What is Machine Learning?
Machine learning is a way for computers to learn from data without being specifically programmed. It uses algorithms which can examine through data, learn from it, and then apply that knowledge to make decisions and predictions.
Andrew Ng-
It’s a science of getting computers to learn without being explicitly programmed.
In layman’s terms, machine learning is the process of having a computer learn from data without having to explicitly program it. It uses
algorithms
to figure outpatterns
indata
and then use those patterns to makedecisions
orpredictions
.
Machine Learning Algorithm Types
- Supervised Learning (Used Mostly in real world applications)
- Unsupervised Learning
- Recommender Systems
- Reinforcement Learning
- Anomaly Detection
- Dimensionality Reduction
What is Supervised Learning?
In supervised learning, we are given a data set and already know what our correct output
should look like, having the idea that there is a relationship
between the input
and the output
.
flowchart LR
X[X Input]-->|Mapping|Y[Y Output]
Supervised learning, refers to algorithms that learn x to y or input to output mappings.
Applications of Supervised Learning
Input (X) | Output (Y) | Application |
---|---|---|
Spam | Spam Filtering | |
Audio | Text Transcripts | Speech Recognition (Google Assistant) |
English | Spanish | Machine Translation (Google Translate) |
Ad, User info | Click? (0/1) | Online Advertising (Google Ads) |
Image, Radar Info | Position of the other car | Self-Driving car (Tesla) |
Image of the Phone | defect? (0/1) | Visual Inspection (Computer Vision) |
Categorization of Supervised Learning
- Regression
- Classification
Figure 2 : Discrete and Continuous Variable
Regression
In a regression problem, we are trying to predict results within a continuous
output, meaning that we are trying to map input
variables to some continuous
function.
Regression model predicts
numbers
. i.e.price
of the house by usingsize
orage
of the person by usingheight
. It predictsinfinetely
many possibleoutputs
.
Classification
In a classification problem, we are instead trying to predict results in a discrete
output. In other words, we are trying to map input variables into discrete categories.
Example 1: Given data about the size of houses on the real estate market, try to predict their price. Price
as a function of size
is a continuous
output, so this is a regression
problem. We could turn this example into a classification
problem by instead making our output about whether the house sells
for more
or less
than the asking price
. Here we are classifying the houses
based on price
into two discrete
categories.
Classification model predicts
Categories
. i.e.cat
ordog
. It predictsSmall
number of possibleoutputs
.
Example 2:
(a) Regression - Given a picture of a person, we have to predict their age based on the given picture.
(b) Classification - Given a patient with a tumor, we have to predict whether the tumor is malignant or benign.
What is Unsupervised Learning?
In Unsupervised learning, data only comes with Input feature X
, but not Output label y
. Unsupervised learning allows us to approach problems with little or no idea what our results should look like. We can derive structure
from data
where we don’t necessarily know the effect of the variables. eg. Google News, Grouping Genes, Grouping customers based on market segments.
Example: Take a collection of 1,000,000 different genes, and find a way to automatically group these genes into groups
that are somehow similar
or related by different variables, such as lifespan, location, roles, and so on.
‘Group similar data points together’. We can derive this structure by clustering the data based on relationships among the variables in the data.
Summary
Machine learning is a science of getting computers to learn without being explicitly programmed. Supervised learning, refers to algorithms that learn x to y
or input to output
mappings. In a regression
problem, we are trying to predict results within a continuous
output. In a classification
problem, we are trying to predict results in a discrete
output. Unsupervised Learning, We can derive the structure
by clustering the data
.
References:
- Machine learning
- Midjourney AI Image Generator Platform
- OpenAI Dall.E 2 AI Image Generator Platform
- Continuous Variable - Types, Examples and Discrete variables
- Real, Irrational, Imaginary World of Mathematics – Mathigon