What is machine learning: Definition, Types, and Applications?

Machine learning is an application of artificial intelligence (AI) that enables computer to think and learn on their own. Its means systems the ability to automatically learn and improve from experience without being explicitly programmed. Machine learning algorithm focuses on the development of system programs that can access data and use it learn for themselves

Have you ever shopped online? So while checking for a product, did you noticed when it recommends for a product similar to what you are looking for? or did you noticed “the person bought this product also bought this” combination of products. How are they doing this recommendation? This is machine learning.

image source – Google

What is Machine Learning?

Machine learning is an application of artificial intelligence (AI) that enables computers to think and learn on their own. It means systems the ability to automatically learn and improve from experience without being explicitly programmed. The machine learning algorithm focuses on the development of system programs that can access data and use it to learn for themselves.

To better understand the uses of Machine Learning, consider some instances where Machine Learning is applied: the self-driving Google car; cyber fraud detection; and, online recommendation engines from Face book, Netflix, and Amazon. Machines can enable all of these things by filtering useful pieces of information and piecing them together based on patterns to get accurate results.

Types of Machine Learning

Machine learning is generally split into three main categories:

Supervised Learning:

Supervised learning is a method in which we teach the machine using labeled data, Dataset which acts as a teacher and its role is to train to the system or the machine. Once the model gets trained it can start making a predication or decision when new data is given to it. Supervised learning is extremely powerful when used in the right circumstances.

Ex: E-mail spam filtering.

Unsupervised Learning:

In unsupervised learning, the machine is trained on unlabelled data without any guidance. Once the model is given a dataset, it automatically finds patterns and relationships in the dataset by creating clusters in it.

Suppose we presented images of apples, bananas, and mangoes to the model, so what it does, based on some patterns and relationships it creates clusters and divides the dataset into those clusters. Now if a new data is fed to the model, it adds it to one of the created clusters.

Ex: Netflix Recommendation of content to the users according to their interests.

Reinforcement Learning:

In Reinforcement Learning, the algorithm focuses on regimented learning processes, where a machine learning algorithm is provided with a set of actions, parameters, and end values. The algorithm tries to find the best possible way to complete the task for better results. So, basically, it works on the principle of rewards and punishment. It learns from past experiences and begins to adapt its approach in response to the situation to achieve the best possible result

Ex: Computers chess game.

 Applications of ML:

Now, what are some of the benefits of doing this? Let’s look at some of the popular applications of ML:

Machine learning is a buzzword for today’s technology, and it is growing very rapidly day by day. The value of machine learning technology has been recognized by companies across several industries that deal with huge volumes of data. We are using machine learning in our daily life even without knowing it is Google Maps, YouTube, Alexa, etc. Let’s see some most trending applications of machine learning:

Virtual Personal Assistants: Siri, Cortana, Alexa, YouTube, Google Now

Finance:  Companies in the financial sector are able to identify key insights in financial data as well as prevent any occurrences of financial fraud

Social Media: Face Recognition, People You May Know, Pages You Might Know

Retail: Product Recommendations — maximization of revenue by learning customers’ habits

 Product recommendations: Machine learning is widely used by various e-commerce and entertainment companies such as AmazonNetflix, etc., for product recommendation to the user.

Healthcare:  With the advent of wearable sensors and devices that use data to access the health of a patient in real-time, Sensors in wearable provide real-time patient information, such as overall health condition, heartbeat, blood pressure, and other vital parameters.

Search Results: When you search on Google, the backend keeps an eye on whether you clicked on the first result or went on to the second page – the data is used to learn from mistakes so that relevant information can be found quicker next time

We’ve reached the conclusion of our discussion. We hope you liked this article. If you have any questions on this topic or on how to become an ML professional, feel free to ask us. We’d love to hear from you.

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