Introduction to machine learning

__________________ algorithms enable the computers to learn from data, and even improve themselves, without being explicitly programmed.

• Artificial intelligence

• Deep Learning

• Machine Learning

• Traditionnal Learning

_______________________ is a category of an algorithm that allows software applications to become more accurate in predicting outcomes without being explicitly programmed.

• Machine Learning

• Artificial intelligence

• Traditional Learning

• Deep Learning

What device below is not an example of Machine Learning?

• google assistant

• speech to text

• google search

• None of the above

 

Check/Click which does not use Machine Learning below.

• Prediction

• Face Recognition

• Image Recognition

• Feeding the newborn

What are the three types of Machine Learning? Choose three.

• supervised Learning

• Learning differentiated

• Unsupervised Learning

• Reinforcement L earning

• Technical L earning

What are the two types of Supervised Learning?

• Regression

• Progression

• classification

• binary classification

What are the two types of Unsupervised Learning?

• Loitering

• Clustering

• Association

• Dissociation

This type of Machine Learning learns by interacting with its environment. The agent receives rewards by performing correctly and penalties for performing incorrectly. The agent learns without intervention from a human by maximizing its reward and minimizing its penalty. It is a type of dynamic programming that trains algorithms using a system of reward and punishment.

• Supervised Learning

• Unsupervised Learning

• traditional Learning

• Reinforcement Learning

In this type of Machine Learning, an AI system is presented with unlabeled, uncategorized data and the system’s algorithms act on the data without prior training. The output is dependent upon the coded algorithms.

• Supervised Learning

• Unsupervised Learning

• Reinforcement Learning

• Technical Learning

In this kind of Machine Learning, an AI system is presented with data which is labeled, which means that each data tagged with the correct label.

• Unsupervised Learning

• Supervised Learning

• Reinforcement Learning

• Traditional Learning