This short course is aimed at students and professionals who in their work need to extract information from Big Data, identify patterns or automate decision making. The course covers a broad range of topics, from both the supervised and unsupervised learning literature, ranging from Regression, Bayesian Inference, Classification, Clustering, Dimensionality Reduction and Feature Learning. An example application of selected topics is provided. Emphasis is given on the implementation of the models, giving the course an applied machine learning aspect. Attendees will implement several models through practical exercises in Python. There are no prerequisites in Machine Learning, however basic matrix manipulation and programming skills are assumed. After completing the course, attendees will have a broad knowledge of Machine Learning and its potential application. They will be able to tackle problems faced when handling Big Data and identify potential solutions through different machine learning models along with their implementation.