5.2 - Machine Learning Basics

Question 1

Why is machine learning important in analytics?

Answer 1

ML enables systems to learn from data and make predictions or decisions without being explicitly programmed.

Question 2

What is the core idea of machine learning?

Answer 2

Using data to train models that can identify patterns, make predictions, or classify information.

Question 3

Name two types of machine learning.

Answer 3

Supervised learning, unsupervised learning

Question 4

In ______ learning, the model is trained on labeled data with known outcomes.

Answer 4

Supervised

Question 5

In ______ learning, the model identifies patterns in data without labeled outcomes.

Answer 5

Unsupervised

Question 6

Give an example of supervised learning.

Answer 6

Examples: Predicting sales, classifying emails as spam/not spam.

Question 7

Give an example of unsupervised learning.

Answer 7

Examples: Customer segmentation, anomaly detection.

Question 8

Which of these is NOT a common ML task?
A) Classification
B) Regression
C) Clustering
D) Manually counting rows

Answer 8

D) Manually counting rows

Question 9

What is a feature in machine learning?

Answer 9

A measurable variable used as input to train a model.

Question 10

What is a model in machine learning?

Answer 10

A mathematical or algorithmic representation that maps input data (features) to predictions or outputs.

Question 11

Name one common tool or platform for machine learning.

Answer 11

Examples: Python (scikit-learn, TensorFlow, PyTorch), R, RapidMiner.

Question 12

What is one limitation of machine learning for beginners?

Answer 12

Requires quality data, careful feature selection, understanding of outputs, and potential bias in predictions.