What is Machine Learning? 

Machine learning is simply the study of tutoring a computer program or algorithm how to progressively improve upon a task that it is given. On the research-side, Machine learning can be seen as theoretical and mathematical modeling of how this process works. 

How does Machine learning work?

Machine learning is a kind of artificial intelligence (AI) that teaches the computers to think in a parallel way to how humans do: Learning and enhancing upon past experiences. Machine Learning works by exploring data and distinguishing patterns that involves minimal human intervention.

Types of Machine Learning 

Largely there are three major categories of Machine Learning: Supervised Learning, Unsupervised Learning, and Reinforcement Learning.

 

Types of Machine Learning

Supervised Learning 

Supervised learning is the most popular model for machine learning. It is the simplest to understand and the easiest to implement. Amongst all the types of machine learning, this model is often portrayed as task oriented. It is highly concentrated on a singular task, feeding more and more examples to the algorithm so that it can accurately perform on that task. 

Applications of Supervised Learning 

• Advertisement Popularity: Selecting advertisements that will perform good is considered as a supervised learning task. Many of the ads we see today are a result of browsing the internet and  are placed in a certain way because the learning algorithm could conclude that they were of reasonably popular and clickable. 

• Spam Classification: Using a modern email system, allows the user to get acquainted to a spam filter. That spam filter is a supervised learning system. 

• Face Recognition: It is very likely that your face has been used in a supervised learning algorithm that is trained to recognize your face on social media platforms like Facebook. 

Unsupervised Learning 

Unsupervised learning is quite the opposite of supervised learning. It highlights no labels. Instead, the algorithm needs to be fed a lot of data and given the tools to understand the properties of the data. henceforth, the algorithm can learn to group, cluster, and/or organize the data in a way such that a human or other intelligent algorithms can make sense of the newly organized data.

Applications of Unsupervised Learning 

• Recommender Systems: Living in the modern era it is likely that we have experienced ‘video recommendation on OTT platforms; these systems are often times placed in the unsupervised domain. 

• Buying Habits: The buying habits can be used in unsupervised learning algorithms to group customers into similar purchasing segments. This helps the companies’ to market to these grouped segments easily and can even resemble recommender systems.

• Grouping User Logs: Although it is less user facing, but still very relevant as we can use unsupervised learning to group user logs and issues. This can help businesses identify central themes to issues their customers face and fix these issues, through improving a product or designing an FAQ to handle common issues. 

Reinforcement Learning 

Reinforcement learning is relatively different when compared to the other types of machine learning; supervised and unsupervised learning. Where the relationship is easily seen in supervised and unsupervised due to the presence or absence of labels, the relationship to reinforcement learning is a bit dimmer. 

This types of machine learning is very behavior driven. It has impacts from the fields of neuroscience and psychology. For any reinforcement learning problem, an agent and an environment as well as a way to connect the two through a feedback loop are required. To connect the agent to the environment, a set of actions that it can take that affect the environment are given. To connect the environment to the agent, one has to continually issue two signals to the agent: an updated state and a reward 

Applications of Reinforcement Learning 

• Video Games: One of the most common places to look at reinforcement learning is in understanding to play games. Mario is by far the most common example to understand this type of machine learning.

• Industrial Simulation: For many robotic applications, it is useful to have the machines learn to complete their tasks without having to hardcode their processes. This can be a not expensive and safer option; it can even be less prone to failure. 

• Resource Management: Reinforcement learning is great example for steering complex environments. It can handle the need to balance certain requirements. 

Closing thought for Techies 

Understanding the basics of machine learning and artificial intelligence is a NECESSITY for anyone working in the tech domain today. Due to the popularity of AI and ML in today’s tech world, working knowledge of this technology is required to stay relevant.