Supervised and Unsupervised Learning

Question 1

What is supervised learning?

Answer 1

Algos that are designed to learn by example and trained on well-labeled data and consists of an input object (typically a vector) and a desired output (supervisory signal)

Question 2

What is the objective of leveraging a supervised learning model?

Answer 2

Predict the correct label for unseen data

Question 3

Explain at a high level how a supervised learning model works?

Answer 3

It takes an input object which is typically a vector and a desired output (supervisory signal) and during the training phase, it searches for patterns that correlate with the desired output. After training is complete, it takes in unseen inputs and determines which label to classify it to

Question 4

Supervised learning can be categorized into which subcategories?

Answer 4

Classification and Regression

Question 5

How do classification algorithms work?

Answer 5

Assigns input values to classes or categories and an example would be determining if an email is spam or not spam

Question 6

What are some of the popular classification algorithms?

Answer 6

linear classifiers, support vector machines, decision trees, k-nearest neighbors, and random forest.

Question 7

What is a regression algorithm

Answer 7

predict continuous numbers, such as sales, income, and test scores and the goal is to find the relationship between dependent and independent variables

Question 8

Examples of common regression algos?

Answer 8

Linear regression, lasso regression, and multivariate regression

Question 9

What is unsupervised learning?

Answer 9

Focuses on datas features and does not use labeled data

Question 10

Difference between supervised and unsupervised learning?

Answer 10

supervised learning uses input and outputs aka labels to train the model while unsupervised learning focuses on features

Question 11

What is the goal of unsupervised learning?

Answer 11

Purpose is to analyze data and find important features in that data

Question 12

What can unsupervised learning do that supervised cannot?

Answer 12

Find subgroups or hidden patterns within a dataset that a human observer might not pick up on

Question 13

What are the two subcategories of unsupervised learning?

Answer 13

Clustering and Association

Question 14

What is clustering?

Answer 14

process of grouping the given data into different clusters or groups

Question 15

What are the components that go into clustering?

Answer 15

Classes will contain data points that are similar as possible to each other and as dissimilar as possible to data points in other clusters

Question 16

What is the objective of clustering?

Answer 16

Helps to find underlying patterns within the data that may not be noticeable through a human observer

Question 17

What is partitional clustering?

Answer 17

refers to a set of clustering algos where each data point in a data set can belong to only one cluster

Question 18

What is hierarchical clustering?

Answer 18

finds clusters by a system of hierarchies; every data point can belong to multiple clusters.

Question 19

What is association?

Answer 19

attempts to find relationships between different entities. market basket analysis is a good example of how someone that usually buys burgers and fries will usually buy a drink with those items

Question 20

What are some use-cases for using unsupervised learning?

Answer 20

Airbnb - customer queries their requirements and airbnb learns those patterns and recommends stays and experiences which fall under the same group of cluster