The concept of algorithms that teach themselves to make predictions is just kind of… cool. Machine learning is a concept that enables machines or systems to mimic human behavior. It is a subset of Artificial Intelligence. It enables the machines to observe data and patterns in data to make decisions and predictions. This is where data science comes into the picture. The system can learn from its past behavior and experience, precisely, from its data. Machine learning has applications in image recognition, cybersecurity, healthcare, face recognition, and image recognition. It gives a sense of understanding of systems and machines. Machine learning is still at its discovery phase, there is a long way to go. This also means there is a huge scope of research and development.
As a developer, you might have been intrigued by machine learning and there is a good chance that you will need to devote at least 2-3weeks to study multivariable calculus and linear algebra.
So why is it important and matters a lot?
Machine learning is a branch of AI and works on the concept that systems can make decisions with the minimum intervention of humans. This is done with the help of data and identifying patterns. In short, ML is a method of data analysis that automates analytical model building.
Machine learning is used to observe large volumes of data and make predictions using statistical algorithms. These large volumes of data are usually extracted from various sources and are raw unstructured data. This data is organized and manipulated using data science techniques to extract insights and meaning. This data used with Artificial intelligence and machine learning gives a capacity to the system to imitate human-like behavior. Here, we discuss some of the applications of machine learning:
- Virtual personal assistant: We all know about Amazon Alexa, Google Now, and similar such assistants which provides us customized information. This is achieved through machine learning. This personal assistant collects information from your previous data, or in case you want to use any app, the personal assistant sends a command to the app and provides you the information that you asked for. Some of the examples are:
- Smart speakers like Amazon Alexa and google home
- Mobile apps like Google Allo
- Social media services: we unconsciously use machine learning on our social media. The ad recommendations and friends recommendations, similar likes, dislikes and so much more, are all impact of machine learning. Here are a few examples to show how machine learning has revolutionized the social media platforms:
- People you may know: Facebook and similar platforms record your data about the common interests with people, the number of mutual friends, and the profiles you visit. On the basis of all this information collected, you are offered a list of people who you want to add in your connection.
- Face recognition: this is absolutely a stunning feature. As soon as you upload a person, the system identifies and suggests a name to tag that person. While this includes a complicated process in the backend with precision concerns but is an application of machine learning.
- Computer vision: machine learning is the core of computer vision. It includes extracting useful information from images and videos and this is used to identify similar information in other images and recommend similar pins.
- Online customer support: many websites and apps provide an option of chatting with customer support while you browse through their content. Not all customer support is real executives sitting on the other side. Some use chatbots that use the data from their websites to answer your queries. With the help of machine learning and artificial intelligence, these chatbots are evolving and are capable of responding to more generic issues with a more personal touch.
- Government: Government agencies such as public safety and utilities have a particular need for machine learning since they have multiple sources of data that can be mined for insights and research purposes.
- Health care: The wearables gadgets are fixed with data that can be observed to analyze the pattern of health progress or failure and provide correct diagnosis and treatment. These gadgets can collect significant data like heartbeat rate, pulse rate, breathing patterns, and so much more, that can be observed to provide correct diagnosis and treatment. Also, machine learning can be revolutionary to determine the outbreak of an epidemic. The different patterns of increase in the number of active cases, the treatments available, the number of deaths, availability of health equipment, and many more, can be observed to determine the upcoming trend in the pattern.
- Product recommendations: While shopping on many websites, you might have noticed that you get recommendations based on your taste. This useful feature is performed through machine learning. It derives information from the products you viewed earlier, items you ordered, items in your cart or in your wishlist, and provides you suggestions based on that.
Machine learning is a fast-growing technology with a lot of job opportunities and high pay. Machine learning is the future, it is already used in different sectors. This is the best time to learn machine learning as it has a wide scope in terms of research and development, and as it is obvious, our dependency will increase on machine learning. The features of customization have received great feedback from customers and every platform is incorporating machine technology techniques to deliver a personalized experience. So, if you want a challenging role or you are enthusiastic to learn and explore new opportunities, then machine learning is for you. Also, getting a certification will help you in getting better opportunities and better pay. However, make sure to get training from only a recognized institute. The average pay of a machine learning engineer salary is much better as compared to other technology jobs. According to Indeed.com, the average machine learning salary is approximately $146,085.