Machine Learning Basics

What is supervised learning in machine learning?

• Supervised learning does not require labeled data

• Supervised learning involves unsupervised data

• Supervised learning focuses on predicting future outcomes based on historical data

• Supervised learning in machine learning involves training a model on labeled data to learn the mapping between input and output data.

Give an example of supervised learning.

• Training a model to predict housing prices based on labeled data

• Clustering unstructured data to find patterns

• Teaching a model to recognize images without labeled data

• Predicting stock prices using unsupervised learning

Explain unsupervised learning.

• Unsupervised learning does not involve finding patterns in data.

• Unsupervised learning requires labeled data for training.

• Unsupervised learning involves training models on unlabeled data to find patterns and relationships within the data.

• Unsupervised learning is only used for classification tasks.

Provide a real-world application of unsupervised learning.

• Predicting stock prices

• Facial recognition in security systems

• Customer segmentation in marketing

• Sentiment analysis in social media

Define machine learning.

• Machine learning is a subset of artificial intelligence that focuses on the development of algorithms and statistical models that enable computers to learn and make predictions or decisions without being explicitly programmed.

• Machine learning is a type of coffee maker

• Machine learning is a form of interpretive dance

• Machine learning is a new type of sandwich

How does supervised learning differ from unsupervised learning?

• Supervised learning and unsupervised learning are the same.

• Supervised learning uses unlabeled data for training, while unsupervised learning uses labeled data.

• Supervised learning uses labeled data for training, while unsupervised learning uses unlabeled data.

• Supervised learning does not require a model, while unsupervised learning does.

What are the key components of a machine learning model?

• features, labels, training data, algorithm, model parameters, evaluation metrics

• weights

• hyperparameters

• validation data

How can machine learning algorithms be categorized?

• Based on learning style and functionality

• Based on color, Based on size, Based on smell

What is the role of data in machine learning?

• Data has no impact on the performance of machine learning models.

• Machine learning models can be developed without the need for data.

• Data is used to train machine learning models and is essential for their development and performance.

• Data is only used for visualization purposes in machine learning.

Why is feature engineering important in machine learning?

• Feature engineering only adds complexity to models

• Feature engineering is only useful for small datasets

• Feature engineering is not important in machine learning

• Feature engineering helps in creating new features from existing data, improving model performance.