About this course
An applied introduction to machine learning for analysts who want to move beyond descriptive reporting. Delegates work through the full workflow of framing a problem, preparing data, selecting features, training models and evaluating them. Methods covered include linear and logistic regression, decision trees, random forests, k-means clustering and principal component analysis, with attention to overfitting and model interpretation.
Why this course
Organisations collect large amounts of operational and financial data but use little of it to classify, segment or anticipate outcomes. Analysts who understand machine learning can find patterns that standard reports miss and can judge when a model is reliable. This also reduces dependence on outside consultants for routine analytical work.
What delegates will be able to do
By the end of the course, delegates will be able to
- Frame a business question as a supervised or unsupervised learning problem
- Prepare, clean and split a dataset for model training and testing
- Train regression, classification and clustering models using Python libraries
- Evaluate models with accuracy, precision, recall and error measures
- Explain model results and limitations to non-technical colleagues
Who should attend
Data analysts, statisticians, economists, researchers and BI staff in banks, public institutions and companies. Delegates should be comfortable with basic statistics. Some exposure to Python or another analysis tool is helpful.
How it is delivered
Hands-on sessions in Jupyter notebooks with prepared Python code, worked examples on financial and public sector datasets, and a short group modelling project. It is available as a public course, in-house at your premises, or as live virtual sessions. Duration and dates are confirmed in your quote, and in-house groups can have the examples and exercises fitted to their organisation.