Artificial Intelligence, Machine Learning, Deep Learning, and Data Science- Many people tend to confuse these complex terms of computer science. A lot of fellows think they know the differences, but actually, they do not.
The roads are too much intermingled and overlapping that it becomes difficult for the non-professional to understand, where one road ends and the other one starts.

Although, the objective of our article is applications of Machine Learning in daily life, first, we want to make sure the reader does not confuse it with Artificial Intelligence and Deep Learning. Look at the following picture to understand the difference.

So you can think of them as the dolls nested within each other. Machine Learning is a subset of Artificial Intelligence, and Deep Learning is a subset of Machine Learning. In summary:

  • All Machine Learning is Artificial Intelligence, but not all Artificial Intelligence is Machine Learning.
  • All Deep Learning is Machine Learning, but not all Machine Learning is Deep Learning.

We will explain later how Machine Learning relates to Data Science.

The term Machine Language was first defined in the ’90s by Arthur Samuel. He was the pioneer in the field of computer gaming and artificial intelligence. That definition is something like that, “It is a field of study that gives the computer the ability to self-learn without being explicitly programmed”.

Now let us explore amazing applications of Machine Learning in our daily life. It is being used in many industries and professions.

Image Recognition

The term must not be alien for smartphone users. It is one of the most common machine learning applications.

In numerous situations, you can classify the object as a digital image, and for digital images, the measurements describe the output of each pixel in the image. Science is different for black and white images and colored images.

The intensity of each pixel serves as one measurement, in case of a black and white image. For the colored image, each pixel considered as providing 3 measurements which are intensities of 3 main color components i.e. RGB.

Speech Recognition

This basically, is the translation of spoken words into text. You may know it as “Automatic Speech Recognition”, “Computer Speech Recognition”, or “Speech to Text”.

A software application recognizes spoken words. Measurements in this Machine Learning application can be a set of numbers that represent the speech signal. We may segment the signal into portions containing distinct words or phonemes. In each segment, we can easily represent the speech signal by the intensities or energy in different time-frequency bands. Many of us unlock our devices using our speech passwords.

Patient Diagnosis

Machine Learning is capable of providing methods, techniques, and tools that can help in solving diagnostic and prognostic problems in a variety of medical domains.

It aids in the analysis of clinical parameters and of their combinations for prognosis. We also use it for Prediction of disease progression, extraction of medical knowledge for outcomes research, therapy planning and support, and for overall patient management.

Machine learning improves the accuracy of medical diagnosis by analyzing data of patients. It is used for data analysis, in medical science too, just like many other areas.

So, we can say that successful implementation of ML methods can help the integration of computer-based systems in the healthcare environment.

Learning Associations

It is the process of developing insights into hidden associations between products. It is baffling how seemingly irrelevant products may turn out to be associated with one another in any random context when analyzed in light of buying behaviors of customers.

This unique concept is also known as basket analysis. If a buyer buys X, would it lead to him buying Y eventually?

If you know the answer for sure, you would be able to devise a better marketing strategy. It would help you suggesting a better associate product to the customer.

However, we will need big data analytics to come upon these learning associations. This is where Machine Learning comes under the umbrella of data science.

In Data Science or Data Analytics, the two main aspects are data mining and machine learning.

  • In Data Mining, we identify patterns in large amounts of real-world data. It may include the techniques of artificial intelligence, machine learning, neural networks, and statistics.
  • Machine learning is an approach to developing artificial intelligence in devices to learn from analyzed data and improve themselves. We badly need machine learning to make sense of increasing data of the world.

Classification & Prediction

It is the process of placing each individual from the population under study in many classes. They may identify an individual as the independent variable.

Analysts can use measurements of an object to identify the category it belongs to. They use data to establish an efficient rule. Data consists of various examples of objects with their correct classification.

Let us elaborate with the help of an example. If a customer applies for a loan, bank assesses customers on his/her ability to repay the loan. Considering factors like the customer’s earning, age, savings, and financial history make it very easy. They retrieve information from the past data of the loan. Hence, Seeker uses to create a relationship between customer attributes and related risks.

The stage of classification starts making sense when we enter the stage of prediction.

Once again, think about the example of a bank computing the probability of any of loan applicants faulting the loan repayment. In order to compute the probability of the fault, the system will first need to classify the available data in certain groups. Set of rules devised by analysts, helps to describe this classification. When we are done with classification, we can compute the probability.

These predictions are possible because of the hottest machine learning algorithms.

Another relevant example would be GPS navigation services. During this, our current locations and velocities are being saved at a central server for managing traffic. This data then helps to build a map of current traffic.

Have you ever thought about how the cab booking apps, estimates the cost of the ride does and minimize the detours? Machine learning makes it possible.

In this article, we found out five ways we all, benefit from Machine Learning in daily life, without knowing it. There are many uses of Machine Learning in a number of fields. Some of the areas are Medical, Defense, Technology, Finance, Security, etc. It is an incredible breakthrough in the area of artificial intelligence.

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