An introduction to Machine Learning-Why it matters?

The technology is advancing rapidly. Presently, Machine Learning method which is a sub-field of Artificial Intelligence is utilized by a large portion of the organizations so that they can predict the activities of the users and provide them with the latest functions and solutions. This growing technology has provided more benefits to the business persons to upgrade their business processes. Machine Learning plays an important role in the areas such as data mining, natural language processing, image recognition and expert systems. It delivers possible solutions in these fields and going to provide a support for the future advancement. So, let us first understand what does machine learning actually means.

Machine learning is, fundamentally a technique of data analytics that explains the computers to perform the tasks based on the previous experience. It refers to the transitions in systems that carry out the tasks related to the artificial intelligence. These tasks incorporate the analysis, recognition, planning, robot control, forecast etc. Machine learning is an extension of artificial intelligence which is based on the fact that the systems can acquire knowledge from the data, recognize patterns and make decisions without human involvement. The main objective of the machine learning is to recognize the data structure and apt that particular data into models which can be easily understood and used by the people. However, it is an area within the computer science and contrasting from the outdated computational methods. These computational techniques involve a series of an algorithm which are the encoded instructions followed by a computer to solve a particular problem, whereas the machine learning algorithms permit the computers to get trained on provided data inputs and utilized statistical analysis to provide the output values within a particular series. Due to this, the computer can create models using the sample data so that the decision-making processes can be automated easily which are based on the data inputs.

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Evolution of Machine Learning:

Presently, the machine learning is not identical to what it was previously due to the latest computing skills. Machine learning was originated from the pattern recognition technique and the theory which can be learned by the computers without being automated to carry out particular tasks. The investigators who were involved in artificial intelligence thought to observe whether the computers can gain something from the data. It is important to know the iterative features of machine learning because when models are visible in the new data, they are capable of adapting them freely. They can get the knowledge, based on the previous computations in order to deliver the reliable, repeatable decisions and consequences. It is not a new technology, it’s a science which has obtained a renewed momentum. There are many machine learning algorithms which have been found for a long time, these algorithms have provided a recent development that the complex mathematical calculation can be applied to big data easily and quickly.

Why is Machine Learning important?

Machine learning plays a very vital role in different areas. There are various practical applications of machine learning which helps to determine the real business outcomes, like saving time and money, which will naturally going to influence the organization’s future. The machine learning has shown its great influence in the customer care industry, where it allows the people to perform the task more efficiently and quickly. The machine learning can be utilized to identify the inconsistencies, enhancing the customer experience and mentioning new products. It is proved to be useful for the future with the amount of available data including the unique computational power. Not only this, with the growth in the big data, the machine learning has become an important method to solve the problems in the areas such as image processing and computer vision to carry out the task related to the face recognition, motion and object discovery. In computational biology to detect the tumor, drug discovery and sequencing of DNA. In computational finance for credit scoring and algorithmic trading. In the field of Natural language processing for voice recognition application. In automotive, aerospace and manufacturing for predictive maintenance. With the help of machine learning algorithms, it is easy to detect the natural patterns in the data which provides an idea to make better decisions and predictions. The data are used regularly to make the complex decision in the area of medical diagnosis, stock trading etc. For example, it helps the vendors to understand the behavior of their customers for purchasing their product.

How does Machine Learning Works?

To understand the working of machine learning let us consider an example: The online shopping has become very popular nowadays. Suppose you want to purchase a smartphone online, then you will search for different products and at the same time, you will go through the product reviews, which describes its qualities. The reviews mostly include words such as great, good, best, bad, poor quality, etc., which provides an idea for the further purchase of the product and helps you to take the decision whether you should purchase the product or not. Therefore, we can say that there is a relationship between the customers and the reviews which will impact on the future customers and also their product reviews will impact on the future purchases. Thus, a pattern can be observed across the people who have already purchased the product and who is going to purchase the product. This human decision-making process can be programmed into the algorithms with the help of machine learning.

Thus, the following conditions should be met to apply the machine learning technique to a problem:

1. There should be a pattern exist in the input data which can help to take a decision. In order to solve a process using machine learning, the algorithm must have a pattern to understand the data.

2. A sufficient amount of data should exist to apply machine learning to solve a particular problem. For example, to make an online purchase for a product if there are no reviews, then it would be difficult to take a decision for purchasing of the product.

3. In order to define the behavior of the problem, machine learning is used to draw the meaning from the data and perform structured learning, so that a mathematical approximation can be made which describes the behavior of the problem.

If these conditions are fulfilled, then we can apply machine learning to solve a problem via structured implication learning. The major components of machine learning are data, output or the decision and the structured learning which is then performed by the machine learning algorithm to know the pattern of the input data to provide an output.

An expression is called as the mapping function which is used to learn the target function is formulated by the machine learning. The machine learning forms an expression which is used to map the input data to an output data. The target function (F) is not always known because it cannot be derived mathematically. Here, the machine learning is used to approximate the target function.

Therefore, machine learning performs a task of learning and based on the input, X it can make the future predictions Y. As the target functions (F) properties are unknown, therefore machine learning is utilized to estimate this target function by learning from the input data X. If the target function is known, then it is not required to use the machine learning technique. In the above example, the target function’s behavior cannot be defined using an equation and thus to derive an estimation machine learning is used. The target function tries to grab the representation of product reviews by mapping each input to the output.

To perform this, some assumptions are considered by the machine learning about the target function and then it estimates the target function using a hypothesis which may vary because the target function is not known. Thus, to derive a better function that estimates the target function properly, certain iteration needs to be performed to guess the best output. Thus, with the help of this hypothesis, the machine learning algorithm can make a better approximation of target function in a short time.

 Application of Machine Learning:

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Nowadays, Machine learning techniques are utilized in most of the industries, as they are working with huge amount of data. Machine learning can be used in everyday life to carry out online transaction as well as it can be formed with the linguistic rule creation to know the views of the customer. Not only this, the application of machine learning can be seen in the following field:

1. Marketing and Sales: The machine learning technique is used in the marketing and sales, to analyze the sales history and to promote the other items.

2. Health Care: Machine learning is used in the healthcare industry as well. With the help of wearable devices and sensors that can use data to measure the patient’s health in real time. It may also help the professionals to analyze the data to recognize the trends which can be used to improve the treatment.

3. Banking and other financial industry: Machine learning is utilized in banking and financial industry to prevent the fraud and to identify the important data.

4. Transportation: Machine learning techniques are used in transportation field so that data can be analyzed to identify patterns and trends, using which the routes can be made more efficient and predict the possible problems to enhance the profitability.

5. Education: With the help of machine learning, the teachers can check the capacity of the student that how many lessons they can learn and how much they are managing with the already taught lessons. With the help of this data, the teacher can help her student to understand the lesson in a better way.

6. Search Engine: Machine learning is used by search engines to enhance their services.

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Conclusion:

The machine learning technique is the more demanding technique nowadays. It is used by more than 80% of the companies around the world to enhance their user experience. In the past few years machine learning has made drastic enhancements and still, there are lots of revolutions which are happening in this particular field. It has become possibly the most important technology of the recent time.

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