Machine Learning Fundamentals

Our ML Fundamental course is a two-day machine learning online course, presented quarterly by one of our expert trainers. The training provides a foundational understanding of AI concepts and applications. The course is a blend of theoretical and practical aspects and covers the following topics:

Data pipelines : the importance of effective data pipelines. Overview of data collection process. Methods for conducting an explorative analysis of your data. Data quality considerations and how to address them. Steps to prepare data for model ingestion. How to reduce the dimensionality of your data. Overview of feature engineering.

Taxonomy and supervised learning : why should you care about machine learning? Differences between supervised, unsupervised, semi-supervised and self-supervised learning, machine learning vs deep learning linear and logistic regression overview, linear and non-linear support vector machine overview, K-nearest neighbours overview decision tree overview random forest overview gradient boosted trees overview.

Unsupervised and deep learning : k-means clustering overview hierarchical clustering overview gaussian mixture models overview dbscan overview what are the building blocks of neural networks? how are they combined into larger networks? Convolutional neural network overview recurrent neural network overview autoencoder overview.

Practical session: Guided examples regarding the visualisation and preprocessing of datasets. demonstration of the use of machine learning models for recurring breast cancer classification. Application of machine learning models to predict the estimated time of arrival of ships in a synthetic dataset. How to use anomaly detection models on a simple network intrusion dataset.