https://github.com/rubynixx/machine_learning

Example python notebooks that go through different machine learning methods with descriptions and code.
https://github.com/rubynixx/machine_learning

Last synced: about 1 year ago
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Example python notebooks that go through different machine learning methods with descriptions and code.

• Host: GitHub
• URL: https://github.com/rubynixx/machine_learning
• Owner: RubyNixx
• Created: 2025-02-12T13:52:46.000Z (over 1 year ago)
• Default Branch: main
• Last Pushed: 2025-03-26T13:38:54.000Z (about 1 year ago)
• Last Synced: 2025-03-26T14:43:17.304Z (about 1 year ago)
• Language: Jupyter Notebook
• Size: 9.9 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

          
# Machine Learning Resources

 Notebooks from the [Python Data Science Handbook](https://github.com/jakevdp/PythonDataScienceHandbook/tree/master):

05.01-What-Is-Machine-Learning.ipynb

05.02-Introducing-Scikit-Learn.ipynb

05.03-Hyperparameters-and-Model-Validation.ipynb

05.04-Feature-Engineering.ipynb

05.05-Naive-Bayes.ipynb

05.07-Support-Vector-Machines.ipynb

05.08-Random-Forests.ipynb

05.09-Principal-Component-Analysis.ipynb

05.10-Manifold-Learning.ipynb

05.11-K-Means.ipynb

05.12-Gaussian-Mixtures.ipynb

05.13-Kernel-Density-Estimation.ipynb

05.14-Image-Features.ipynb

05.15-Learning-More.ipynb

 Notebooks from my university lectures: 

When you click on these notebooks, you have the option to open in google colab.

[Supervised Machine Learning](https://github.com/RubyNixx/machine_learning/blob/main/1_Supervised_Machine_Learning.ipynb)

Summary stats, finding relationships between variables, correlation, scaling, splitting data into train-test, linear regression, evaluation metrics, decision tree, artifical neural networks (ANN) 

[Support_Vector_Machine_Classification](https://github.com/RubyNixx/machine_learning/blob/main/2_Support_Vector_Machine_Classification.ipynb)

Build a Support Vector Machine that will find the optimal hyperplane that maximizes the margin between two toy data classes using gradient descent.

It's a supervised machine learning algorithm which can be used for both classification or regression problems. But it's usually used for classification. Given 2 or more labeled classes of data, it acts as a discriminative classifier, formally defined by an optimal hyperplane that seperates all the classes. New examples that are then mapped into that same space can then be categorized based on on which side of the gap they fall.

*What are Support Vectors?*

Support vectors are the data points nearest to the hyperplane, the points of a data set that, if removed, would alter the position of the dividing hyperplane. Because of this, they can be considered the critical elements of a data set, they are what help us build our SVM.

*Use cases:* 

Classification, regression (time series prediction, etc) , outlier detection, clustering

As a rule of thumb, SVMs are great for relatively small data sets with fewer outliers.

Other algorithms (Random forests, deep neural networks, etc.) require more data but almost always come up with very robust models.

The decision of which classifier to use depends on your dataset and the general complexity of the problem.

"Premature optimization is the root of all evil (or at least most of it) in programming." - Donald Knuth, CS Professor (Turing award speech 1974)