Machine learning is a subfield of Artificial Intelligence (AI) and defines, that machines can learn by experience and acquire skills without human involvement. It has the ability to automatically learn and improve from experience without being explicitly programmed. Within machine learning, there are several techniques you can use to analyse your data. How does machine learning work? In addition, what is the difference between artificial intelligence (Al), machine learning and deep learning?
Author: Zakaria Ibn Mohamed
Four Machine learning methods
– Technique from statistics that is used to predict values of a desired target quantity when the target quantity is continuous.
– Regression algorithms are used to make predictions about numbers e.g. like predicting prices of a house given the features of house like size, price etc. is one of the common examples of Regression.
– Simplest method is linear regression (mathematical equation) of the line: y = m*x + b to model a data set.
– Goal of regression is to be able to predict the optimal price and make predictions about customers lifetime value (perhaps spotting potentially valuable customers before they have declared themselves by the volume of their purchases).
– Classification method: predict or explain a class value
– Classification method could help to assess whether a given image contains an apple car or a pear or predict whether or not an online customer will buy a product
– Example: spam detection in email can be identified as a classification problem (there are only two classes as spam and not spam)
– Another example would be for being admitted to college or not
– For finding this out à logistic regression is the easiest classification model which estimates the probability that e.g. the student will get admitted to college due to two exam scores
– The estimate is a probability, the output is a number between 0 and 1, where 1 represents complete certainty.
This allows us to group a chart into a certain class such as pass/fail, win/lose etc.
3) Clustering (Cluster analysis)
– Clustering is a Machine Learning technique that involves the grouping of data points and divides the population/data points into a number of groups.
– Objects in the same group are called a cluster which are are more similar to each other than to those in other groups (clusters).
– Goal is to group or cluster observations that have similar characteristics
– In general, it is a group of data on the basis of similarity and dissimilarity between them.
Example: Classifying dataset (review) of movies
“The machine-learning model will be able to infere that there are two different classes without knowing anything else from the data.” (Victor Roman, 2019)
4) Anomaly Detection
– Technique which is used to identify data points that do not conform to the expected pattern of a given group.
– Applicable in domains such as fraud detection, system health monitoring (spotting diseases like tumour, cancer cells).
What you’re looking for is something unusual, something different, something that stands out in some way.
First, Artificial Intelligence (Al) or machine intelligence is the simulation of human intelligence, which is demonstrated by machines/computer systems. Machines imitate the human mind, like learning and solving problems.
Just like Machine Learning, Deep Learning (known as deep structured learning or hierarchical learning) is a subfield of Artificial Intelligence (AI). Deep learning is a branch of machine learning, where algorithms learn independently from excessive amounts of information. Similarly to people, these algorithms get smarter with experience by gathering and processing more and more data. “Deep neural networks can solve the most challenging problems, but require abundant computing power and massive amounts of data” (Martin Heller, Contributing Editor, InfoWorld 2019). Alexa (Amazon Echo, a virtual assistant developed by Amazon) is capable of voice interaction, setting alarms, playing music and more — only with the help of Deep Learning. Even cars can drive itself due to deep learning.
Note: To put it in a nutshell, Artificial Intelligence (AI) is when a machine is trained to do one particular task, Machine Learning (ML) for machines/computers to learn without being programmed and Deep Learning (DL) for using deep artificial neutral networks to understand large amounts of data. It is like letting the computer create its own structure and that it figures the data out for itself.