This repository was created for educational purposes to explore and better understand the Scikit-learn library and its applications in machine learning algorithms.
The contents of this repository are based on the document SISTCA_2024-25_3DA_ING_Team1_Scikit-learn.pdf, which presents the use of Scikit-learn in a detailed yet accessible manner. The document serves as a step-by-step learning resource, combining theoretical insights with practical coding examples.
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Tutorial Code Examples
Implementations of machine learning techniques introduced in the document, including:- Regression
- Classification
- Clustering
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Exercise and Challenge Solutions
Solutions to exercises and coding challenges formulated in theSISTCA_2024-25_3DA_ING_Team1_Scikit-learn.pdfdocument. These tasks are designed to deepen the understanding of the presented methods and encourage hands-on practice. -
interesting.txt
A list of curated links to external resources, documents, and papers that either extend the topics discussed in the main document or explore related areas not covered in it.
Before exploring the code or reviewing the solutions, it is strongly recommended to first read the
SISTCA_2024-25_3DA_ING_Team1_Scikit-learn.pdf document.
To make the most of this repository:
- Carefully go through the content of the PDF to understand the theoretical background and code structure.
- Try to solve the exercises and challenges independently.
- Refer to the solutions in this repository only after making your own attempt.
Following this approach will help build a solid understanding of Scikit-learn and fundamental machine learning workflows through active learning and experimentation.
Happy learning and coding!