Core Machine Learning Concepts

Quick Answer: Machine learning trains models on data to learn patterns and make predictions without explicit programming. Key concepts include features (inputs), labels (outputs), training data (for learning), validation data (for tuning), and test data (for evaluation). Supervised learning uses labeled data; unsupervised learning finds patterns in unlabeled data.

What Is Machine Learning?

Machine learning (ML) is a subset of artificial intelligence where computer systems learn from data and improve their performance over time without being explicitly programmed for every possible scenario. Instead of writing rules like "if temperature > 100 then alert", you provide examples of normal and abnormal temperatures and let the model learn the boundary.

Traditional Programming vs. Machine Learning

In traditional programming, you write the rules. In machine learning, the algorithm discovers the rules by analyzing patterns in the data.

Features and Labels

Understanding features and labels is essential for the AI-900:

Features (also called attributes or input variables) are the characteristics of the data that the model uses to make predictions. Think of features as the "questions" the model considers.

Labels (also called target variables or output variables) are the values the model tries to predict. Think of labels as the "answers" the model produces.

On the Exam: When a question describes a dataset with input columns and an output column, the input columns are features and the output column is the label. If the question says "predict the price based on size, location, and age" — size, location, and age are features; price is the label.

Training, Validation, and Test Data

Machine learning requires splitting your data into three subsets:

Training Data (typically 60-80% of all data)

• Used to teach the model patterns
• The model adjusts its internal parameters based on this data
• Larger training sets generally produce better models

Validation Data (typically 10-20% of all data)

• Used to tune the model during training
• Helps prevent overfitting by evaluating the model on data it has not trained on
• Used to select the best model configuration (hyperparameters)

Test Data (typically 10-20% of all data)

• Used to evaluate final model performance on completely unseen data
• Provides an unbiased estimate of how the model will perform in production
• Only used AFTER the model is fully trained and tuned

Total Dataset
├── Training Data (70%)  → Model learns patterns here
├── Validation Data (15%) → Model is tuned here
└── Test Data (15%)       → Final evaluation here

On the Exam: The key distinction: training data teaches the model, validation data tunes it, and test data provides the final unbiased evaluation. A question might ask which dataset is used to prevent overfitting (validation) or which provides an unbiased performance estimate (test).

Overfitting and Underfitting

Supervised vs. Unsupervised Learning

Supervised Learning

The model learns from labeled data — data where both features and labels are known. The "supervision" comes from the labeled examples that guide the learning process.

Types of supervised learning:

• Regression — predict continuous numerical values (price, temperature, quantity)
• Classification — predict discrete categories (spam/not spam, disease/no disease)

Unsupervised Learning

The model discovers patterns in unlabeled data — data where only features are known, with no predefined labels. The model finds natural groupings or structures in the data.

Types of unsupervised learning:

• Clustering — group similar items together (customer segments, document topics)

Comparison

The Machine Learning Workflow

The end-to-end ML process follows these steps:

1. Define the problem — What business question are you trying to answer?
2. Collect data — Gather relevant data from databases, APIs, files
3. Prepare data — Clean, transform, handle missing values, select features
4. Split data — Divide into training, validation, and test sets
5. Choose an algorithm — Select the appropriate ML algorithm for the task
6. Train the model — Feed training data to the algorithm
7. Evaluate the model — Test performance on validation and test data
8. Tune the model — Adjust hyperparameters to improve performance
9. Deploy the model — Publish as an endpoint for applications to consume
10. Monitor the model — Track performance and retrain when accuracy degrades