Future of Machine Learning
People hear about machine learning and how it can contribute greatly to any economy quite a few times. After all, it is a popular tool and is used widely. This makes them wonder, what is machine learning? And they decided to find the answer to this question.
If it sounds like you, then you have reached the right place. This article is dedicated to explaining everything you should know about machine learning, how it works, and what is the future of machine learning in India.
In this guide, we have tried to explain the mentioned topics in simple terms so that even if you are not tech-savvy, you can understand well without any issues. So without beating around the bush, let’s get straight to the point.
What is machine learning?
First things first, what is machine learning? Machine learning (ML) is a subsidiary of artificial intelligence, the computer science related to developing algorithms that have human-like working and actions.
Machine learning enables a software application to predict outcomes with accuracy. The ML algorithms use old data as input, and after analyzing and learning from input, it returns the output value.
We come across examples of machine learning on the internet every day in recommendation engines, malware threat detection, spam filtering, and more.
Machine learning has become the focal point of operations of many businesses as it provides clear insights into customer behaviour and operational patterns.
Types of machine learning?
The complete answer to the question, what is machine learning? Lies in learning not just its definition but also its types. Now that you know the definition of machine learning, let’s explore its different types:
- Supervised machine learning: When data scientists feed an algorithm labelled training data, they mark the input and output values and want the algorithm to analyze the given correlation.
- Unsupervised machine learning: In unsupervised learning, the algorithm is given unlabeled data. The algorithm assesses the different sets of input and output and tries to identify a meaningful correlation. Based on the correlation it finds, it can predict the outcome for given inputs.
- Semi-supervised learning: As the name suggests, this type of machine learning is the combination of the above two types. Here, the algorithm is fed with data that is mostly labelled. However, the algorithm is free to inspect the data and create its own meaning.
- Reinforcement learning: Here, the algorithm is programmed to complete a task. It decides what steps to take. It is provided with positive or negative cues based on its decision to guide it through the process. This type of learning is mostly used to teach algorithms to complete multi-step procedures.
How machine learning works?
Now that you know the answer to the question, what is machine learning, it’s time to understand how it works.
How supervised learning works?
The data scientist has to train the algorithm for both the input and output of a given data. This can be used for the following tasks:
- Classifying the data in two types.
- Selecting between two or more kinds of answers.
- Regression module that is finding continuous values.
- Analyzing the predictions of several machine learning models to give more precise, accurate results.
How unsupervised machine learning works?
In unsupervised machine learning, no data is labelled. The algorithm will go through different sets of inputs and outputs and try to identify a pattern that can be utilized to categorize group data into subsets. It is used for deep learning tasks such as:
- Dividing data into groups based on resemblance.
- Determining the abnormal points in data sets.
- Identifying associations between points in a data set that often occur together.
- Eliminating variables in a data set.
How semi-supervised machine learning works?
Training with labelled data can give good results. However, it is also a time-taking process. Hence, some people resort to semi-supervised learning where only some data is labelled. After learning the correlation of the labelled sets, unlabeled sets are given to apply the acquired knowledge. It has the following applications:
- Training algorithms to translate languages without having to learn a complete dictionary of words.
- Algorithms learn to apply data labels using small data sets and then automatically apply the same on more extensive data sets.
- It can be utilized for fraud detection when there are very few positive examples.
How reinforced machine learning works?
In reinforced machine learning, the algorithm is programmed to achieve a certain goal along with a predetermined set of rules to achieve the given goal. The algorithm is also given positive and negative cues whenever it makes a decision. Its purpose is to make decisions associated with positive signals and avoid those with negative cues, which helps it achieve the ultimate goal. This type of machine learning has the following applications:
- Robots use reinforced learning to learn how to do real-world tasks.
- This learning method has enabled many bots to learn how to play video games.
- When the number of resources to achieve a goal is finite, reinforced learning is used to determine how these resources can be allocated to get desired results.
How ML helps the Indian economy in the future?
We hope that the above data was sufficient to answer the question, what is machine learning? But why is it so important? The answer is Machine learning (ML) helps the Indian economy grow, and this growth will be even more significant in the coming years.
The future of machine learning is encouraging, especially in India. In fact, a study was done in 2018 to anticipate the influence of machine learning and AI on the world’s economy, and this impact was gauged on three factors:
- The behavior and dynamics of different companies and sectors.
- Disruptions may occur due to the introduction of machine learning and AI in organizations and countries.
- Dynamics of AI considering the several countries with unique characteristics.
This study indicated a colossal contribution of AI and machine learning in the world economy. It predicts that by 2030, almost 70 per cent of organizations will incorporate some form of AI.
But what is the future of machine learning in India? A more recent study by NASSCOM shows that by 2025, AI and machine learning may contribute $500 billion to the total GDP of India.
Current scenario of machine learning in India. Also, out of the $500 billion, 45 per cent is expected to come from consumer goods and retail, banking, and agricultural sectors.
ML in USA vs. ML in India
Machine learning is gaining popularity in every country of the world. Due to the advantages and convenience it brings, it has been incorporated in most types of industries. As its market continues to grow, the future of machine learning in North America will be most significant. Based on some stats, North America will be the leading contributor in the machine learning market in the future, and it may make up to 38 per cent of the total ML market.
Though the advancement of machine learning may not be as swift in India as in Western countries, the market growth would still be revolutionary. As stated before, the future of machine learning in India includes that almost 70 per cent of organizations of different sectors will infuse machine learning which would contribute to forming 45 per cent of the total GDP of India.
While machine learning has widespread applications today, it was not always a very welcomed field. However, after many years of advancement and transformations, machine learning finally turned out to be very helpful. Being one of the most-talked-about topics today, it is essential that you learn about machine learning even if it is up to an elementary level. We hope that our post was able to enlighten you about what is machine learning, how machine learning (ML) helps the Indian economy, and the future of machine learning.
Is deep learning the same as machine learning?
Deep learning, also referred to as deep neural networks, are algorithms that are designed to resemble the working fundamentals of the human brain. The algorithms analyze data to determine patterns for decision-making. Deep learning is considered a part of representation learning, which is a subsidiary of machine learning. The only key difference between the two is that, unlike traditional machine learning, deep learning algorithms can automatically extract information from unlabelled data sets.
How are machine learning, deep learning, and artificial intelligence related to each other?
Artificial intelligence is a computer science that focuses on developing intelligent machines that have the ability to work and take actions like a human. Artificial intelligence is a wide field and has many sub-concepts, one of which is machine learning, and as stated before, deep learning is a type of machine learning.
What is the future of machine learning?
Machine learning is already a thriving market, and its applications in the future are only expected to have many more applications than now. Machine learning will be used in sectors like health, education, business, and many more. So, in the future, machine learning will have many versatile applications.