MachineLearning


Introduction to MachineLearning.

📋 Content

📄 Description

This directory contains a series of notebooks designed to build your machine learning knowledge, focusing on fundamental algorithms and techniques. Using Python with libraries like scikit-learn, NumPy, and Matplotlib, you’ll explore a range of machine learning methods, from supervised learning (classification and regression) to unsupervised learning (clustering and dimensionality reduction), as well as ensemble methods for improved performance.

By the end of these notebooks, you should feel more comfortable with:

  • Understanding key machine learning concepts, including linear models, decision trees, support vector machines, ensemble methods, and unsupervised learning techniques.

  • Implementing and optimizing machine learning models using scikit-learn, including data preprocessing, model training, hyperparameter tuning, and performance evaluation.

  • Applying machine learning to real-world problems, with a focus on medical applications such as disease prediction, patient segmentation, and anomaly detection in health data.

📣 Current announcements

In this skill track, the notebooks build on each other. Therefore, complete them in the order given!

❗ Course requirements

  • you should have completed all previous skill tracks
  • in this notebook we use data from open-source databases (the references are at the bottom of the page); in Google Colab the data is loaded automatically at the beginning of the notebook
  • have a look at the theoretical basics before you start with the notebooks

📒 Syllabus

  • Introduction to Machine Learning Open In Colab
  • End-to-End Machine Learning Open In Colab
  • Classification Open In Colab
  • Training Models Open In Colab
  • SVMs Open In Colab
  • Decision Tree Open In Colab
  • Ensemble Learning & Random Forests Open In Colab
  • Dimensionality Reduction Open In Colab
  • Unsupervised Learning Open In Colab

☝️ References

In this skill track, you’ll work with different Open-Source datasets: