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

Get involved with the future of Artificial Intelligence

Technology is giving life the potential to flourish like never before… or to self-destruct. Let’s make a difference!

That is the motto of the initiative hat attempts to map the future of AI. There is also a survey that you can fill out, although it is highly recommended that you spend some time in the entire website:

The future of AI

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