By Arpit GautamMachine learning is a trailblazing technological solution that continues to help large & small enterprises to drive their business growth. So what is machine learning all about? Let’s dive right into it!
What is Machine Learning?
As the name rightly implies, you enable a machine preferably a computer system to learn about a particular process by giving it sufficient data. The machine then makes accurate predictions or decisions based on the data it has learned from.
Some real-world examples of this process include accurately recognizing a face from a picture, differentiating between a set of fruits, sensors & imaging technology in self-driving cars that detect people crossing a road so on & so forth. These systems are real & are being used by a variety of industries for various purposes.
Traditional software cannot make such decisions as it needs human input to generate the desired output. But in the case of machine-learning, the model is trained with a large amount of reliable data to empower the systems for making accurate predictions. Therefore, Data is a key component of the entire machine learning process. Reliable data will make machine learning robust & fool-proof!
Is AI & Machine Learning Different?
Machine learning is a subset of AI. These terms are often used interchangeably but they are not the same though.
Artificial Intelligence is an extensive concept wherein machines are empowered to carry out tasks just like humans do. But in the case of the machine learning, the systems are given access to troves of data so that
What are the types of Machine Learning?
- Supervised Machine Learning
In this process, a system is taught with the help of examples. Usually, such machines are fed troves of labelled data. As the machine is continually exposed to such data (fruits like banana or apple), it begins to effectively recognize it. After thorough training, these systems are used for real-world application.
One caveat of this method is the requirement of huge amounts of labelled data. Sometimes you might even have to expose a particular machine learning system to thousands if not millions of examples. This is done to help the system carry out a specific task skillfully.
Some of the examples of huge datasets are:
- Google – It’s Open Images Dataset boasts of more than 9 million images.
- Youtube – You can find around 8 million Youtube videos
- ImageNet – It comprises almost 14 million images that are categorized into various types.
- Facebook – It has a large compilation of more than 3.5 billion images. This includes Instagram images as well which are labelled using hashtags.
- Unsupervised Machine Learning
This process requires minimum human intervention as the models are taught to learn identifying particular patterns in data. These machine learning systems often try to recognize similarities in datasets that are categorized into different types.
So, to simply put, these machine learning algorithms are tasked with identifying data that are similar in nature. These are then grouped together. Another objective of these algorithms is to find any anomalies in the given dataset.
- Semi-Supervised Machine Learning
The name aptly implies the combination of supervised & unsupervised learning techniques.
Understanding this process is pretty simple, you need:
- A small amount of labelled data
- Huge amounts of unlabeled data
Both of these are used to train computing systems. The labelled data is leveraged to train a machine learning model. Once it is trained, the model itself is used to label the unlabeled data. This innovative process is called pseudo-labelling. After this process, the model is fed a combination of the labelled as well as pseudo-labelled data to train it thoroughly.
This technique will be used in the coming days as the necessity of labelled data diminishes over time.
What is the need of Machine Learning?
If you want your business or organization to get ahead of the competition, then machine learning might just be the technological solution you need. Many businesses are currently utilizing machine learning to improve productivity, reduce costs, eliminate redundant processes & drive business growth. Some of the prominent business applications of machine learning are:
- Image Recognition
- ECommerce Applications
- Smarter Hiring Practices
These are some basic things that you need to know while getting started with Machine Learning. ARSR holds expertise in Machine Learning and Artificial Intelligence. ARSR offers a wide range of AI/ML services that include Strategy & Consulting, Customization, Development, Implementation, Integration & Robust Support.
