The Machine Learning course provides a gentle introduction to the concepts and techniques of machine learning using the Python programming language and the scikit-learn library. In this one-day course, you will gain both theoretical knowledge and hands-on experience implementing various machine learning algorithms and applying them to real-world data. We use the Microsoft Windows operating system for this course.

1. Introduction to Machine Learning

Basic terms and concepts of machine learning
Types of machine learning: Supervised, Unsupervised
Installing and setting up the environment (Python, scikit-learn, Jupyter Notebook)
 

2. Data pre-processing

Data loading and basic data analysis
Data cleaning and editing
Data normalisation
 

3. Clustering

Basic principles
Implementation in scikit-learn
Cluster Visualisation
K-Means Clustering
Hierarchical clustering
DBSCAN (Density-Based Spatial Clustering of Applications with Noise)