Basics of Machine Learning

What is machine learning?

• Machine learning is a method of data analysis that automates analytical model building.

• Machine learning is a programming language for software development.

• Machine learning is a type of hardware used for data storage.

• Machine learning is a process of manual data entry and analysis.

Name one common application of machine learning.

• Data cleaning

• Recommendation systems

• Image formation

• data processing

What is the difference between supervised and unsupervised learning?

• Supervised learning is used for clustering, while unsupervised learning is used for classification.

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

• Supervised learning requires no data for training, while unsupervised learning requires labeled data.

• Supervised learning is faster than unsupervised learning regardless of data size.

What is a dataset in the context of machine learning?

• A dataset is a structured collection of data used for training and testing machine learning models.

• A dataset is a random collection of data points without any structure.

• A dataset is a type of algorithm used in machine learning.

• A dataset is a single data point used for making predictions.

What is a feature in machine learning?

• A feature is the output of a machine learning model.

• A feature is a type of machine learning algorithm.

• A feature is an individual measurable property or characteristic used as input for a machine learning model.

• A feature is a dataset used for training a model.

Explain the term 'training' in machine learning.

• Training involves only testing the model's performance.

• Training is the process of collecting data for analysis.

• Training is the final step before deploying a model.

• Training in machine learning is the process of teaching a model to learn from data by adjusting its parameters.

What role does a model play in machine learning?

• A model in machine learning is used to make predictions or decisions based on input data.

• A model is used to store data permanently.

• A model helps in data cleaning and preprocessing.

• A model is primarily for visualizing data trends.

Name a popular programming language used for machine learning.

• Python

• Java

• SQL

• HTML

Which of the following is not a type of Machine learning?

• Supervised learning

• Unsupervised learning

• Informed learning

• Reinforcement learning

In which type of Machine Learning does the model learn from labeled data?

• Unsupervised Learning

• Reinforcement Learning

• Supervised Learning

• Semi-supervised Learning