Machine Learning Fundamentals

Explore the core concepts and applications of machine learning technology.

Question 1 of 10

Question 1: Supervised vs Unsupervised

What is the difference between supervised and unsupervised learning?

ASupervised learning requires labeled data while unsupervised learning doesn't

BSupervised learning is faster while unsupervised learning is more accurate

CSupervised learning works with images while unsupervised works with text

DSupervised learning is for prediction while unsupervised is only for classification

Question 2: Overfitting Definition

What is overfitting in machine learning?

AWhen a model performs well on training data but poorly on new data

BWhen a model is too complex to run efficiently

CWhen training data is insufficient

DWhen a model is too simple to capture patterns

Question 3: Classification Algorithms

Which algorithm is NOT typically used for classification problems?

ALogistic Regression

BRandom Forest

CK-Means Clustering

DSupport Vector Machines

Question 4: Regularization Purpose

What is regularization used for in machine learning?

ATo speed up the training process

BTo prevent overfitting

CTo visualize complex data

DTo convert categorical data to numerical data

Question 5: Regression Metrics

Which of these is a common evaluation metric for regression problems?

APrecision

BRecall

CF1-Score

DMean Squared Error (MSE)

Question 6: Feature Engineering

What is “”feature engineering”” in machine learning?

AThe process of creating new algorithms

BThe process of selecting and transforming variables for model training

CThe process of building user interfaces for ML applications

DThe process of documenting the model behavior

Question 7: Deep Learning Distinction

What distinguishes deep learning from traditional machine learning?

ADeep learning uses multiple layers of neural networks

BDeep learning doesn't require training data

CDeep learning is always more accurate

DDeep learning only works with numerical data

Question 8: Decision Tree

What is a decision tree in machine learning?

AA flowchart-like structure that makes decisions based on feature values

BA visualization technique for data exploration

CA method for organizing machine learning algorithms

DA technique for data cleaning

Question 9: Data Splitting

What is the purpose of the training-validation-test split in machine learning?

ATo organize data by date

BTo separate data for different purposes in the model development process

CTo distribute workload across multiple processors

DTo classify data by complexity

Question 10: Transfer Learning

What is transfer learning?

ATraining a model on one task and using it as a starting point for a different task

BTransferring a model from one programming language to another

CMoving trained models between different servers

DSharing models between competitors