Support Vector Machines in Python

Below you can view the code used to create introductory machine learning code in Python, using specifically the Support Vector Machines (SVM) algorithm, in the Jupyter environment.

Support Vector Machines in Python

A screenshot of the environment is depicted below:

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The SVM syntax inside the Jupyter environment
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Machine Learning cheat sheet

A nice overview of which algorithm to use, depending on your problem at hand is provided by the official website of Python’s Scikit-learn library.

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via Choosing the right estimator — scikit-learn 0.19.1 documentation

Principal Component Analysis in Python

Below you can view the code used to create introductory machine learning code in Python, using specifically the Principal Component Analysis (PCA) algorithm, in the Jupyter environment.

PCA in Python

A screenshot of the environment is depicted below:

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Understanding the principal components inside the Jupyter Notebook

Accounting for the style and color, when using in the Seaborn library

Below you can view the code used to create advanced grid visualizations in Python, by adjusting the style and color as well, using Seaborn, in the Jupyter environment.

Grids, Style and Colour using Seaborn

A screenshot of the environment is depicted below:

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Inside the Jupyter Notebook using advanced visualization techniques

Color palette for Matplotlib

Visit the link below, to get access to the full palette of Matplotlib. Once you set the plot that you want to visualize, you can add as an additional argument to the function, one of the strings described in this site. There is also sample code that shows exactly the structure of the function.

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Sample palette

via color example code: colormaps_reference.py — Matplotlib 2.0.2 documentation

k Means Algorithm in Python

Below you can view the code used to create introductory machine learning code in Python, using specifically the k-Means algorithm, in the Jupyter environment.

k Means Algorithm

A screenshot of the environment is depicted below:

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Comparing the original plot (right) with the one after running the algorithm (left)