Exploring AI Fundamentals

What is the primary goal of machine learning?

• To create static algorithms that do not adapt.

• To enable computers to learn from data and make predictions or decisions.

• To store data without analysis.

• To replace human intelligence entirely.

Define supervised learning and give an example.

• Predicting stock prices without historical data

• Clustering customer data into distinct groups

• An example of supervised learning is email classification, where emails are labeled as 'spam' or 'not spam'.

• Image recognition of cats and dogs

What is overfitting in machine learning?

• Overfitting occurs when a model is too simple and cannot capture the underlying patterns.

• Overfitting is when a model performs well on training data but poorly on new data due to excessive complexity.

• Overfitting happens when a model is trained on too much data, leading to confusion.

• Overfitting is when a model performs poorly on both training and new data due to lack of data.

Explain the concept of natural language processing.

• Natural Language Processing focuses solely on speech recognition and ignores written text.

• Natural Language Processing is a method for teaching machines to perform arithmetic calculations.

• Natural Language Processing (NLP) is a field of AI that enables computers to understand, interpret, and generate human language.

• Natural Language Processing is a technique used to enhance image recognition capabilities.

What is tokenization in NLP?

• Tokenization refers to the analysis of sentence structure in grammar.

• Tokenization is the method of translating text into different languages.

• Tokenization is the process of summarizing text into a single sentence.

• Tokenization is the process of dividing text into individual tokens, such as words or phrases.

Describe the role of convolutional neural networks in computer vision.

• Convolutional Neural Networks are primarily used for natural language processing tasks.

• Convolutional Neural Networks play a crucial role in computer vision by automatically extracting features from images for tasks like classification and detection.

• Convolutional Neural Networks require manual feature extraction from images.

• Convolutional Neural Networks are only effective for audio signal processing.

What is image classification?

• Image classification involves creating 3D models from images.

• Image classification is the task of identifying and categorizing objects within an image.

• Image classification is the process of enhancing image quality.

• Image classification is the technique of compressing images for storage.

Explain the difference between object detection and image segmentation.

• Image segmentation only works with grayscale images, while object detection works with color images.

• Object detection identifies objects by color, while image segmentation uses shapes.

• Object detection focuses on locating objects with bounding boxes, whereas image segmentation classifies each pixel for detailed object representation.

• Object detection requires more computational power than image segmentation.

What is the purpose of a training dataset in machine learning?

• The purpose of a training dataset in machine learning is to train the model to recognize patterns and make predictions.

• To provide a dataset for testing the model's accuracy.

• To store the final predictions of the model.

• To validate the model's performance after training.

How does sentiment analysis work in NLP?

• Sentiment analysis works by preprocessing text, tokenizing it, training a model on labeled data, and predicting sentiment for new text.

• Sentiment analysis is performed by human reviewers reading each text.

• It relies solely on the author's intent without analyzing the text.

• Sentiment analysis only counts the number of words in a text.