Hello Everyone! I'm sure you all must have heard the name Machine Learning in day to day life. Through this blog I'll share why I chose Machine Learning and what are it's applications.
What is Machine Learning?
Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Machine learning focuses on the development of computer programs that can access data and use it learn for themselves.
Where is it used?
This image depicts some real life examples where Machine learning is used. Right from something as basic as Weather Prediction to something as complex as predicting cancer cells Machine Learning is present everywhere.
Even though the scope of Machine Learning has grown exponentially large over the years I'll introduce you to some areas where it is used and how it used.
- Prediction/Classification related tasks.
Let's say a developer collected or got access to a database/dataset which records all the details about earthquakes that have happened over a time like frequency,impact, no of buildings affected,no of people affected,terrain of the place,climate of the place etc.
With help of Machine Learning you can use this real time dataset to study various features of the report with help of codes, understand the pattern,visualizing it for people with non technical background and then building a model to predict when an earthquake can happen based on previous dates,what magnitude can cause what impact and how many people it can impact.
- Robotics
Yes,you read it right. If creating robots is the work of hardware developers/robotics engineers then writing the codes to make it work like humans and take decisions like us is done through Machine Learning. To make it learn from its mistakes and provide the functionality to make these robots learn like us an advanced field of Machine Learning called Deep Learning is used.
- Computer Vision
Suppose you saw this lady on a news channel or in case you already know her you need to search the dress she wore in Grammy's you can do it with an advanced field of Machine Learning called Computer Vision which takes images as data,analyzes them and then makes predictions on the data on the basis of previously trained/stored data. A popular example of this is Google lens and search by a specific image. This is how it works and makes predictions:
- Augmented Reality
Remember when Snapchat was introduced and we had the feature to make a Bitmoji,i.e.,an animated image of us but close to our facial features and customisable to our liking. That feature is what we call as AR. However,today AR has grown far advanced and is even used in Gaming,security,education and even unboxing or looking how a particular product from a particular company will look like at your home or in your hands.
- Suggestion/Clustering
I would like to explain this taking an example of a reader(novel reader to be specific). Suppose a book selling company wants to suggest books to its users on the basis of their interests and search history. This take can be done through Machine Learning by clustering the users into specific groups and then suggesting them the results.
- Virtual Reality(V.R.)
Remember when gears like this were cool and everyone wanted to have one?
This in itself used advanced level Machine Learning and Computer Vision.
Data is prepared and then it is trained and tested in real world settings to give the end user an experience of actually living in that moment/reality. It's still popular and used for gaming and streaming.
To sum it up, Machine Learning is used in almost any field that you can imagine. This article was just meant to make you familiar with it and why I chose this field over everything with the help of real life examples. In the upcoming articles, we'll gradually see things in detail and with codes too. We'll start with a Machine Learning environment setup and then we''ll have a look at various libraries used for ML and what functions each library provides.
Thankyou for reading. You all are awesome. Feedback and suggestions are welcome.