MODULE 2

Machine Learning & Deep Learning Essentials

Understand how AI systems learn from data, the machine learning lifecycle, and the critical considerations for model evaluation and governance.

Parts

4-5

Hours

Quiz Questions

Learning Objectives

Upon completing this module, you will be able to:

◈ Distinguish between supervised, unsupervised, and reinforcement learning

◈ Describe the end-to-end machine learning pipeline

◈ Explain neural network concepts at a conceptual level

◈ Interpret common model evaluation metrics

◈ Identify data quality issues and their impact on AI systems

◈ Recognize common model risks including drift and adversarial attacks

Explore the three main approaches to machine learning: supervised learning, unsupervised learning, and reinforcement learning.

Understand the complete journey from raw data to deployed model: collection, preprocessing, training, validation, and deployment.

Learn how neural networks work at a conceptual level: layers, activation functions, and backpropagation explained simply.

Master the key metrics for assessing model performance: accuracy, precision, recall, F1 score, and ROC-AUC.

Understand data labeling challenges, bias in training data, and the emerging threat of data poisoning attacks.

Explore model drift, adversarial attacks, the black box problem, and other critical risks in AI systems.

Test your understanding with 30 questions covering all topics from this module. You need 80% to pass.

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