The quiz is about MCQs Machine Learning Questions. Test your Machine Learning knowledge with these MCQs! Covering structured vs. unstructured data, types of machine learning models (supervised learning, unsupervised learning, and reinforcement learning), AI applications, and popular algorithms like decision trees, neural networks, and SVM. Perfect for interviews & exams! Let us start with the MCQs Machine Learning Quiz now.
Online MCQs Machine Learning Quiz with Answers
Online MCQs Machine Learning Quiz
- What is the primary purpose of an Application Programming Interface (API)?
- Which of the following are machine learning models?
- In a REST API architecture, what does the client typically receive from the web service after sending a request?
- What term is used to describe a structured collection of data?
- Which type of machine learning model is primarily used to predict a numeric value?
- Which R library is used for machine learning?
- What tool is used to edit front-end languages like HTML, JavaScript, and CSS in the context of exploring machine learning models?
- PyTorch is what type of Python library?
- Which of the following is an example of structured data?
- Which of these is NOT one of the main skills embodied by data scientists?
- Which of the following statements do you agree with?
- Machine learning is an “iterative” process, meaning that an AI team often has to try many ideas before coming up with something good enough, rather than have the first thing they try work.
- Suppose you want to use Machine Learning to help your sales team with automatic lead sorting. That is, input $A$ (a sales prospect) and output $B$ (whether your sales team should prioritize them). The 3 steps of the workflow, in scrambled order, are: (i) Deploy a trained model and get data back from users, (ii) Collect data with both A and B (iii) Train a machine learning system to input A and output B What is the correct ordering of these steps?
- Machine Learning programs can help:
- Unless you have a huge dataset (“Big Data”), it is generally not worth attempting machine learning or data science projects on your problem.
- Suppose you are building a trigger word detection system and want to hire someone to build a system to map from Input $A$ (audio clip) to Output $B$ (whether the trigger word was said) using existing AI technology. Out of the list below, which of the following hires would be most suitable for writing this software?
- What is the key difference between supervised and unsupervised models?
- Select the model you would try first if you had labeled non-continuous value data.
- What is Machine learning?
- Which of the following areas does not belong to machine learning?
Table of Contents
Take Quiz on Deep Learning
Introduction to Machine Learning
Machine Learning (ML) is a transformative branch of Artificial Intelligence (AI) that enables systems to learn from data, identify patterns, and make decisions with minimal human intervention. It powers applications like recommendation systems, fraud detection, and self-driving cars.
Key Concepts in Machine Learning
- Supervised Learning – Algorithms learn from labeled data (e.g., classification, regression, spam detection, sales forecasting).
- Unsupervised Learning – Finds hidden patterns in unlabeled data (e.g., clustering, dimensionality reduction, customer segmentation).
- Reinforcement Learning – Trains models via rewards/punishments (e.g., game-playing AI, self-driving cars).
- Deep Learning – Uses neural networks for complex tasks (e.g., image recognition).
What is Machine Learning?
Machine Learning algorithms analyze large datasets to identify patterns, make predictions, and automate decision-making. Unlike traditional programming, machine learning systems adapt and improve over time, making them essential for data-driven businesses.
Why Learn Machine Learning?
- High demand for ML engineers in tech, healthcare, finance, and e-commerce.
- Automates repetitive tasks, improving efficiency and accuracy.
- Enhances predictive analytics, helping businesses make smarter decisions.
Top Machine Learning Applications
- Natural Language Processing (NLP) – Powers chatbots like ChatGPT.
- Computer Vision – Used in facial recognition and medical imaging.
- Recommendation Systems – Drives platforms like Amazon and YouTube.