Let us look at the topics that will be going through in this blog:
Why do we need Machine Learning?
We have heard a lot about the scope of Machine Learning, its applications, job and salary trends, etc. But, do you know, what is Machine Learning? Why do we need Machine Learning? Where do we use it? To answer these questions popping up in your mind, this blog will use an application of Machine Learning in the investment sector or the stock market and try to understand the need and future scope of Machine Learning.
The investment sector has always been a profitable business. In earlier days, investing money required a thorough knowledge of domestic as well as international markets. People used to manually study and analyze the trends of the market. The manual analysis required a lot of time. But, nowadays, as the scope of Machine Learning is widening, we can see a lot of mobile applications that provide us assistance within seconds for investment in various sectors. For making a smart investment in the stock market, there is an application called ‘Upstox.‘ It uses Machine Learning for predicting the future possibilities of the market. Let us discuss it in detail.
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Use of Machine Learning in upstox
The application called Upstox is trending in the market. It is a trading application used for the stock market. This application helps us in getting insights into the market and various growing companies without any manual intervention and brokerage.
The suggestions given by the application help in investing money in the right place. This is all possible with the help of Machine Learning. Due to the Machine Learning techniques, it provides us with features such as:
- Real-time information: The application gives us the current details of the market trends. Also, it uses techniques of Machine Learning to process the information and find the hidden trends in the data to provide us with the proper market information.
- Stock prediction: Upstox visualizes the data of traders and predicts the ups and downs of the market. For smart prediction, it uses Machine Learning algorithms. This helps us properly invest money in stocks with lesser chances of losing it.
- Security: The app uses built-in Machine Learning systems to predict fraudulent activities that make it secure for users.
There are more applications similar to Upstox in the market. However, coming to the point of discussion now, in the 21st century, trading has become an easy way of earning money. This is only possible with the help of Machine Learning. Further, in this blog on the scope of Machine Learning, we will have a quick glance at what exactly Machine Learning is.
What is Machine Learning?
Machine Learning is the sub-field of Artificial Intelligence. It helps to build automated systems that can learn by themselves. Then, the system enhances their performance by learning from experience without any human intervention. This helps the machines make data-directed choices. Whatever the machines learn from past experience using the available data, the machines use it to make predictions. For example, you must have used Google Maps for navigation. It tries to show the fastest route with less traffic and congestion. It accomplishes this task by using Machine Learning algorithms.
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Engineers create the Machine Learning algorithms in such a way that the algorithm gets used to explore and experience new data for prediction. This gives the benefit to the organization for making effective business strategies as per the predictions of the ML algorithms. Now, let us check the future scope of Machine Learning in various sectors.
Future Scope of Machine Learning
The scope of Machine Learning is not limited to the investment sector. Rather, it is expanding across all fields such as banking and finance, information technology, media & entertainment, gaming, and the automotive industry. As the Machine Learning scope is very high, there are some of the areas where researchers are working toward revolutionizing the world for the future. Let us discuss them in detail.
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The automotive industry is one of the areas where Machine Learning is excelling by changing the definition of ‘safe’ driving. There are a few major companies such as Google, Tesla, Mercedes Benz, Nissan, etc. that have invested hugely in Machine Learning to come up with novel innovations. However, Tesla’s self-driving car is the best in the industry. These self-driving cars are built using Machine Learning, IoT sensors, high-definition cameras, voice recognition systems, etc.
You just need to sit in the car and enter the location. It will find the best possible route to that location and will ensure to safely drive you to the specified destination. How wonderful it would be to experience such a great creation by humans! This is all possible with the help of Machine Learning.
Robotics is one of the fields that always gain the interest of researchers as well as the common. In 1954, George Devol invented the first robot that was programmable and it was named as Unimate. After that, in the 21st century, Hanson Robotics created the first AI-robot, Sophia. These inventions were possible with the help of Machine Learning and Artificial Intelligence.
Researchers all over the world are still working on creating robots that mimic the human brain. They are using neural networks, AI, ML, computer vision, and many other technologies in this research. In the future, we may come across robots that would be capable of performing various tasks similar to a human.
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We are still at an infant state in the field of Machine Learning. There are a lot of advancements to achieve in this field. One of them that will take Machine Learning to the next level is Quantum Computing. It is a type of computing that uses the mechanical phenomena of quantum such as entanglement and superposition. By using the quantum phenomenon of superposition, we can create systems (quantum systems) that can exhibit multiple states at the same time. On the other hand, entanglement is the phenomenon where two different states can be referenced to each other. It helps in describing the correlation between the properties of a quantum system.
These quantum systems are built using advanced quantum algorithms that process data at high speed. Fast processing enhances the processing power of Machine Learning models. Thus, the future scope of Machine Learning will accelerate the processing power of the automation system used in various technologies.
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As the name suggests, computer vision gives a vision to a computer or a machine. Here comes into our minds what the Head of AI at Google, Jeff Dean, has once said, ‘ The progress we’ve made from 26% error in 2011 to 3% error in 2016 is hugely impactful. The way I like to think is, computers have now evolved eyes that work.’
Giving the ability to a machine to recognize and analyze images, videos, graphics, etc. is the goal of computer vision. The progress in the field of Artificial Intelligence and Machine Learning has made it possible to achieve the goal of computer vision faster.
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Machine Learning Job Scope and Salary Trends
The scope of Machine Learning in India, as well as in other parts of the world, is high in comparison to other career fields when it comes to job opportunities. According to Gartner, there will be 2.3 million jobs in the field of Artificial Intelligence and Machine Learning by 2022. Also, the salary of a Machine Learning Engineer is much higher than the salaries offered to other job profiles.
According to Forbes, the average salary of a Machine Learning Engineer in the United States is US$99,007. In India, it is ₹865,257. Let us look at the graph of top job profiles listed by Indeed.
This shows that the Machine Learning scope is extremely high in terms of salary and the number of job opportunities. Thus, it is a good option to make a lucrative career in ML by becoming a Machine Learning professional. Further, in this blog on the future scope of Machine Learning, we will look into the skills that are required to become an ML Engineer.
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Skills Required to Become a Machine Learning Engineer
There are certain skills that you need to master for becoming a successful Machine Learning Engineer and they are:
- Programming: Programming is one of the important aspects for any Machine Learning enthusiast. For Machine Learning, we generally use R and Python languages. We can learn both. However, the scope of Machine Learning with Python is high.
- Understanding of data structures: The data structure is the core of any software. Thus, it is recommended to have a good grasp of the concepts of data structure.
- Mathematics: We cannot perform computation without mathematics. Therefore, we should have knowledge of applying mathematical concepts into Machine Learning models. These concepts include calculus, linear algebra, statistics, and probability.
- Software engineering: Machine Learning models are built to integrate with the software. Thus, an ML Engineer should have a thorough knowledge of software engineering.
- Data mining and visualization: As we built Machine Learning models on top of various data, it becomes essential to understand the data. For this, a Machine Learning enthusiast must have experience in data visualization and mining.
- Machine Learning algorithms: Along with all these, most importantly, we should have experience in implementing various ML algorithms.
In this blog on the future scope of Machine Learning, we have looked around the need for Machine Learning. Also, we have seen the future scope of Machine Learning and the opportunities in the field. We can make a bright career in Machine Learning by mastering it and becoming an ML professional.
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