Generative AI Certificate Q&A: What type of machine learning to search social media for topics and classify the comments

Question

You work for a political organization that does sentiment analysis of social media networks. Politicians look to your service to see how people feel about certain difficult topics. Your organization has developed an artificial neural network that can search social media for topics and classify the comments as strongly agree, neutral, and strongly disagree.

What type of machine learning are you using?

A. unsupervised learning binary classification
B. variational auto encoding generative AI
C. reinforcement learning unsupervised clustering
D. supervised learning multiclass classification

Answer

D. supervised learning multiclass classification

Explanation 1

The correct answer is D. supervised learning multiclass classification. This is because the artificial neural network is trained with labeled data, which means that each comment has a predefined category (strongly agree, neutral, or strongly disagree).

Supervised learning is a type of machine learning where the model learns from labeled data and tries to predict the correct output for new inputs. Multiclass classification is a type of supervised learning where the model has to assign one of more than two possible classes to each input.

In this case, the model has to classify each comment into one of three possible classes. Therefore, the type of machine learning used by the organization is supervised learning multiclass classification.

Explanation 2

The answer is D. supervised learning multiclass classification.

Supervised learning is a type of machine learning where the model is trained on a dataset of labeled data. This means that each data point in the dataset has a label that tells the model what the correct output should be. In this case, the labeled data would be the comments from social media, and the label would be the sentiment of the comment (strongly agree, neutral, or strongly disagree).

The artificial neural network would then be trained on this labeled data. This means that the network would learn to associate the features of the comments (such as the words that are used, the tone of the writing, etc.) with the label (sentiment of the comment).

Once the network is trained, it can be used to classify new comments. To do this, the network would be given a new comment and would output a prediction of the sentiment of the comment.

In this case, the sentiment analysis is being done on a multiclass dataset, which means that there are more than two possible labels. In this case, there are three possible labels: strongly agree, neutral, and strongly disagree.

Here are some other examples of supervised learning multiclass classification:

Classifying images of animals. The model would be trained on a dataset of images of animals, and the label would be the type of animal (dog, cat, bird, etc.).
Classifying customer reviews. The model would be trained on a dataset of customer reviews, and the label would be the rating of the product (1 star, 2 stars, 3 stars, etc.).
Classifying medical diagnoses. The model would be trained on a dataset of medical diagnoses, and the label would be the disease (cancer, heart disease, etc.).

Supervised learning multiclass classification is a powerful tool that can be used to classify data into multiple categories. It is used in a wide variety of applications, and it is likely to become even more important in the future.

Explanation 3

The answer to your question is D. supervised learning multiclass classification. Let me explain why.

Machine learning is a branch of artificial intelligence that uses data and algorithms to enable machines to learn from data and make predictions or classifications. There are three main types of machine learning: supervised learning, unsupervised learning, and reinforcement learning.

Supervised learning is a type of machine learning where the machine learns from labeled data, meaning the data has a known output or target variable. The machine uses the data and an algorithm to learn a function that maps the input to the output. The goal of supervised learning is to make accurate predictions or classifications for new data that has not been seen before. There are two main types of supervised learning: regression and classification.
- Regression is a type of supervised learning where the output or target variable is continuous, meaning it can take any value within a range. For example, predicting the price of a house based on its features is a regression problem.
- Classification is a type of supervised learning where the output or target variable is discrete, meaning it can take only a finite number of values or categories. For example, predicting whether an email is spam or not is a classification problem.
Unsupervised learning is a type of machine learning where the machine learns from unlabeled data, meaning the data has no known output or target variable. The machine uses the data and an algorithm to discover patterns, structures, or hidden features in the data. The goal of unsupervised learning is to find useful information or insights from the data without any guidance or supervision. There are two main types of unsupervised learning: clustering and dimensionality reduction.
- Clustering is a type of unsupervised learning where the machine groups similar data points together based on some measure of similarity or distance. For example, finding customer segments based on their purchase behavior is a clustering problem.
- Dimensionality reduction is a type of unsupervised learning where the machine reduces the number of features or variables in the data while preserving as much information as possible. For example, reducing the size of an image while keeping its quality is a dimensionality reduction problem.
Reinforcement learning is a type of machine learning where the machine learns from its own actions and experiences in an environment. The machine does not have any labeled data or explicit feedback, but instead learns by trial and error. The machine receives rewards or penalties for its actions and tries to maximize its total reward over time. The goal of reinforcement learning is to find the optimal policy or strategy for the machine to act in a given situation. For example, teaching a robot to walk or play chess is a reinforcement learning problem.

Now, let’s look at your scenario. You have developed an artificial neural network that can search social media for topics and classify the comments as strongly agree, neutral, and strongly disagree. This is clearly a classification problem, since you are trying to assign each comment to one of three categories.

Moreover, this is a supervised learning problem, since you need labeled data to train your neural network, meaning you need some comments that have been already classified by human experts or annotators.

However, this is not a binary classification problem, since you have more than two categories to choose from. A binary classification problem would be if you only had two categories, such as spam or not spam. Instead, this is a multiclass classification problem, since you have multiple categories (three in this case) to choose from.

Therefore, the type of machine learning you are using in your scenario is supervised learning multiclass classification.

Explanation 4

The type of machine learning used for sentiment analysis of social media networks is supervised learning multiclass classification. This is because the artificial neural network is trained on labeled data to classify comments as strongly agree, neutral, and strongly disagree. The labeled data is used to train the model to recognize patterns in the data and make predictions on new data.

Explanation 5

The answer is D. supervised learning multiclass classification.

In supervised learning, the model is trained on a dataset of labeled data. This means that each data point in the dataset has a label that tells the model what the correct output should be. In this case, the labeled data would be the comments from social media, and the labels would be strongly agree, neutral, and strongly disagree.

Once the network is trained, it can be used to classify new comments. To do this, the network would be given a new comment and would output a prediction of the sentiment of the comment.

Here are some other examples of supervised learning:

Classifying images of cats and dogs. The model would be trained on a dataset of images of cats and dogs. Each image would be labeled as either a cat or a dog. The model would then learn to associate the features of the images (such as the shape of the ears, the length of the tail, etc.) with the label (cat or dog).
Predicting the price of a house. The model would be trained on a dataset of houses. Each house would be labeled with its price. The model would then learn to associate the features of the houses (such as the number of bedrooms, the square footage, etc.) with the label (price).
Recommending products to customers. The model would be trained on a dataset of customer purchases. Each purchase would be labeled with the products that the customer bought. The model would then learn to associate the features of the customers (such as their age, gender, interests, etc.) with the products that they are likely to buy.

Supervised learning is a powerful tool that can be used to train models to perform a variety of tasks. It is the most common type of machine learning used in practice.

Explanation 6

The type of machine learning used for sentiment analysis of social media networks is supervised learning multiclass classification. In this case, the artificial neural network is trained on a dataset of labeled comments that are classified as strongly agree, neutral, and strongly disagree. The neural network learns to classify new comments based on the patterns it has learned from the training data.

Explanation 7

The correct answer is D. Supervised learning multiclass classification.

Machine learning is a branch of AI that enables machines to learn from data and improve their performance without explicit programming. Machine learning can be divided into four main types, depending on the nature and availability of the data:

Supervised learning, where the machine learns from labeled data with human guidance. The data contains both the input and the output variables, and the machine tries to find the relationship between them. The goal is to make accurate predictions or classifications for new data.
Unsupervised learning, where the machine learns from raw data without any labels or tags. The data contains only the input variables, and the machine tries to find patterns or structures in them. The goal is to discover hidden insights or features from the data.
Semi-supervised learning, where the machine learns from a mix of labeled and unlabeled data. The data contains some input-output pairs and some input-only variables, and the machine tries to leverage both types of information. The goal is to improve the performance and accuracy of the machine with less human intervention.
Reinforcement learning, where the machine learns from its own actions and feedback. The machine interacts with an environment and receives rewards or penalties based on its actions. The goal is to find the optimal policy or strategy that maximizes the rewards.

In this scenario, you work for a political organization that does sentiment analysis of social media networks. Politicians look to your service to see how people feel about certain difficult topics. Your organization has developed an artificial neural network that can search social media for topics and classify the comments as strongly agree, neutral, and strongly disagree.

You are using supervised learning multiclass classification for your network. This means that:

You are using labeled data with human guidance to train your network. You have a dataset of social media comments labeled with their sentiment (strongly agree, neutral, or strongly disagree) based on their content and tone.
You are using an artificial neural network as your machine learning model. An artificial neural network is a type of machine learning model that mimics the structure and function of the human brain. It consists of interconnected units called neurons that process information and learn from datahttps://www.coursera.org/articles/types-of-machine-learning.
You are using multiclass classification as your machine learning task. Multiclass classification is a type of supervised learning task where the output variable has more than two categories, such as strongly agree, neutral, or strongly disagree. The goal is to assign each comment to one of these categories based on its features.

Therefore, you are using supervised learning multiclass classification for your network.

Explanation 8

The correct answer is D. supervised learning multiclass classification.

In supervised learning, the model is trained on a dataset of labeled data. This means that each data point in the dataset has a label that tells the model what the correct output should be. In this case, the labeled data would be the comments from social media, labeled as strongly agree, neutral, or strongly disagree. The label would be the sentiment of the comment.

The artificial neural network would then be trained on this labeled data. This means that the network would learn to associate the features of the comments (such as the words used, the tone of the writing, etc.) with the label (the sentiment of the comment).

Once the network is trained, it can be used to classify new comments. To do this, the network would be given a new comment and would output a prediction of the sentiment of the comment.

In this case, the model is using multiclass classification because it is classifying the comments into three different categories (strongly agree, neutral, and strongly disagree).

Here are some other examples of supervised learning multiclass classification:

Classifying images of animals. The model would be trained on a dataset of images of animals, labeled as cat, dog, bird, etc. The model would then learn to associate the features of the images (such as the shape of the ears, the length of the tail, etc.) with the label (the animal type).
Classifying text as spam or not spam. The model would be trained on a dataset of emails, labeled as spam or not spam. The model would then learn to associate the features of the emails (such as the words used, the sender’s address, etc.) with the label (spam or not spam).
Classifying customer reviews as positive or negative. The model would be trained on a dataset of customer reviews, labeled as positive or negative. The model would then learn to associate the features of the reviews (such as the words used, the rating, etc.) with the label (positive or negative).

Explanation 9

The correct answer to the question is D. supervised learning multiclass classification. Here’s a detailed explanation to elaborate on this answer:

In the described scenario, the organization has developed an artificial neural network that performs sentiment analysis on social media networks. The network searches social media for topics and classifies the comments into three categories: strongly agree, neutral, and strongly disagree. The type of machine learning used in this case is:

D. supervised learning multiclass classification: Supervised learning refers to a type of machine learning where a model is trained on labeled data with input samples and corresponding output labels or target values. In this scenario, the artificial neural network is trained to classify comments into multiple classes: strongly agree, neutral, and strongly disagree.

The training process involves providing the neural network with a labeled dataset that consists of social media comments and their corresponding sentiment labels. The neural network learns from this labeled data by extracting relevant features from the comments and mapping them to the appropriate sentiment category.

By training on this labeled dataset, the neural network learns the patterns and relationships between the input features (social media comments) and the output classes (sentiment categories). It optimizes its internal parameters to make accurate predictions and classify new, unseen comments into one of the three sentiment categories.

Multiclass classification refers to the scenario where there are more than two possible classes for classification. In this case, the sentiment analysis network is classifying comments into three distinct categories: strongly agree, neutral, and strongly disagree. It assigns each comment to one of these classes based on its sentiment.

Options A, B, and C are incorrect because they do not accurately represent the machine learning type used in the described scenario:

A. unsupervised learning binary classification: Unsupervised learning typically does not involve labeled data or explicit output labels. In this scenario, the network is trained on labeled data, making it a supervised learning problem. Additionally, the classification involves three classes (strongly agree, neutral, strongly disagree), so it is not binary classification.

B. variational auto encoding generative AI: Variational autoencoders (VAEs) are a type of generative AI model used for unsupervised learning. They are typically employed for tasks such as generative modeling or data synthesis. However, in this scenario, the focus is on sentiment analysis and classifying comments into predefined categories, not on generative modeling.

C. reinforcement learning unsupervised clustering: Reinforcement learning involves training an agent to make decisions or take actions based on feedback in the form of rewards or punishments. Unsupervised clustering refers to grouping or clustering data points based on similarity without the use of labeled data. However, in the described scenario, the sentiment analysis network is trained using labeled data, making it a supervised learning task rather than reinforcement learning or unsupervised clustering.

In summary, the organization’s sentiment analysis artificial neural network, which classifies social media comments into strongly agree, neutral, and strongly disagree categories, utilizes supervised learning multiclass classification. The network is trained on labeled data to learn patterns and accurately classify comments into the appropriate sentiment category based on their features.

Reference

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