Machine Learning Concepts and Applications

Machine learning enables computers to perform tasks without explicit programming by using data to improve outcomes. Coined by Arthur Samuel in 1959, it involves classification and prediction tasks. The process includes data acquisition, cleaning, feature engineering, and model training/testing. Algorithms range from simple regressions to complex neural networks. Error functions guide improvements, with Python, R, and Julia as popular languages. The final model can be deployed on devices or the cloud.

Who coined the term 'machine learning'?

• Alan Turing

• Arthur Samuel

• Marvin Minsky

• John McCarthy

What are the two fundamental tasks of machine learning?

• Classification and Clustering

• Regression and Clustering

• Prediction and Clustering

• Classification and Prediction

What is the first step in the machine learning process?

• Training the model

• Choosing an algorithm

• Deploying the model

• Acquiring and cleaning data

What is feature engineering?

• Choosing the right algorithm

• Transforming raw data into features

• Testing the model

• Cleaning the data

What is the purpose of separating data into training and testing sets?

• To enhance feature engineering

• To reduce data size

• To validate the model's accuracy

• To improve data quality

Which algorithm is useful for image data?

• Linear Regression

• Logistic Regression

• Decision Tree

• Convolutional Neural Network

What error function might be used for a classification problem?

• Mean Squared Error

• Root Mean Squared Error

• Mean Absolute Error

• Accuracy

Which programming language is most popular among data scientists?

• Java

• JavaScript

• Python

• C++

What is the final output of the machine learning process?

• A list of features

• A set of algorithms

• A trained model

• A cleaned dataset

Where can a machine learning model be deployed?

• On a local server

• In the cloud

• On an actual device

• All of the above