The subject of data science has made great progress in recent years, and machine learning has grown immensely in popularity. An area of artificial intelligence (AI) known as machine learning (ML) enables a system to automatically learn from data and get better over time without having to be explicitly programmed to do so.
Machine learning entails utilising algorithm(s) to train computers on data that is already accessible in order to build a model. To produce predictions, this model is used with data that has never been seen before.
Many of you may not be aware, but ML is silently having a big impact on how we live our daily lives, making it easier to do so.
Here are the five ways that machine learning has impacted daily life.
Spam Email Detection
Nowadays, having an email address is not a big deal. Every time we log in to our email account, a sidebar folder called “spam” is available that houses all the unsolicited communications. Additionally, none of the emails in the spam folder were manually deleted from the user’s inbox. ML is used to carry out this automatically.
Spam-filtering uses a variety of algorithms, the most prominent ones being Naive Bayes, K-Nearest Neighbours, and Random Forest. The training set for these algorithms consists of emails that have successfully been pre-classified as spam or not.
They are then trained on this dataset, and the resulting model is used to the fresh batch of incoming emails to determine whether they are spam or not. Utilising ML in a very straightforward but efficient manner. One of the numerous capabilities of machine learning is the ability to identify fraudulent emails.
Most of us turn to search engines like Google, Yahoo, Bing, etc. as our first option if we need a solution to a question. We input our search term, hit enter, and are then presented with a variety of links that are pertinent to our original query. How do these search engines learn about our needs, though? Once more, the power of ML is being used for this.
Search engines may interpret the meaning of a search query using techniques like Natural Language Processing (NLP), Deep Learning, and Tensorflow, and they make sure that the most relevant and high-quality results are displayed at the top. The machine learning model used by Google for predicting queries is called RankBrain.
The majority of us are experts at using mobile apps like Uber, Ola, and others to book cabs for our trips. These applications all make use of ML. When you order a cab, the machine learning (ML) algorithms ensure that the driver will be close to your position or, in the case of carpooling, will be travelling on the same route as your destination.
Additionally, ML is used to show you the quickest route depending on traffic congestion, ongoing construction, or any other potential roadblocks that might appear as you approach your destination.
We now have access to a wide range of entertainment resources because of the invention of the internet and the quick improvements that have been achieved in this industry since then. While services like YouTube, Netflix, and Amazon Prime are the best for streaming movies and TV shows online, audio streaming apps like Spotify offer high-quality, real-time access to your favourite music.
And the enjoyment isn’t only limited to entertainment because there are many platforms available that let you carry out a variety of tasks like ordering food, groceries, or other items for your home from anywhere in the world.
Detection of False Transactions
Every day, millions of transactions are conducted online. There is always a potential that some of them will be fake. The staff won’t be able to manually review every transaction and identify the ones that stand out as suspicious. In addition to potentially causing the clients enormous financial losses, it would also result in a significant loss of potential income for the banks, and customers would turn away from them.
Here, ML serves as the bank’s and your joint saviour. To determine whether a fraudulent transaction is being performed, ML algorithms use the customer’s location data and the device being used for the transaction. If a fraudulent transaction is detected, a warning message is sent to the customer through message, email, or even both. It’s fair to argue that many online businesses wouldn’t exist without machine learning.