Since the beginning of time, human beings have been utilizing various types of tools to accomplish numerous tasks. The imagination of the human brain led to the invention of various machines. Life becomes easy with these machines as they empower and enable humans to fulfill various needs, including traveling, industries, constructions, and computing.
Despite rapid advancement in the machine industry, intelligence remains the significant difference between humans and machines in doing their tasks. A human uses his or her sense to collect information from the environment; the human brain analyzes that information and then takes appropriate decisions. On the other hand, machines are not intelligent by nature. A machine cannot analyze data and take decisions.
It is when Alan Turing thought if it possible for the machine to think, then the intelligent machines’ era began in/around the mid 20th century. Since then, artificial intelligence (AI) has been evolving rapidly.
The term machine learning means to enable machines to think and learn without using any software. The goals of the machine-learning are to enable machines to do prediction, clustering, and make well-informed choices from a given dataset. Machine learning can be considered as a branch of artificial intelligence. The statistical learning methods act as a backbone of intelligent software which is used to create machine intelligence.
Four machine learning methods are explained below.
In this machine learning technique, the objective is to conclude a function or mapping from the training data that is labeled. The training data contains X (input vector) and Y (output vector) of labels or tags. A tag or label from the Y vector is the description of its respective input example from X (input vector). Together they create a training example. In other words, the training data contains training examples.
In this method, there is a lack of supervisors or training data. To put it simply, unlabeled data is what all we have. The objective is to find a hidden structure in this data. There can be various explanations for the data not having a label. It can be because of the unavailability of investments to pay for manual labeling or the innate nature of the data itself. With various devices of data collection, data is collected at an unmatched rate.
In this type of machine learning method, the given data are a combination of classified and unclassified data. This mixture of labeled and unlabeled data is utilized to produce a suitable model for the classification of the data. In many of the cases, labeled data is very less and there is plenty of unlabeled data. To grasp a model that can predict classes of future test data is the primary target of semi-supervised learning.
This type of machine learning technique intends to use observations collected from the interaction with the surroundings to take actions that would increase the reward or reduce the risk.
Machine learning is a combination of various fields and it has proved itself to be able to solve many real-world problems and challenges. Let’s see some applications of machine learning:
The US Postal Service was successfully in using the potential of machine learning in the 1960s when they were able to use machines to automatically read the different address codes of typed addresses to sort letters. There is a typed, handwritten, or printed content of the text in the images that are readable for humans. Optical character recognition technology is utilized so that machines can also read such text.
OCR software tries to analyze the image and attempts to recognize the alphabetic letter and numeric digit. When it successfully identifies a character, it converts it into machine-encoded text. Then, we can electronically edit, search, and compress this machine-encoded text. Moreover, this encoded text can further be used as an input for various applications like automatic translation, text-to-speech, and text mining. With the help of precise OCR, data entry becomes easy, quicker, and cost-effective.
Algorithms used for pattern recognition can be helpful in computer-aided diagnosis. It can help doctors in explaining medical images in comparatively less time. Medical images of the various medical tests, for example, X-rays, MRI, and ultrasound are the sources of information explaining a patient’s condition.
A radiologist has to analyze and gauge the output of these medical tests that are in the form of digital images. The radiologist must be assisted by a machine due to time constraints. Computer-aided diagnosis utilizes methods of pattern recognition from machine learning to recognize doubtful things in the image.
The primary objective behind making robots is to provide them with the ability to see and behave accordingly after understanding the situation. The in-built cameras in a robot can give images. However, they will not assist the robot to identify or interpret the image. But what type of learning can a robot do by using pattern recognition?
There is a popular event called Robot Soccer World Cup which is a global tournament of soccer for robots. The RoboCup 2015 which was held in China got massive popularity. In the game, the robots are anticipated to coordinate and cooperate with the other members (which are also robots) of their team to win the match. They must categorize objects and identify activities. They do such tasks with the help of input from their cameras. These tasks come purely under the domain of pattern recognition, a branch of machine learning.
The domain of speech recognition intends to create techniques and methods that can enable the computer to identify and translate spoken language into text. Translation of speech that is automatic finds its applications in numerous settings such as court reporting and video captioning.
This innovation can be beneficial to people with disabilities. Over a period of time, the precision of speech recognition has been improving. While it is a fact that voice-controlled software, for example, Amazon’s Alexa, Microsoft’s Cortona, and Apple’s Siri fail to understand our speech sometimes, but the things will likely improve in the coming future.
Machine learning is the science of computer algorithms that has been consistently improving and has been central to artificial intelligence since its inception. With the rapid evolution of the field, there has been a rise in the uses, demands, and significance of machine learning.
Machines can help in filtering out only useful information that assists in a major development, and we are already observing how the innovation of machine learning is being utilized and implemented in a wide range of industries. Recent advancement in machine learning has been boosted by the development of new computer algorithms & theory and by the ongoing expansion in the availability of online data and low-cost computation. The acceptance of various types of machine learning methods can be seen in various fields such as science and technology.
With extensive ongoing research and rapid advancement, it can be assumed that machine learning will flourish extensively in the near future. Due to the high demands of machine learning techniques across different industries, the career prospects in this field are bright and lucrative.
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Owing to infinite applications in numerous fields, a career in machine learning will never disappoint you. It is a stable and lucrative career option that will provide money and balanced life. According to some reliable online website, the average salary of a machine learning engineer in India is approx. 7 lakhs per annum. The bright side is this would increase substantially with experience.
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An entry-level Machine Learning Engineer with less than 1-year experience can expect to earn an average total compensation (includes tips, bonus, and overtime pay) of ₹501,571 based on 113 salaries. An early career Machine Learning Engineer with 1-4 years of experience earns an average total compensation of ₹693,442 based on 328 salaries. A mid-career Machine Learning Engineer with 5-9 years of experience earns an average total compensation of ₹1,159,032 based on 78 salaries. An experienced Machine Learning Engineer with 10-19 years of experience earns an average total compensation of ₹1,948,718 based on 19 salaries.
Machine learning is not a new subject. It can be considered as a subset of artificial intelligence. The current development in machine learning is bound to the growth in some important areas such as data availability, computer power, and algorithmic innovation.
Machine learning finds its application across various fields such as speech recognition, computer diagnosis and robotic, etc. Moreover, machine learning has been successful in creating a revolution in these fields by taking them to the next level of functionality and efficiency. Therefore, machine learning will take an upward curve in the near future when it comes to the applications. This field will grow more and more in the next few years.