What is Machine Learning?
Let’s start with the hot topic in the market which is “Machine Learning”.
If you ask 10 people, you may get 10 different definitions of what machine learning is. But, the simple definition of machine learning is;
“Practice of using algorithms to parse data, learn from it, and then make a determination or prediction about something in the world.” -By NVIDA
There are three types of machine learning.
- Supervised Machine Learning
- Unsupervised Machine Learning
- Reinforcement Learning
- Supervised Machine Learning:
This is where you have an explicit label and it’s a task of inferring a function from that labeled training data to make a prediction on unseen data. In other words, supervised learning is the machine learning task of learning a function that maps an input to an output based on example input-output pairs. It infers a function from labeled training data consisting of a set of training examples.
An example of supervised learning would be a spam filter. The machine learning model would predict whether any given email is spam based on known information about the email, so email content, maybe sender, recipient, what time it was sent, maybe the structure of the email, things like that.
2. Unsupervised Machine Learning:
Unsupervised learning is where you don’t have any explicit labels and it’s about deriving structure from the data where you don’t know the effect of any of the features. In other words, it looks for previously undetected patterns in a data set with no pre-existing labels and with a minimum of human supervision. Also, you don’t have anything you’re trying to predict, necessarily. You’re just trying to back out some sort of information or structure using the variables that you are given.
An example of unsupervised learning would be grouping together similar emails into distinct folders based on the content. There is no right or wrong answer necessarily, but if a model can identify that these 15 emails are all in regards to a vacation to Italy and those other ones are about planning a certain family gathering, and then it can group those together into their own little bundles.
3. Reinforcement Learning:
Reinforcement learning is the training of machine learning models to make a sequence of decisions. The agent learns to achieve a goal in an uncertain, potentially complex environment. In other words, Reinforcement learning (RL) is an area of machine learning concerned with how software agents ought to take actions in an environment in order to maximize some portion of cumulative reward.
Reinforcement Learning has four essential elements:
- Agent. The program you train, with the aim of doing a job you specify.
- Environment. The world, real or virtual, in which the agent performs actions.
- Action. A move made by the agent, which causes a status change in the environment.
- Rewards. The evaluation of an action, which can be positive or negative.
If you want to learn more about Data Science and machine learning, so stay tuned!
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Pooja Umathe
