Machine learning is one of the trending words in the technology field. Machine learning helps to learn and analyze a huge amount of data and to learn the patterns in it and help to make decisions based on the data called data-driven decisions.
We are using machine-learning applications in our daily life without knowing we are using this technology. Machine learning is one of the most growing technologies in our modern world; it will be the next revolution in the technology world.
Before learning Machine learning we need to know where this machine learning is used in the practical world. In this tutorial, we are going to familiarize some of the famous and daily used applications that work with machine learning.
Social media like Facebook, Twitter, etc are using machine learning for data analysis and predictions. We have used friend suggestion, similar videos, recommended pages and groups are all doing machine learning internally for their work. Facebook continuously checks your likes, visited pages, groups, and videos for analyzing the data using ML algorithms and using the predictions for getting the recommended videos, groups, pages, friend suggestions.
Second, Facebook does face recognition. Have you ever think when you upload a photo, how Facebook instantly identify the friends in it and give you a tag option. It involves a lot of backend functions but overall it is an application of machine learning.
Facebook is using the technology called Deep Face that analyzing the data of the photo and identifies the similar patterns in it and compares that with the photos in your friend list, which involves a complex process as we said earlier; it is an application of Machine learning.
Today mostly, we are using speech commands like voice search in Google that is an application of machine learning. The applications like Google speech, Siri, Alexa, etc, which come under speech recognition, are using machine learning for speech processing.
When we speak the system, translate the speech into text that is commonly called speech recognition. Now we are using machine-learning algorithms to analyze and learn the data and take decisions.
Nowadays, when we want to go to a place first thing we do, is to take Google maps to get the route and traffic conditions. Google maps predict the traffic and it gives the shortest time path to the destination.
Did you ever think of how Google map predicts traffic? It uses two inputs to analyze the traffic, which is the real-time location of a vehicle and the average time it takes in the past days to reach a location.
Data is processed, analyze, and learn from the data some patterns in it. Then predicts the output, which is clearly an application of machine learning.
Have you ever think how an email is filtering automatically to our inbox or spam folder. We are always getting only spams in spam folders and important emails as separate headings in inboxes. It is an application of machine learning.
How Gmail is doing the spam filtering?
Machine learning algorithms are used for malware detection also.
Online transactions are using machine learning for safe operation. We all are doing online transactions every day and there are many frauds, which are trying to play fraud online aiming our transactions such as fake ids, fake accounts, fake websites, and can able to steal our money in between the transaction.
The machine learning algorithm is helping us to protect our transactions. For every transaction, the output is converted to some hash values. In genuine transactions, this value will be a specific pattern and in fraud transactions, there will not be a specific pattern. Therefore, machine learning can easily understand the frauds and take necessary actions.
We use machine learning for pattern recognition and prediction. This applicability of machine learning is used in weather forecast and stock prediction.
Machine learning algorithms can analyze and learn from a huge amount of data and can predict accurate and efficient results which will be used in both applications.
Healthcare contains various problems, including high overhead costs, maintaining privacy and security, streamlining recordkeeping, and improving patient outcomes. Health informatics uses information technology to improve patient outcomes and optimize management within the healthcare space.
Applying machine learning to diagnostics technology is an emerging and exciting space. In a 2017 paper, Esteva et al. trained a deep neural network using 130,000 skin disease images that performed as accurately as 21 board-certified dermatologists. Additional FDA-approved AI-based medical devices are beginning to emerge for other diseases
If you have ever interacted with Amazon's Alexa or Google's Nest Smart Speakers, you have interacted with a chatbot. A chatbot is a program that simulates and interprets human language. This allows humans to interact with their electronic devices as if they were talking to a human. This technology has been growing due to advances in natural language processing, a subfield in artificial intelligence that allows a computer to understand human languages.
Chatbots are being built on understanding both text and speech. Recently, they have been replacing customer service personnel when it comes to addressing Frequently Asked Questions or for tasks that are not complex.
The upcoming technology uses machine-learning algorithms for working. For example, the Tesla Company uses machine-learning algorithms to analyze and learns the data. Using such data prediction it can recognize the persons, objects, cars, and easiest route for the destination.
We all are using e-commerce websites like Amazon, Flipkart, and video websites like Netflix, etc in our daily life. Machine learning is playing a good role in these websites. Have you ever wondering the recommended product choice for us from these websites. It uses machine-learning algorithms to analyze our shopping patterns and searching data and predict our recommended products.
This method is used in advertisements also. When we search for something in Google or other search engines they also use machine learning algorithms to show us related ads.
If you are going to create a solution using any technology, we need to know the problem. To get to see the problem, here are some questions to help you think:
Once you have a good understanding of the problem, not only do we need to know who our customers are, but we also need to identify our competitors. Here are some questions to ponder over:
Now it's time to develop a database and machine learning solution for your problem. This part has a variable time frame, but it will be worth it.