After completing the course, the student will master the most typical machine learning techniques and understand their potential uses. In addition to theoretical understanding, the student will be able to apply the methods they have learned to solve practical problems and have a basic understanding of good practices related to the implementation of machine learning and artificial intelligence applications.
Algorithms used:
- Introduction to Machine Learning
- Typical steps in the machine learning workflow
- Basics of data processing (Z-score, Box-Cox, etc.)
- Model performance measurement (MSE, F1, etc.)
- Naive Bayes
- Decision trees and Random forest
- k Nearest Neighbour
- k-Means
- Linear Regression (Hill Climbing and Gradient Descent)
In the /docs/docs is located the learning diary entries and images used in them.
This repository has the codes for the course where the algorithms are implemented from scratch. Tasks have # IMPLEMENT comments where the implementation is needed.
In folder notebooks are jupyter notebooks that are used in the courses exercises for training the models.