Machine Learning with Python

Welcome to the course!

This page contains the material for the Machine Learning with Python course at Sendai KOSEN on 25th to 28th February, 2020.

Please send your assignment answers for grading at latest on Tuesday, 3rd March. You can send them as email attachments to vesa.ollikainen@metropolia.fi.

Note: for Day 3, one assignment is enough. You can send the answers in ipynb or pdf format. Thank you.

Day 1: Introduction and working with data

Learning goals:

1. Understand the basics of machine learning.
2. Learn to process, examine and visualise data with pandas Python library.

Day 2: Clustering

Learning goals:

1. Learn the idea and application area of clustering.
2. Understand the theory of k-means and hierarchical clustering.
3. Learn to carry out clustering tasks with sklearn Python library.

Day 3: Classification

Learning goals:

1. Learn the performance metrics of classifiers.
2. Understand the principles of kNN, decision tree, and random forest classifiers.
3. Learn the validation techniques.
4. Learn to carry out classification tasks with sklearn Python library.

Day 4: Regression and association rule mining

Learning goals:

1. Learn the idea of multiple linear and logistic regression.
2. Learn how to evaluate a regression model.
3. Understand the idea of association rule mining.
4. Learn to carry out linear and logistic regression with sklearn Python library.
5. Learn to carry out association analysis with apyori Python library.

Manuals and documentation