With the coming of machine learning and AI together, there are many opportunities for people to grab. Machine learning is a further branch of Artificial intelligence that focuses on building applications that can learn from data and improve accuracy with time, even though they are not programmed to do that. The algorithms trained in machine learning are trained to find features and patterns in massive data forms. The accuracy of algorithms will generate more accurate decisions and processes. 

We are all surrounded by machine learning examples, whether we have digital assistants at our home who respond to our voices immediately. Whether it is your favourite brand recommending you to buy what you like or have already shopped, whether it is a deep analysis of medical problems within seconds or minutes. And now the cars that are fully hitting the road by their automatic functions, each and everything has machine learning involved. As computing will become more affordable and powerful and with the data scientists developing new algorithms, machine learning will bring more efficiency to our lives and workplaces. 

The career in machine learning has been trending for a few years, but now both the field AI (Artificial Intelligence) and Machine Learning. Youngsters are looking to adopt the same career opportunities and to explore a different world of data. These fields are related to the data world, the data has become the primary source for the companies, and most importantly, the companies want to keep their data secure. To keep the data safe, the organization and big brands are investing in heavy machine learning techniques.

Methods of Machine Learning

  1. Supervised machine learning
  2. Unsupervised machine learning 
  3. Semi-supervised learning 

Real-world Examples

  • Digital Assistants are all famous assistants like Google Home, Siri, and Alexa-are powered by natural language processing known as NLP. A machine learning application can help the computer process the voice data and understand language similarly as people do. Through Natural language processing, applications like GPS and speech recognition also work. 
  • Recommending products and services: the deep learning model, which comes under machine learning, helps develop. Indeed, you must have seen a suggestion on apps like Amazon saying ‘just for you’ or recommended for you these coming from machine learning only. Netflix also gives you tips on the type or genre of shows you watch. This is also achievable through machine learning. 
  • Chatbots: Chatbots are another form that can be achieved through natural language processing and deep neural networks. Through these, they can interpret the text and respond the same. 
  • Fraud Detection- Fraud detection is another cyber issue that we face in this digital pacing world. Machine learning can produce many fraud detection systems that help identify frauds like stolen credit cards, illegal use of data, and many others. 
  • Cybersecurity: With the upcoming of cyber scams and cybercrimes happening in we need more fast cybersecurity systems. We can extract potential threats through machine learning and come to solutions or conclusions as soon as possible. 
  • Medical image data: After the Coronavirus pandemic, there has been an immense need for technology in the medical industry. Digital imaging medical data has been in a massive amount. Digital machine learning and other deep learning models have successfully extracted or taken out information and other material required to analyse any diagnosis. These have helped the people to know the medical issue they have and what medical treatments they require. 
  • Self-driving car: The future is in self-driving cars. The time will soon come when we will have self-driving cars, making our lives more convenient, fast, and accessible. Self-driving vehicles require machine learning techniques to identify the objects near them, look at how the car will move, how the surrounding things are changing and moving, and others. The vehicle will need to figure out and guide itself towards the destination where the person wants to reach. 

Applications of Machine Learning

The algorithms of machine learning are used at places where the solution is required for improvement. The machine learning solutions are very dynamic and flexible due to which is adapted by most of the big companies. The algorithms of machine learning and its answers are very versatile. That is why it can be used in places of human labour skilled at a medium level. 

A very common or layman example for this is the customer care executives are replaced by the natural language processing machine learning algorithms, which are also named chatbots. 

With all the hyped-up technologies, machine learning and Artificial intelligence have been on the boom in recent years. But there is more future and more scope for machine learning. Let us have a look at those: 

1. Automotive Industry

This industry has majorly used the technology for machine learning to promote the concept of safe driving for people nowadays. Some of the automobile industry’s biggies like Mercedes Benz, Google, Tesla, Nissan, and others are adopting massive machine learning techniques and investing in them massively for some super innovations.

One of the examples is Tesla’s self-driving car which has been made by using machine learning. Like high definition cameras, IoT sensors, voice recognition systems, and other features have been built through machine learning. The self-driving car can make you reach your destination safely on your advice. These are created, and new creations have been possible through the concept of machine learning. 

2. Robotics 

Robotics has gained attention from researchers as well as ordinary people. Researchers around the globe are creating robots that can copy the human brain. For this creating, they are using Artificial intelligence, machine learning, and other techniques. No one knows; it might be possible to have robots who can apply the same tasks as humans can. 

3. Quantum computing

We are still at the learning stage of machine learning with all these developments. There are a lot of genres that can take machine learning to a different significance level. One of them which can machine learning to a new story is quantum computing. 

Skills required to become a machine learning engineer

  1. Programming: Programming is one of the basic requirements for any machine learning person. The majorly used languages are R and Python. You can learn both of them but keep in mind that machine learning with Python is considered much higher. 
  2. Data structure: One who wants to invest and have a career in machine learning should know the basic understanding and reading of the data structure. Whether it is machine learning or Artificial intelligence, i.e., AI, both require learning data structures. 
  3. Mathematics: There is no point in learning to compute; without mathematics, it is not possible. We need mathematical concepts to apply in the machine learning models—these consist of algebra, stats, probability, and others. 
  4. Software engineering: The models made by machine learning are integrated through software, so one needs to have proper knowledge and skills as a software engineer. 
  5. Data visualization and mining: Machine learning models are built on various data. So it is good and equally important to know the visualization of data. If you have any prior experience in it, you have more bonus points as a machine learning engineer. 
  6. Machine learning algorithms: You should know that where the algorithms will be implemented. It is all based on algorithms. So having a nag in it will be of great help.

Machine learning in Upstox

This application, named Upstox, has been in the recent trend of the market. It is used in the share market industry for trading purposes. This application helps the users get good insights into the market and know about the progressing companies without any brokerage. 

machine learning

The use of machine learning in these applications have been used to:

  1. Real-time information: This app uses machine learning techniques to look into the lead and get out the confidential data. This can provide accurate and appropriate information to the app users, making all their efforts easy to extract the data.
  2. Prediction of stock: This app Upstox identifies traders’ data and predicts the stock market’s ups and downs. All these are done by using machine learning algorithms. This further helps the investors to help their money correctly without having any chances of losing it. 
  3. Security: This app also uses machine learning systems to detect fraudulent activities and make the app safer for people to use. 

Trading nowadays has become a new and easy way of earning money. Many people are investing in the stock market, and all this has been possible with machine learning techniques. No matter that there is a job opening now, machine learning is a career that will flourish for a long. There are lots of employment and job opportunities in this if one sees it as a dream job. If you have relevant knowledge and, most importantly, an appropriate skill set, you are the best fit for this industry. 

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Also Read- What is Edge Computing? How it is changing the network?

About the Author

Sakshi Gautam


I am a freelance content writer working in the field for the past five years. I specialize in blog posts, articles, and websites contents for e-commerce, education, immigration, travel and food platforms. For a platform to grow, I believe that it needs quality contents and uniqueness that stands out from competitors.

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