Applied Machine Learning

This applied level moves learners from using AI tools to building machine learning models, with emphasis on Python, data, and the full model-development workflow from preparation through evaluation.

What You Will Learn

• Python for AI and machine learning.
• NumPy and Pandas for data processing.
• Essential mathematics and statistics for ML.
• Regression, classification, and clustering.
• Model evaluation, overfitting, and generalization.

Training Modules

• Variables, functions, data structures, and libraries.
• Data cleaning, transformation, and analysis.
• Averages, variance, probability, and essential math concepts.
• Features, labels, and dataset splitting.
• Training, testing, and model selection.
• Accuracy, precision, recall, and core metrics.
• Responsible ML principles and deployment introduction.

Practical Work

• Prepare realistic or simulated datasets.
• Train and compare multiple models.
• Analyze errors and improve performance.

Applied Project

Build a machine learning solution for a classification or prediction problem, covering data preparation, training, evaluation, and presentation of results.

Expected Outcomes

• Use Python to process and analyze data.
• Prepare datasets for machine learning.
• Train and test foundational models.
• Select suitable evaluation metrics.
• Interpret results and document an end-to-end ML experiment.

Prerequisites

• Successful completion of Level 1 is mandatory.
• Direct entry to Level 2 is not permitted.

Progression Requirement

Level 3 requires successful completion of this level, its assessments, and project.