This SAS course develops theoretical and practical skills in supervised learning, from basic algorithms to advanced deep learning techniques. The program blends theoretical lessons with hands-on sessions using Python frameworks and libraries. By the end, students will be prepared to apply machine learning algorithms in real-world scenarios.

OBJECTIVES

Familiarize participants with supervised learning tools for regression and classification.

Develop skills for critical assessment of outputs from machine learning algorithms.

Enable familiarity with the Python implementation of supervised and deep learning algorithms, using advanced Python frameworks and libraries.

PREREQUISITES

Knowledge of Python

SAS in Data Mining and Analysis (optional but beneficial)

PROGRAMME

Basic concepts of machine learning

Supervised machine learning algorithms: linear regression, logistic regression, feedforward and recurrent neural networks

Feature selection

Model performance evaluation (metrics, cross-validation)

Deep Learning