Machine Learning in Python – Dr Irina Chelysheva
17, 18 and 19 May, 11.00-12.00, Microsoft Teams

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To book, please visit: oxford.onlinesurveys.ac.uk/machine-learning-in-python-17-18-and-19-may

In this short series we will get familiar with the most common Machine Learning algorithms and apply them in Python, using scikit-learn and beyond. We will perform end-to-end ML projects for various data types and research questions. We will select the best ML method by evaluating their performance, use feature selection approaches, apply cross-validation and make the actual predictions. For those previously participating in the first two sessions – new(!) third session will be covering unsupervised ML methods and their applications.

Topics to be covered – Overview of ML methods and algorithms, unsupervised vs supervised learning – Evaluation of the performance of the algorithms and the choice of the best one – Application of ML to different problems – classification vs regression, different types of data – Hands-on end-to-end ML analysis in Python with sklearn – Feature selection methods and their application – Metrics and evaluation of the performance – using cross-validation and making further predictions

Learning Objectives:
-perform end-to-end machine learning analysis of the dataset using Python; – select the best machine learning algorithm for particular dataset; – apply ML methods to both regression and classification problems; – perform the feature selection; – make the predictions and evaluate the performance of the ML algorithms

Prior knowledge required
Basic experience in Python is strongly desired

Pre-course work
Setup the Python IDE and install the required packages (see below), download the codes, which will be provided for each session

Type of session
Combination of presentation element and hands-on demo session: walk-through code with some exercises to perform live

Software required
Python3 – www.python.org
Python SciPy libraries:
scipy
numpy
matplotlib
pandas
sklearn (shorten from scikit-learn)
Any suitable IDE (tutor will use Spyder – www.spyder-ide.org