Machine Learning Algorithms Quiz

True (A)

To group similar data points together based on patterns or trends identified within the dataset.

Signup and view all the answers

<p>Labeled dataset (C)</p> Signup and view all the answers

<p>False (B)</p> Signup and view all the answers

<p>Labels categorize data.</p> Signup and view all the answers

<p>unlabeled</p> Signup and view all the answers

<p>Classification = Works on labeled data Regression = Predicts continuous outcomes Unsupervised Learning = Works on unlabeled data Patterns = Identifiable structures within data</p> Signup and view all the answers

<p>Predicting next salary (A)</p> Signup and view all the answers

<p>Data Mining (B)</p> Signup and view all the answers

<p>True (A)</p> Signup and view all the answers

<p>True (A)</p> Signup and view all the answers

<p>Who, what, where, why</p> Signup and view all the answers

<p>Identify patterns in data.</p> Signup and view all the answers

<p>Rule</p> Signup and view all the answers

<p>It follows predefined rules set by the developer. (D)</p> Signup and view all the answers

<p>False (B)</p> Signup and view all the answers

<p>It does not adapt to changes in the training dataset.</p> Signup and view all the answers

<p>Rule Based = Follows instructions set by the developer Learning Based = Adapts and modifies itself based on new data</p> Signup and view all the answers

<p>Social Media Posts (A)</p> Signup and view all the answers

<p>False (B)</p> Signup and view all the answers

<p>Acquisition, Precision, Recall, F1 Score</p> Signup and view all the answers

<p>Data Acquisition</p> Signup and view all the answers

<p>Problem Scoping = Setting the goal for the AI project Data Exploration = Visualizing data patterns Data Modelling = Selecting and testing models Evaluation = Assessing model efficiency</p> Signup and view all the answers

<p>Data Exploration (C)</p> Signup and view all the answers

<p>True (A)</p> Signup and view all the answers

<p>To decide upon and test suitable models for the project</p> Signup and view all the answers

<p>People who face the problem and would benefit from a solution. (A)</p> Signup and view all the answers

<p>False (B)</p> Signup and view all the answers

<p>It is a machine learning algorithm.</p> Signup and view all the answers

<p>developer</p> Signup and view all the answers

<p>Rule-Based Approach = Patterns defined by the developer Learning-Based Approach = Automatically learns from data Artificial Neural Network = Modeled on the human brain Feature Extraction = Automatic feature discovery</p> Signup and view all the answers

<p>The situation and locations where the problem arises. (D)</p> Signup and view all the answers

<p>False (B)</p> Signup and view all the answers

<p>Rule-based uses predefined rules while learning-based automatically learns from data.</p> Signup and view all the answers

<p>Allowing the machine to learn patterns from unlabeled data (B)</p> Signup and view all the answers

<p>False (B)</p> Signup and view all the answers

<p>To perform computations that lead to the output.</p> Signup and view all the answers

<p>reliability</p> Signup and view all the answers

<p>The first layer that accepts inputs from the programmer (A)</p> Signup and view all the answers

<p>Key points of a problem.</p> Signup and view all the answers

<p>False (B)</p> Signup and view all the answers

<p>Reinforcement</p> Signup and view all the answers

<p>True (A)</p> Signup and view all the answers

<p>Developing new algorithms for data analysis. (B)</p> Signup and view all the answers

<p>Clustering = Grouping similar data points together Dimensionality Reduction = Simplifying high-dimensional data Reinforcement Learning = Learning through trial and error with rewards and punishments</p> Signup and view all the answers

<p>Clustering algorithms are used to group similar data points together based on patterns or trends.</p> Signup and view all the answers

<p>Personal opinions and beliefs (D)</p> Signup and view all the answers

<p>False (B)</p> Signup and view all the answers

<p>information</p> Signup and view all the answers

A machine learning model using labeled datasets for training.

Information used as a tag for data in supervised learning.

A supervised learning algorithm that categorizes data into discrete classes.

A supervised learning algorithm predicting continuous outcomes based on input data.

A machine learning model that works with unlabeled data to find patterns.

Identified relationships or trends from unlabeled data by the machine.

Data that can take any value within a range, used in regression.

Data that consists of distinct or separate values, used in classification.

The stages include Problem Scoping, Data Acquisition, Data Exploration, Modeling, and Evaluation.

Global Goals adopted to end poverty and protect the planet, ensuring peace and prosperity.

Who, What, Where, Why - key questions in problem scoping.

Data used to train an AI model to learn patterns.

Data used to evaluate the performance of the trained AI model.

Approach where rules are defined by a developer; static and does not adapt.

Approach where the AI learns from data and adapts over time.

The ability of a model to adjust to new input data.

An unsupervised learning algorithm that groups unknown data by identifying patterns and trends.

A technique used to simplify complex data by reducing its number of dimensions for better visualization.

A type of machine learning that enables agents to learn from experiences via rewards and punishments.

The stage of analyzing data to extract useful information and verify its quality.

Learning that occurs in an environment where an agent interacts and receives feedback.

A method of learning involving repeated attempts and adjustments based on outcomes.

The process of ensuring that collected data meets specified requirements and is error-free.

Authentic places from which trustworthy and valid datasets can be collected.

Training an AI model requires data to predict outputs effectively.

The process of gathering data from various sources for an AI project.

Selecting and testing models to achieve the goals established in the project.

Testing the efficiency of the AI model using metrics like precision and recall.

Defining the AI project's goals and understanding the parameters involved.

A framework to define who is affected, what the problem is, where it occurs, and when it happens.

Four key metrics used to evaluate an AI model: Acquisition, Precision, Recall, and F1 Score.

Individuals affected by a problem and benefit from its solution.

Characteristics defining what the problem is and its evidence.

The situation or location where the problem occurs.

Advantages gained by stakeholders from an implemented solution.

Characteristics of ANNs, mimicking human brain functions and extracting patterns automatically.

Training a model with labeled images (like apples and bananas) to recognize them.

An AI model learns patterns from unlabeled data without developer-defined relationships.

A model that mimics the human brain, consisting of multiple layers of connected perceptrons.

The layer in an ANN that receives all input data from the programmer.

Layers between the input and output in an ANN where computations are performed.

The final layer in an ANN that delivers the output after transformations.

A summary tool that captures key points of a problem for future reference.

The process of testing an AI model's reliability using a test dataset to compare outputs.

Evaluating the efficiency and effectiveness of an AI model against actual answers.