Curriculum Overview: Identifying Regression Machine Learning Scenarios
Identify regression machine learning scenarios
Curriculum Overview: Identifying Regression Machine Learning Scenarios
This curriculum provides a structured pathway for mastering the identification and application of Regression within the context of Microsoft Azure AI Fundamentals (AI-900). Regression is a fundamental supervised learning technique focused on predicting continuous numerical values.
Prerequisites
Before starting this module, students should have a baseline understanding of the following:
- AI Workload Basics: Familiarity with what Artificial Intelligence is and its common workloads (Vision, NLP, etc.).
- Data Fundamentals: Understanding the difference between Features (input variables) and Labels (the target value to predict).
- Supervised Learning: Knowledge that supervised learning uses labeled datasets to train models.
- Basic Math: Comfort with the concept of independent and dependent variables ().
Module Breakdown
| Module | Title | Difficulty | Description |
|---|---|---|---|
| 1 | Introduction to Supervised Learning | Beginner | Distinguishing between labeled and unlabeled data tasks. |
| 2 | Regression vs. The World | Beginner | Comparing Regression against Classification and Clustering. |
| 3 | Identifying Regression Scenarios | Intermediate | Recognizing real-world problems that require numerical prediction. |
| 4 | Evaluating Regression Models | Intermediate | Understanding metrics like MAE, MSE, and . |
Learning Objectives per Module
Module 1: Introduction to Supervised Learning
- Define supervised learning and its reliance on labeled data.
- Identify the role of features () and labels () in a training dataset.
Module 2: Regression vs. The World
- Contrast Regression (predicting numbers) with Classification (predicting categories).
- Distinguish between supervised techniques and Clustering (unsupervised grouping).
Module 3: Identifying Regression Scenarios
- Analyze a business problem to determine if the output is a continuous value.
- Identify common use cases such as price prediction, weather forecasting, and demand estimation.
Module 4: Evaluating Regression Models
- Define Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE).
- Explain how the Coefficient of Determination () represents model fit.
Visual Anchors
Deciding on an ML Technique
Simple Linear Regression Visualization
Success Metrics
To demonstrate mastery of this curriculum, the learner must be able to:
- Scenario Recognition: Given 10 business problems, correctly identify the "Regression" scenarios with 100% accuracy (e.g., predicting stock prices vs. detecting spam emails).
- Metric Selection: Explain why Mean Squared Error (MSE) is more sensitive to outliers than Mean Absolute Error (MAE).
- Concept Distinction: Explain the difference between Linear Regression (predicting a value) and Logistic Regression (predicting a probability for classification).
Real-World Application
Regression is the engine behind many financial and scientific decisions:
- Real Estate: Estimating the market value of a home based on square footage, location, and age.
- Retail: Forecasting future sales volumes to manage inventory levels and avoid stockouts.
- Meteorology: Predicting the exact temperature or rainfall amount for the following day.
- Agriculture: Estimating crop yields based on soil quality, rainfall, and fertilizer usage.
[!IMPORTANT] Always remember: If you are asking "How much?" or "How many?", you are likely looking at a Regression problem. If you are asking "Which one?", you are looking at Classification.