Machine Learning is the subset of Artificial Intelligence (AI) which makes devices capable of gaining knowledge from the experiences and upgrade themselves without any extra coding.
The term Machine Learning was framed by Arthur Samuel in 1959. He was then exploring Artificial Intelligence and computer gaming and he also defined Machine Learning as, “Field of study that gives computers the capability to learn without being explicitly programmed.”
As discussed earlier, Machine Learning is a subset of Artificial Intelligence, so it basically focuses on making machines capable to behave like a human and learn and develop their own programs. Large amount of data is given into the machines and various algorithms are used to create ML models to train other machines on the provided data. The algorithm selected for the building the model depends on the quality of data fed into the machine and the process to be automated.
The data used can contain numbers, words, images, videos, and many more; all these data can be stored digitally, so it can easily be fed into a ML algorithm. There are many applications, like Netflix, Google, Facebook, Instagram, Alexa, etc. that we use on the daily basis that have large quantity of data of millions of users and yet work smoothly.
WHY MACHINE LEARNING IS IMPORTANT ?
We all know, that Machine Learning helps in automating several tasks, mostly that humans can do using their inbuilt intelligence. And their intelligence can be used to make machines as intelligent as them, and use them to perform our tasks in an efficient manner. Everything around us will be automated and will be saving our time and effort to perform the actions and tasks. There are some points that we should consider while learning ML:
ML helps to increase efficiency
Let’s consider an example of online grocery store. How convenient it is to go on their app or website and buy products rather than going to a nearby grocery store, standing and waiting for your turn? Isn’t it easy to go and buy all the needed products in just one click? Yes, it is and it’s obvious that most of us would go for the online store as it saves our time and effort as well. We can not only select products of our choice but also can save them for buying later, pay for them right at the moment.
All these actions can be performed due to the concept of Machine Learning. ML has made these actions possible run in an efficient manner.
Helps detecting frauds
In the world where everyone is running fast with technology, most of the payments done are online. Even we’re also engaged in it, but we must be careful of the frauds that are increasing day by day. Machine Learning algorithms helps us in detecting frauds and malicious actions and stops the transaction and actions performed before it reaches its ending point.
Has better career opportunities
A TMR report said that Machine Learning as a Service (MLaaS) is expected to grow from $1.07 billion in 2016 to $19.9 billion by the end of 2025.
As most of the industries are opting to apply AI, so having knowledge about ML would be very beneficial for the job seekers and IT professionals. Also, ML has applications in various sectors like, cyber security, image recognition, medicines, face recognition and many more.
Beneficial for Engineers
The engineers who are working on ML technology have very high salary. According to SimplyHired.com, a ML engineer has an average salary of $142,000, while an average ML engineer can earn up to $196,000.
Linked to Data Science
Machine Learning is emerging as the shadow of data science. Going for machine learning can give you two options for your career, first one for the machine learning engineer job and second one for the data scientist job. A machine learning engineer and a data scientist, if work together, then can create a better synchronization of the data and work products.
WHAT ARE THE TYPES OF MACHINE LEARNING ?
We’ve four different types of machine learning:
- Supervised Learning
- Unsupervised Learning
- Semi-Supervised Learning
Now, we’ll learn about all the above mentioned types in detail.
In this type of machine learning, the data is already labelled so that the machine can exactly see the pattern it should look for. It helps various organizations to solve variety of real-world problems. It has a set of input variables (x), and an output variable (y). Supervised learning algorithm identifies the function that establishes the relationship between x and y. the relationship is y=f(x). Supervised Learning can be grouped as:
- Regression problems: Such type of algorithms are used to forecast about the future values on the basis of previous data. E.g., prediction of diseases due to existing health issues.
- Classification problems: In this, labels are used to train the algorithm to identify items based on their categories. E.g., students of class 11 or 12.
In this type of machine learning, the data is not labelled. The machine itself finds the patterns based on its knowledge. In this, only the input values are known by the machine and the output value/s is/are unknown. The main objective of such type of ML algorithm is to understand the patterns lying within the data and get the output. The unsupervised learning can further be categorized into:
- Clustering: In this, the input data having similar characteristics are grouped together. E.g., grouping of animals as herbivores and carnivores.
- Association: This creates associations that are related to the existing input data. E.g., customers buying smartphones(X) would also prefer headphones(Y).
This type of machine learning was introduced to cover-up the disadvantages of supervised and unsupervised learning. In this type of machine learning, the algorithm is trained with the combination of labelled and unlabelled data, in which there is a large amount of unlabelled data and a small amount of labelled data. Initially, similar data are clustered using unsupervised learning algorithm and then labelled data are used to label the remaining unlabelled data. When all the data are labelled, supervised learning algorithm is used to solve the problem.
In such type of machine learning algorithm, the models are trained to make decisions based on the experience. It is very different from the supervised and unsupervised learning as the outputs or actions are already feed in the machines based on the particular input values while in reinforcement learning the machines or models themselves decide the action to be performed or the output to be given on the basis of feedbacks and rewards.
APPLICATIONS OF MACHINE LEARNING
Facial Recognition or Image Recognition
It is the most common application of machine learning. In this, we generally identify or detect persons, objects, places or images. The face recognition is used at various places, like in school or offices for marking the attendance, identifying criminals, security purposes etc.
Most of the voice assistants that we use in our daily lives are based on the concept of machine learning. Whether it be Google Assistant, Alexa, Siri, or Cortana; all of these work of ML. In the process of speech recognition, the voice instructions are converted into text and then the task is performed. Various speech prototypes are already fed into the machine and based on that these voice assistants give us the result.
Amazon, YouTube, Netflix, Spotify, Flipkart; all such e-commerce websites and entertainment websites use machine learning to give their users recommendations based on the recent searches they’ve made. Web-series, songs, products, movies, etc. whatever you see and search for, later the websites automatically suggest us, and this is just possible for them to do so via machine learning.
In fact, various business organizations have also implied this technique to improve their business strategies.
It is the most important application of machine learning. It has helped the medical sector in the diagnosis of diseases and the ailments, which are difficult to detect through other means. With the help of machine learning, the 3D models of the patient’s body so that it can be understood in a better way and thus the medication and treatment can be done efficiently.
It is the most amazing and wonderful application of machine learning. The most popular car manufacturing company, Tesla is working on the self-driving car. The unsupervised machine learning is used to train the car models to perform several actions while driving.
So far, we’ve got the basic introduction of machine learning. We’ve understood the history of ML and what it basically helps us with. Also, why do we need to learn about machine learning. All the factors mentioned to define the importance of ML are sufficient enough to have our eyes on machine learning. Also, there’re different types of ML which we’ve learnt about. All we need to focus is that ML is the evolving technology in today’s world and in order to have pace with the world, we need to learn all the advanced and latest technologies. Its applications are really amazing that may get more things and activities facilitated in the coming future.