Artificial Learning Techniques

3-Day Intensive Course for Analysts, Data Practitioners & Applied AI Learners
3 Intense Days
7 Hours per Day (Split into two 3.5-hour sessions)

Learning Path Visual

From pattern recognition to real-world detection and insight:

Day 1: Foundations of Supervised Learning
Learn the logic and process behind supervised learning — training models on labeled data to predict outcomes. Explore algorithms like linear regression, decision trees, and support vector machines (SVMs), using structured datasets and Python tools.

Day 2: Unsupervised Learning & Pattern Discovery
Move into exploratory techniques using unlabeled data. Learn how clustering (e.g., k-means, DBSCAN) and dimensionality reduction (e.g., PCA) can uncover hidden patterns, segments, and structural insights in complex datasets.

Day 3: Anomaly Detection in Real-World Systems
Learn how to detect outliers, fraud, failures, and rare events using anomaly detection techniques. Apply both supervised and unsupervised models to identify deviation from expected patterns — crucial in cybersecurity, operations, and finance.

Course Overview

This hands-on course introduces essential machine learning techniques used to classify, cluster, and detect patterns in real-world data. Designed for learners with basic Python skills or data literacy, the course emphasizes practical modeling, interpretability, and relevance to everyday problems.

Whether you’re detecting equipment failures, classifying customer behavior, or cleaning messy datasets, this course gives you the tools and confidence to apply artificial learning methods effectively.

You’ll learn how to:

• Train and evaluate models using labeled datasets

• Use unsupervised methods to cluster and reduce high-dimensional data

• Detect outliers and anomalies using statistical and ML-based techniques

• Choose the right technique based on data and business needs

• Interpret model outputs and avoid common pitfalls

• Apply artificial learning techniques to real-world cases

What’s Inside Each Day

Day 1 — Foundations of Supervised Learning

• Labeled data and prediction pipelines

• Classification vs regression: when to use what

• Core algorithms: linear regression, decision trees, SVMs

• Model training, validation, overfitting, and performance metrics

• Workshop: Predict student performance using real data
  Toolkit: Model selection guide + Python sklearn notebook
  Focus: Accuracy • Structure • Predictive Power

Day 2 — Unsupervised Learning & Pattern Discovery

• Unlabeled data and discovery tasks

• Clustering: k-means, hierarchical, DBSCAN

• Dimensionality reduction: PCA, t-SNE

• Applications: market segmentation, topic modeling, anomaly grouping

• Workshop: Cluster customer behavior from transaction logs
  Toolkit: Clustering playbook + visual explanation pack
  Focus: Structure Discovery • Insights • Visualization

Day 3 — Anomaly Detection in Real-World Systems

• Defining “anomaly” and when to look for it

• Statistical thresholds, isolation forests, autoencoders

• Real use cases: fraud detection, equipment failure, access abuse

• Combining detection with alert systems and dashboards

• Workshop: Detect anomalies in IoT sensor or finance data
  Toolkit: Detection toolkit + evaluation checklist
  Focus: Risk Detection • Rare Events • Business Safety

Course Goals

By the end of this course, you will be able to:

• Select and apply appropriate learning techniques to real problems

• Train, evaluate, and compare models using best practices

• Uncover hidden structures in unlabeled data

• Detect and report on anomalies for business-critical systems

• Communicate ML results clearly and responsibly to stakeholders

Who Should Take This Course?

This course is for IT professionals, managers, and analysts who need to understand and manage risks in a business and compliance context.

Class Reference: AL/10
Form Updated on: 06/19/2025 (Version 1)
Last Modified on: 06/19/2025

Program Note
Participants will receive downloadable Python notebooks, evaluation rubrics, algorithm cheat sheets, and guided project files for supervised, unsupervised, and anomaly detection models.