Machine Learning

This comprehensive course covers the fundamentals of Machine Learning including supervised and unsupervised learning algorithms, model evaluation, feature engineering and more.

Learn how to process and represent data, perform model evaluation, and feature engineering, and understand machine learning concepts.

Discuss the future and impact of Machine Learning on society, including ethics and safety concerns. Gain a strong understanding of Machine Learning and its applications by the end of the course.

Key Learning Outcomes:

• Define machine learning and its different types (supervised and unsupervised) and understand their applications.
• Apply supervised learning algorithms, including linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), and k-nearest neighbours (k-NN).
• Implement unsupervised learning techniques, such as K-means clustering.
• Evaluate machine learning models and perform hyperparameter tuning to improve model performance.
• Perform feature engineering and dimensionality reduction techniques, such as feature extraction, feature selection, feature scaling, and PCA.
• Analyse the future and trends of machine learning, including its impact on society and the ethics and safety concerns associated with machine learning.
• Synthesize the concepts and techniques of machine learning into a comprehensive understanding of the field.