Introduction to Applied Machine Learning

This module, "Introduction to Machine Learning Applications," provides a foundational understanding of machine learning, including the machine learning process, classification, regression, unsupervised learning, and reinforcement learning. Participants will also explore deep learning and semi-supervised learning, gaining valuable insights into various machine learning techniques and concepts.

"Machine Learning in the Real World" delves into the practical application of machine learning, covering topics such as generalization, features and transformations of raw data, precision agriculture, and explainability and accuracy. Participants will gain a deeper understanding of how machine learning is implemented in real-world scenarios and the considerations involved.

The "Learning Data" module focuses on essential aspects of data for machine learning, including sources of training data, ethical issues, bias in data sources, noise, and data cleaning. Participants will also explore the significance of setting up a data pipeline and understanding data protection laws.

The "Machine Learning Projects" module provides insights into the machine learning process lifecycle, problem definition, assessing the applicability of machine learning in business, and understanding different kinds of wrong outcomes. Additionally, participants will gain advanced knowledge in tasks such as deep learning for identifying metastatic breast cancer and the implications of false positives and false negatives in machine learning projects.