Future Scope of Machine Learning- How to Get Job Opportunities

Future Scope of Machine Learning

Machine learning automates tedious and repetitive jobs, delivers greater insights from data, and even allows automobiles to drive themselves by giving robots the ability to ‘learn’ to replicate human behaviour.

If the current state of machine learning is interesting, the future of machine learning will provide technologists with substantially more and extremely complex options.

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What is Machine Learning

Artificial Intelligence has a subfield known as Machine Learning. It facilitates the creation of self-learning automated systems. The algorithm then improves their performance without human intervention by learning from previous experiences. This facilitates the machines’ data-driven decision-making. Machines generate predictions based on what they’ve learned from previous experience and the data they have access to. You must, for example, have navigated using Google Maps. It tries to show the quickest route with the least amount of traffic. Machine Learning algorithms are used to accomplish this goal.

Engineers construct Machine Learning algorithms so that they may be utilised to study and experience new data in order to make predictions. This allows the company to make efficient business decisions based on the ML algorithms’ forecasts. Let’s have a look at Machine Learning’s future potential in several fields.

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Future Scope of Machine Learning

“Machine learning is the process of automatically extracting business value from data,” says Lavanya Tekumalla, founder of AiFonicLabs and a Springboard mentor. This is usually accomplished by following the steps below:

• Obtaining and arranging massive amounts of data for the computer to learn from.
• Data is fed into machine learning models, which are then trained to make proper decisions through monitoring and correction.
• Extending the model’s capabilities by deploying it to make analytical predictions or feeding it with new types of data.

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Top Use Cases

1. Optimising Operations

Document management is the most typical use case for optimizing processes. A wide number of robotic process automation and computer vision firms, such as UIPath, Xtracta, ABBYY, and others, are now able to do so. Machine learning will, however, strive higher in the future.

• There are growing machine learning technologies that allow retail outlets to use thermal imaging and computer vision technology to monitor body temperatures and mask wear in order to safely return from COVID-19 to normalcy.
• Sensors and IoT technology are assisting manufacturing processes in achieving granular supply chain optimization.
• Artificial intelligence is being used by the renewable energy industry to offset the volatility of sources.

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2. Safer Healthcare

We’ve seen a tremendous increase in the usage of machine learning to anticipate and assist COVID-19 methods. The future scope of machine learning will include more complicated use cases. The healthcare business has long used ML for a variety of objectives.

• Robots are capable of executing complex surgeries with pinpoint accuracy.
• ML programs read patient histories, records, reports, and other data to create individualized treatment recommendations. In this field, IBM Watson Oncology is a significant initiative.
• Wearable technology is also making significant progress in illness prevention and elder healthcare monitoring.

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3. Fraud Prevention

Machine-learning-based fraud detection technology is used by banks and other financial organizations to prevent fraud (though the irony of proving “I am not a robot” to a machine is not lost on us!).

• To predict fraudulent transactions, banks are developing machine learning algorithms based on past data.
• Phishing emails are identified and filtered using classification and regression methods.
• To avoid identity theft, machine learning and computer vision algorithms check for identity matching across major databases in real-time.
• These pattern matching algorithms are also used to detect and prevent the forging of papers.

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4. Maas Personalisation

ML is used by retailers, social networking platforms, and entertainment platforms to provide customers with personalised services and experiences.

• The face swap filter detects and (nearly) properly swaps facial features using image recognition and computer vision techniques.
• E-commerce and media platforms are utilising machine learning to provide hyper-personalized experiences as well as freemium payment methods.

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Other Then This

1. Automotive Industry

Machine Learning is transforming the meaning of “safe” driving in the automotive industry. Several big corporations, like Google, Tesla, Mercedes-Benz, Nissan, and others, have made significant investments in Machine Learning in order to develop unique products. Tesla’s self-driving car, on the other hand, is the best in the business. Machine learning, Internet of Things sensors, high-definition cameras, voice recognition systems, and other technologies are used to create these self-driving cars.

All you have to do now is get in the car and drive to the spot. It will locate the most direct route to that location and ensure that you arrive safely. What a treat it would be to witness such a magnificent human creation! All of this is made possible via Machine Learning.

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2. Robotics

Robotics is a discipline that has piqued the curiosity of both scientists and the general public. The first programmable robot, Unimate, was created by George Devol in 1954. Hanson Robotics developed the first AI-robot, Sophia, in the twenty-first century. Machine Learning and Artificial Intelligence were used to make these breakthroughs possible.

Robots that imitate the human brain are still being developed by researchers throughout the world. In this study, they use neural networks, artificial intelligence, machine learning, computer vision, and a variety of other technologies. We may encounter robots in the future that are capable of doing duties similar to those performed by humans.

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3. Computer Vision

Computer vision, as the name implies, provides the vision to a computer or machine. What comes to mind is what Google’s Head of AI, Jeff Dean, once said: “The improvement we’ve achieved from 26 percent mistake in 2011 to 3 percent error in 2016 is highly meaningful.” I like to conceive of computers as having evolved working eyes.’

The purpose of computer vision is to enable a machine to detect and analyze photos, videos, graphics, and other data. Artificial Intelligence and Machine Learning advancements have made it possible to attain the goal of computer vision more quickly.

4. Quantum Computing

In the discipline of Machine Learning, we are still in the early stages. In this subject, there are numerous advancements to be made. Quantum computing is one of them, and it will take Machine Learning to the next level. It’s a sort of computing that makes use of quantum mechanical phenomena like entanglement and superposition. We can design systems (quantum systems) that can exhibit many states at the same time by utilizing the quantum phenomena of superposition. Entanglement, on the other hand, is a phenomenon in which two separate states can be referenced to one other. It aids in describing the relationship between a quantum system’s attributes.

Advanced quantum algorithms are used to create these quantum systems, which process data at a rapid rate. Machine Learning models have more processing capacity when they are processed quickly. As a result, the future scope of Machine Learning will increase the automation’s processing capability.

How to Get Job Opportunities

More than 23,000 positions for an ML engineer are presently listed on LinkedIn, with hiring continuing throughout the year. PayPal, Morgan Stanley, Airtel Payments Bank, Google, Autodesk, and more organisations are currently hiring.

Because machine learning necessitates knowledge of computer programming, statistics, and data analysis, your machine learning job could lead to leadership roles in automation or analytics environments that employ data science, big data analysis, AI integration, and other techniques.

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Future Scope of Machine Learning- Salary Trend

In India, an ML engineer earns an average of 687,250 rupees. As seen in the figure below, this is greater than other similar tech jobs such as data scientist, software engineer, and data analyst.

According to the LinkedIn community, ML wages can rise to 19,30,000 with 6-14 years of experience. ML engineers with extra abilities in deep learning, natural language processing, computer vision, and other areas will be able to take on multi-skill opportunities like this one at Accenture for a machine learning application developer.

Skills Required to Become a Machine Learning Engineer

1. Programming: For any Machine Learning enthusiast, programming is one of the most critical components. R and Python are the most commonly used languages in Machine Learning. Both can be learned. Machine Learning with Python, on the other hand, has a lot of potentials.
2. Knowledge of data structures: Any software’s data structure is its foundation. As a result, having a thorough understanding of data structure ideas is recommended.
3. Mathematic: Without mathematics, we are unable to perform computations. As a result, we must be able to apply mathematical principles to Machine Learning models. Calculus, linear algebra, statistics, and probability are examples of these topics.
4. Software engineering: Models for machine learning are created to work with the software. As a result, an ML Engineer should be well-versed in software engineering.
5. Data Mining and Visualization: Understanding the data becomes increasingly important when we build Machine Learning models on top of varied sources. A Machine Learning enthusiast must have prior knowledge of data visualization and mining in order to do so.
6. Machine Learning algorithms: Along with all of this, we should have prior expertise implementing various machine learning methods.

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