Understand the fundamental concepts and techniques of machine learning, including supervised and unsupervised learning, deep learning, and model evaluation.

Preprocess data to make it suitable for use in machine learning algorithms, including data collection, cleaning, transformation, and reduction.

Apply supervised learning techniques such as regression, decision trees, and ensemble methods to predict output variables based on input variables.

Apply unsupervised learning techniques such as clustering, dimensionality reduction, and association rule mining to find patterns and relationships in data without labeled output variables.

Select the best machine learning model for a given task and evaluate its performance using metrics such as accuracy, precision, and recall.

Understand ethical considerations in machine learning, such as bias in data and models, algorithmic fairness, and privacy
and security.