image data for deep learning), check out A Data Odyssey's video: kzbin.info/www/bejne/Z3nPn6iZhqxmfKcsi=uwBKIb3dTzuAzhnd

This is a free preview video from the Synthetic Data: Advanced Concepts and Applications Course: www.linkedin.com/learning/synthetic-data-advanced-concepts-and-applications/unlock-the-power-of-synthetic-data 00:00: What is a robust dataset? 00:27: Real data lacks enough rare classes and edge cases (healthcare and PII) 00:47: Identify edge cases and rare classes in real data 01:02: Check that real data is representative of real-world scenarios with minimal domain gaps 01:15: Make sure your model validation process has enough real data edge cases in the test set 01:28: Don't let your machine learning model learn that all synthetic data is an edge case 1:40: Synthetic data can be iteratively improved over time to account for more edge cases

Thank you for such simple information.

This is a free preview video from the Synthetic Data: Advanced Concepts and Applications Course: www.linkedin.com/learning/synthetic-data-advanced-concepts-and-applications/unlock-the-power-of-synthetic-data 00:00: Why do you need synthetic data 00:26: Privacy issues with real data (healthcare and PII) 00:46: Safety issues with real data (self-driving cars and drones) 01:20: Real data collection and labeling are not scalable (annotating medical images) 02:04: The real data you have is not ideal (not enough data to properly train a model)

Code: github.com/mGalarnyk/Python_Tutorials/blob/master/Sklearn/KMeans/KMeans.ipynb 00:00: What is K-Means Clustering (KMeans.ipynb) 01:05: Interactive K-Means Visualization (www.naftaliharris.com/blog/visualizing-k-means-clustering/) 03:13: Import Libraries, Visualize Data, and Standardize Data (KMeans.ipynb) 03:58: Clustering Data using K-Means (KMeans.ipynb) 04:35: Visually Evaluate the Clusters (KMeans.ipynb) 06:05: Compute the Silhouette Score for your Clusters (KMeans.ipynb) 06:53: K-Means Potential Assumptions/Issues (KMeans.ipynb)

Code: github.com/mGalarnyk/Python_Tutorials/blob/master/Sklearn/HierarchicalClustering/HierarchicalClustering.ipynb 00:00: Introduction (HierarchicalClustering.ipynb) 00:14: Hierarchical Clustering Animation (HierarchicalClustering.ipynb) 00:45: Linkage Criterion Determines which Distance to Use Between Sets of Observations (HierarchicalClustering.ipynb) 01:31: How Hierarchical Clustering Works (HierarchicalClustering.ipynb) 01:50: Comparing K-Means and Hierarchical Clustering Algorithms (HierarchicalClustering.ipynb) 02:33: K-Means Starting Position of Cluster Centers Problem (HierarchicalClustering.ipynb) 02:46: Hierarchical Clustering in Python

After fitting a random forest on data, you can view all the individual decision trees. This visualization was made for the blog post Visualizing Decision Trees with Python (Scikit-learn, Graphviz, Matplotlib): medium.com/p/1c50b4aa68dc

Code: github.com/mGalarnyk/Python_Tutorials/tree/master/Sklearn/Logistic_Regression Logistic Regression Visualization: mfviz.com/binary-predictions/ 00:00: Introduction (LogisticRegression.ipynb) 01:05: Visualizing Relationship between worst_concave_points and diagnosis (LogisticRegression.ipynb) 03:13: What is Logistic Regression (LogisticRegression.ipynb) 03:47: Showing Predictions for Logistic Regression (LogisticRegression.ipynb) 05:46: Advantages and Disadvantages of Logistic Regression (LogisticRegression.ipynb) 07:10: Evaluating Logistic Regression with accuracy and a confusion matrix (LogisticRegression.ipynb) 08:35: True Negatives, False Positives, False Negatives, and True Positives (LogisticRegression.ipynb) 09:27: Sensitivity, Specificity, Type 1 Error, and Type 2 Error (LogisticRegression.ipynb) 11:37: Trading True Positive and True Negatives through an ROC Curve (LogisticRegression.ipynb) 12:49: Interactive Logistic Regression Visualization (mfviz.com/binary-predictions/) 14:51: Logistic Regression on the Titanic Dataset

Unhearable, the audio is really bad

thanks!

is this the same course is also available on linkedin learning?

Code here: github.com/mGalarnyk/Python_Tutorials/tree/master/Sklearn/CART Video based on this blog: medium.com/p/9663d683c952 09:31: Train Test Split (TrainTestSplit.ipynb) 18:17: Decision Tree Exercise with Titantic Data (ExerciseDecisionTree.ipynb) 18:52: Solution to Decision Tree Exercise with Titantic Data (ExerciseDecisionTreeSolution.ipynb) 19:18: Arrange Data into Features Matrix and Target Vector (ExerciseDecisionTreeSolution.ipynb) 21:02: Split Data into Training and Testing Sets (ExerciseDecisionTreeSolution.ipynb) 21:12: Fit a Decision Tree on the Titantic Dataset (ExerciseDecisionTreeSolution.ipynb) 21:56: Make Predictions on the Testing Set and Calculate the Accuracy (ExerciseDecisionTreeSolution.ipynb) 22:10: Compare the Testing Accuracy to the Null Accuracy (ExerciseDecisionTreeSolution.ipynb) 23:38: Confusion Matrix of Titanic Predictions (ExerciseDecisionTreeSolution.ipynb) 24:14: Feature Importance Metric from Decision Trees (ExerciseDecisionTreeSolution.ipynb) 24:52: Creating a Decision Tree Visualization using Matplotlib and Graphviz (ExerciseDecisionTreeSolution.ipynb)

Thanks!

Great step by step video and still works. Thank you Micheal!

Good materials. Needs to work on teaching and presentation skills. Don’t need to say “okay” after every sentence!

This video seems helpful, but the sound is so feeble.

thank u so much sir.... was trying to do this and was pissed

Thanks. this is very helpful.

Why I am getting this error in 4:45 ? Error in FUN(left) : invalid argument type

The link no longer works :'( Can you provide the new one (if it exists), please?

Thanks so much Michael

I have random forest, gradient boosting, and adaboost model prediction. Can I visualize it in one scatter plot? I have been hardly thinking to figure it out actually. If anyone knows how to visualize the model in one scatter plot like PCA, I really apreciate it.

Thank you

Thank you for providing so perfect video. I also meet the problem that my connection stays setting up for hours... and never complete. I tried to fresh page but it didn't work either. Is there any solution?

Thank you, Michael! greatly appreciate the step by step process! very helpful.

Great video, saw the post on LinkedIn

should i choose ubunto even if i am windows user?

Great video! Thanks! I have a problem, my connection stays setting up for hours... any idea why?

If you are having trouble with this following this tutorial, I made an updated tutorial: kzbin.info/www/bejne/nmWoZY2sis2MkKc Accompanying blog: www.kdnuggets.com/2023/01/setup-jupyterhub-tljh-aws-ec2.html?fbclid=IwAR1aswRoPmV4Q-3tviwAq4TedBxj_-HopnguGS3TXRBYjJeWEY8y2u9vuE8

Accompanying Blog Post: www.kdnuggets.com/2023/01/setup-jupyterhub-tljh-aws-ec2.html?fbclid=IwAR1aswRoPmV4Q-3tviwAq4TedBxj_-HopnguGS3TXRBYjJeWEY8y2u9vuE8

This was really helpful. The process got hustle-free

Fantastic. This helped me out a lot