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 ($y = f(x)).
Module Breakdown
| Module | Title | Difficulty | Description |
|---|---|---|---|
| 1 | Introduction to Supervised Learning | Beginner | Distinguishing between labeled and unlabeled data tasks. |
| 2 | Regression vs. The World | Beginner | Comparing Regression against Classification and Clustering. |
| 3 | Identifying Regression Scenarios | Intermediate | Recognizing real-world problems that require numerical prediction. |
| 4 | Evaluating Regression Models | Intermediate | Understanding metrics like MAE, MSE, and R^2. |
Learning Objectives per Module
Module 1: Introduction to Supervised Learning
- Define supervised learning and its reliance on labeled data.
- Identify the role of features (xy) in a training dataset.
Module 2: Regression vs. The World
- Contrast Regression (predicting numbers) with Classification (predicting categories).
- Distinguish between supervised techniques and Clustering (unsupervised grouping).
Module 3: Identifying Regression Scenarios
- Analyze a business problem to determine if the output is a continuous value.
- Identify common use cases such as price prediction, weather forecasting, and demand estimation.
Module 4: Evaluating Regression Models
- Define Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE).
- Explain how the Coefficient of Determination (R^2$) 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.