Machine Learning 101:Introduction
While Machine Learning has been around since the 50s starting with Arthur Samuel’s famous checkers game, it has recently gained traction because of the large number of libraries and the ease of using those libraries, more and more businesses have been using data to generate valuable insights and everyone seems to be talking about Machine Learning, but what is machine learning? Machine Learning can be defined as
“A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P, if its performance at tasks in T, as measured by P, improves with experience E.”- Tom Mitchell
Machine Learning is broadly classified into 3 types, namely
Supervised Learning
In Supervised Learning, the agent already knows what the right answer looks like and it establishes the relation between the input data and output which helps the agent make predictions. Supervised Learning has 2 types: Regression where the data predicted here has a continuous output and Classification where the data predicted has a discrete output, if you had to predict whether or not you can buy a house that would be classification, if you had to predict how much a house would cost you that would be regression.
Unsupervised Learning
In Unsupervised Learning, the agent doesn’t know what the right answer looks like, it identifies the relationship between data points and helps give the user several hidden insights from the data. Unsupervised Learning is further classified into Clustering where data is grouped together into clusters based on similarities and Association where the agent determines which items in the dataset are more likely to occur together if the agent groups customers together based on consumption/spending habits that would be clustering and if the agent identifies which items are more likely to be bought together eg. peanut butter & jelly that’s association
Reinforcement Learning
Reinforcement Learning is nothing like the other 2 types, supervised learning made use of labeled data to make predictions, unsupervised learning helped capture the relations between data, reinforcement learning makes use of rewards and punishments, the goal of this method is to maximize the rewards, think of it like training a dog when it does something right you say good dog and toss him a treat when it does something wrong you say bad dog!
If you liked this article please leave an upvote, I will be writing about regression next week
