Understand Machine Learning - Definition | Types | Applications

Table of Contents [show]

Introduction

Machine learning is a fascinating new area of study that is steadily reshaping daily life. Nowadays. everything uses ML, from targeted marketing to even spotting cancer cells. However, this is only an introduction to ML. The real-world ML models are more complex than a simple definition. Still, there are many examples of how powerful it can be. The below blog will clear your doubts about ML including its applications and uses. 

Introduction to Machine Learning

The concept of ML helps computers to replicate human decision-making without explicit programming. Thus, ML allows systems to automatically learn from examples and experiences. 

It is an area of artificial intelligence where algorithms and statistics serve to learn from data and find undiscovered patterns. The final goal of machine learning is to create algorithms that help a system gather data. Furthermore, it uses that data to learn more. Thus, systems must search for patterns in the collected data and use those patterns to make critical decisions on their own.

Furthermore, ML models can perform tasks in the actual world that are currently accessible. 

Main Components of Machine Learning Algorithm

ML contains several algorithms that can be put into various categories. These algorithms are divided according to how they learn or the type of problem they attempt to answer. But the following key components are present in every ML algorithm:

1. Training Data 

It describes the data that the ML system needs to learn from. It can be text, photos, videos, or time series. Moreover, training data sets is one of the crucial machine learning basics that a beginner should know.

2. Representation

It implies the objects in the training data that have been represented in an encoded way. For example, a face that has features like a "nose" to represent it. Additionally, the choice of the model determines which models can be encoded more easily than others. 

3. Evaluation

It affects the assessment of or choice of one model over another. It is also referred to as a scoring function, utility function, or loss function. 

4. Optimization

This explains how we explore the set of models or improve the labels in the training set. Moreover, the model parameters must be optimised to minimise the loss function's value. It also enables the model's accuracy to rise faster.

Applications of ML

Below are some popular real-world applications of machine learning:

1. Speech Recognition

Speech recognition, often known as "Speech to text" or "Computer speech recognition," is the process of turning what is said into text. Additionally, speech recognition applications currently use Ml algorithms widely. 

2. Image Recognition

One of the popular machine learning examples is its application in image recognition. It identifies things like people, places, things, and digital photographs. Moreover, automatic buddy tagging suggestion is also a common application of face and image recognition.

3. Online Fraud Detection

ML improves the security and safety of our online transactions by detecting fraudulent transactions. When we conduct an online transaction, a scam may occur in several ways. It can be through the use of fictitious accounts and identification documents or the theft of money in the middle of a transaction. Thus Feed Forward Neural Network assists us in identifying this by detecting if the transaction is genuine or fake.

4. Automatic Language Translation

These days, it is not a problem if we go to a new location and do not speak the native tongue. It is because ML translates the text into the languages we know and help us. 

5. Stock Market Trading 

A lot of trading on the stock market uses machine learning. We are ot sure when the share prices will go up and down. However, a long short-term memory neural network in ML serves to predict stock market patterns.

Uses of Machine Learning in Everyday Life

1. Using Social Media (Facebook)

Automatic Friend Tagging Suggestions on Facebook or any other social media platform are among the popular examples of ML. Facebook uses facial detection and image recognition to automatically identify faces that match its database. It then suggests that we tag each of them using DeepFace.

2. Transport and Travel (Uber)

If you have ever used an app to get a cab, you have already used Ml to some level. It gives a unique application that is special to you. Thus, using your history and patterns it detects your location and offers options to travel to a regular area.

3. Products Recommendations

Consider checking out a product online without immediately buying it. But the following day an ad for the same thing pops up. What causes this, then? This happens because Google records your search history and makes advertising suggestions using that data. This is one of the most creative uses of ML.

4. Google Translate

In the olden days, when you visit a new location you have difficulties getting in touch with people or navigating. This is because everything was printed in a different language. But, now Google's GNMT (Google Neural Machine Translation) uses Natural Language Processing and thousands of languages. Additionally, it has several dictionaries to give the most accurate translation.

5. (Maps) Traffic Alerts

Nowadays, Google Maps is the tool we use if we need help with directions or traffic. Everyone who uses Google Maps gives Google their location, average speed, and the route they are taking. Thus, it helps Google gather a ton of data about the traffic and the map modifies your route accordingly.

Conclusion

Machine learning is currently a trending topic in technology, and for good reason. It represents a significant advancement in the capacity of computers to learn. Thus, ML computers can automatically learn from their experiences and get better at doing so. Moreover, we already use ML in our daily lives without even recognizing it. It can be through services like Google Maps, Google Assistant, Alexa, YouTube, Netflix, or Amazon.