Machine Learning Basics

What is the main difference between supervised and unsupervised learning?

• Supervised learning is faster than unsupervised learning.

• Supervised learning requires labeled data, while unsupervised learning works with unlabeled data.

• Supervised learning uses neural networks, while unsupervised learning uses decision trees.

• Supervised learning does not require a training phase, unlike unsupervised learning.

Explain the concept of reinforcement learning.

• Reinforcement learning is a type of supervised learning

• Reinforcement learning is a type of machine learning where an agent learns to make decisions by interacting with an environment.

• Reinforcement learning does not involve decision-making

• Reinforcement learning involves only unsupervised learning techniques

Give an example of a classification problem in machine learning.

• Identifying the color of an image

• Predicting whether an email is spam or not spam based on its content.

• Predicting the weather forecast

• Determining the stock market trends

What is regression in the context of machine learning?

• Regression is used for classification tasks in machine learning.

• Regression in machine learning predicts continuous values based on input features.

• Regression involves predicting discrete values in machine learning.

• Regression is only applicable to supervised learning.

Describe how clustering is used in unsupervised learning.

• Clustering is used in unsupervised learning to identify patterns and structures within data by grouping similar data points together.

• Clustering helps in predicting future outcomes based on historical data.

• Clustering is used to classify data into labeled categories.

• Clustering is primarily used in supervised learning.

What is a data set in the context of machine learning?

• A data set is a single data point used for testing a machine learning model

• A data set is a group of algorithms used to analyze data in machine learning

• A data set is a software tool used to visualize machine learning models

• A data set in the context of machine learning is a collection of data points used to train a machine learning model.

Define machine learning and its significance.

• Machine learning is the development of algorithms to learn from and make predictions or decisions based on data.

• Machine learning is the process of fixing machines when they break down.

• Machine learning is the art of designing physical machines.

• Machine learning is the study of mechanical systems and their applications.

What are some common data processing techniques used in machine learning?

• Model training

• Data normalization

• Data cleaning, Feature scaling, Feature engineering, Dimensionality reduction, Data augmentation

• Hyperparameter tuning

List and explain the different stages of a typical machine learning process.

• Data Collection, Data Preparation, Model Selection, Training, Evaluation, Hyperparameter Tuning, Deployment

• Preprocessing, Testing, Backpropagation, Regularization, Prediction

• Algorithm Selection, Data Visualization, Loss Function Optimization, Cross-Validation, Inference

• Data Analysis, Model Deployment, Feature Engineering, Validation, Interpretation

How is accuracy measured in classification tasks?

• By dividing the number of false positives by the total number of instances

• By summing the values of the predicted classes

• By calculating the ratio of correctly predicted instances to the total number of instances in the dataset.

• By counting the number of features in the dataset