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Day 4: Working with images and text
28 - Finding clusters when there are none
28 - Finding clusters when there are none
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28 - Finding clusters when there are none-.pdf
za ogled datoteke.
◄ 27 - k-Means clustering
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Announcements
Discussion
Re-training program (tentative)
Re-training agenda (tentative)
01 - Workflows in Orange
02 - Basic data exploration
03 - Saving your work
04 - Loading your own data set
Data files folder
05 - Classification
06 - Classification trees
07 - Model inspection
08 - Classification accuracy
09 - How to cheat
10 - Cross-validation
11 - A few more classifiers
14 - How to correctly perform test and score
Data files folder
17 - Linear regression
18 - Regularization
19 - Regularization and accuracy on test set
21 - Evaluating regression
23 - Hierarchical clustering
24 - A clustering example
25 - Discovering clusters
Data files folder
26 - Cluster interpretation
27 - k-Means clustering
35 - Image embedding
36 - Images and classification
Data files folder
Instructional videos of Orange
Kaggle data repository and more
Data Mining and Machine Learning: Fundamental Concepts and Algorithms
Mining of Massive Datasets (Stanford)
Deep Learning (MIT - the book)
Data files folder
12 - A sneaky way to cheat
13 - Cheating works even on randomized data
15 - Model scoring
16 - Choosing the decision threshold
20 - Prediction of tissue age from level of methylation
22 - Feature scoring and selection
Data files folder
35 - Image embedding ►