What is Machine learning with Examples and Why it is Important ?

Raktim Singh

May 26, 2021

Let’s understand What is Machine learning with Examples and Why it is Important?

Machine learning (ML) is the study of computer algorithms that improve their outcome & result automatically while processing various types of data.

Machine learning algorithms build a model based on sample data.

This sample data is known as “training data” in order to make predictions or decisions without being explicitly programmed to do so. (Wikipedia)

To put this in simple terms, now machines are able to learn on their own. That is, with one initial algorithm, computing power & big abundance of data, machines keep on improving the initial algorithm.

As a part of scientific research, machine learning came from a curiosity for artificial intelligence.

Basically, Machine learning is a field of digital science, computer science, and artificial intelligence.

Or We define Machine learning as the Automatic detection of Meaningful patterns from data, and now Machine Learning has become a powerful source to extract powerful patterns through any form of data.

It all started when during the early days of AI evolution, many researchers started experimenting with Machine learning from the data.

They tried to solve the problem with different symbolic methods and

these were mostly perceptron (algorithm for supervised learning of binary classifiers) and other models that were later found to be reinventions of the generalized linear models of statistics.

Later in the 1990s, Machine learning was reorganized as a separate field. It changed its goal from acquiring artificial intelligence to tackling the solvable problem of a practical nature.

Now Machine Learning has conquered the Industry.

It is now the main reason for the existence of the number of high-tech products.

Like Powering Smartphones, Intelligent searches, Recommended products, and much more.

Nowadays, AI is a big topic, and ML is the main reason behind this.

How Machine Learning Help Business?

1) It eliminates manual entry and reduces personal involvement

2) It is a very helpful in voice command related work

For Amazon, the voice assistant is Alexa
Google Assistant.
Apple has developed Siri as their voice assistance
Microsoft has Cortana
and Samsung has Bixby

3) Image recognition

This technology is widely used in car parking, Toll roads, offices, and many other places,

Face recognition is now widely used in Smartphones, entry and exit points, and many other places.

Basically, Image recognition enhances the overall customer experience.

4) It helps in Financial analysis. It’s very useful in Share markets analysis, various report regeneration where big data (related to purchase, sale, gains, tax..etc)is involved.

5) Medical Diagnose

6) Cybersecurity

and many other dimensions.

What is the basic premise of machine learning?

Machine learning is working on the principle that digital machines “learn” the way humans learn and process information. Or this is also known as algorithm training that is automated.

Machine learning is the use of techniques to automate the analysis and production that is trained on. It uses techniques to achieve automated data and algorithm analysis.

What is machine learning with Examples and its model?

A machine learning model is a set of inputs and outputs which helps achieve the tasks to be accomplished.

It is the end result of the trained machine. A machine learning model can be in any format. Once trained, the model can be used across multiple different tasks and industries.

How can machine learning models be used?

Once trained, machine learning models can be used in any digital or manual process that needs automated learning to do something. In practical terms, the model can be used in areas where the traditional workflow process is inefficient.

For example – A product manufacturing environment where the humans can be slower and resource-intensive.

It also can be used in other industries such as healthcare and banking.

We can say that Machine learning works in a way that the digital machines “learn and understand” the way humans learn and process information.

When do we Need Machine Learnings?

The answer lies in two major aspects

1) How complex is the problem.

2) Task that is beyond Human capabilities to Solve it.

In simple words, a task that requires huge memory and very good processing speed.

Approaches for What is Machine Learning with Examples)

Machine learning approaches are divided into three categories, depending on the nature of signal or feedback accessible to the learning system.

Supervised learning: In this approach, the “teacher” presents the computer with the desired output, and the target is to learn a general rule that maps input to output.
Unsupervised learning: The learning algorithms are not labelled, leaving on their own to find structure in its input. Unsupervised learning can itself be a goal or a means towards an end.
Reinforcement learning: A computer application interacts with a varied environment in which it performs a certain target. As it comes across the problem space, the program is provided with feedback that is similar to rewards, which it tries to maximize.

For machine learning, input data is key. With respect to data, 2 things are important. Based on algorithm, either data is ‘Classified’ or ‘Put in Clusters’.

I hope you got a fair idea of What is Machine learning with Examples and Why it is Important?

In future articles, I will be talking more about ‘Classification’, ‘ Clustering’, ‘Exploration’, ‘Exploitation, various techniques like “Random forest regression’, ‘Upper Confidence Bound’, ‘Multiple Arm Bandit & ‘Thomson Sampling’ etc.