Machine Learning is become the hottest technology in enterprise application development. Basically, machine learning is a subset of artificial intelligence (AI). That helps computers to learn based on data and experience. Machine learning is a huge discipline that has exploded over the past few years. It’s the process of applying complex algorithms to data to produce a result, in the past, machine learning was used for big data problems, and not much else. However, it’s now being used for a range of different applications, from websites and mobile apps to drones and car engines.
fastest-growing fields
Machine learning is one of the most exciting and fastest-growing fields of computer science. It has the potential to solve a lot of the problems that are currently out of our grasp. There are three main areas of machine learning: supervised, unsupervised, and Reinforcement learning.
PRODUCTS or ALGORITHMS
Before Machine Learning you have to define your goal which PRODUCTS or ALGORITHMS for machine learning and artificial intelligence (AI). if you selected the Product it means you don’t want to learn deeply the algorithms and heavy maths of machine learning. But to use already builtin packages or components to just create a product or to learn to build up your resume.
else if you want to learn algorithms you have to learn deep heavy maths to build complicated software to develop step-by-step procedures and algorithms. You can see various examples or implementations of machine learning around us, such as Tesla’s, self-driving car, Apple, Siri, Sofia, AI robot, and many more out there.
So what exactly is machine learning? Machine learning is a subfield of artificial intelligence that focuses on the design of a system that can learn from and make decisions and predictions based on the experience, which is data.
These programs are designed to learn and improve over time when exposed to new data. Let’s move on and discuss one of the biggest confusions of the people in the world.
They think that all three of them, AI, machine learning, and deep learning all the same. You know what? They’re wrong. Let me clarify things for you. Artificial intelligence (AI) is a broader concept of machines being able to carry out tasks in a smarter way.
It covers anything which enables the computer to behave like humans. Think of a famous Turing test to determine whether a computer is capable of thinking like a human being or not. If you are talking to city on your phone and you get an answer, you’re already very close to it. So this was about the artificial intelligence now coming to the machine learning part.
Machine Learning and Artificial Intelligence (AI) is the technology that allows computer applications to become intelligent and adapt to the data and actions that they are being fed. This is a very powerful technology because it means that we do not need to teach computers how to function; rather, we can simply feed computers data. And they can learn how to function independently. So this was about the machine learning part now coming over to deep learning.
Deep learning is a subset of machine learning. Similar machine learning algorithms are used to train deep neural networks to achieve better accuracy. In those cases where the former was not performing up to the mark.
Now let’s move on and sub-categorize machine learning into three different types supervised learning, unsupervised learning, and reinforcement learning.
Supervised Learning: Machine Learning
Supervised learning is the machine learning task of learning a function that maps an input to an output based on example input-output pairs. In other words, supervised learning involves training an algorithm with a set of example data. This is in contrast to unsupervised learning, where the algorithm learns from data with little or no guidance. In the case of classification, supervised learning is called classification, and in the case of regression, it is known as regression.
let’s see some of the popular use cases of Supervised Learning, we have Cortana or any other speech automation in your mobile phone trains using your voice. And once trained, it starts working based on the training. This is an application of Supervised Learning. Suppose you are saying okay, Google call someone (Jenny) or you say Hey Siri, call Jenny. You get an answer to it and the action is performed and automatically a call goes to Jenny.
Unsupervised Learning
Unsupervised learning is a machine learning task. Where the algorithms are not given any information about the inputs and their labels. The goal is to find hidden structures in the data. To discover the inherent patterns in the data and to find the underlying structure that governs the observed data. It is related to the field of semi-supervised learning. In which only some of the inputs in the training set are labeled.
It can be contrasted with supervised learning, in which the training data consists of both inputs and their corresponding outputs. Which does not rely on an external measure of performance. Unsupervised learning has applications in many fields. Such as natural language processing, computer vision, speech recognition, signal processing, data compression, and bioinformatics.
Reinforcement Learning
Reinforcement learning is an area of machine learning concerned with how software agents ought to take actions in an environment to maximize some notion of cumulative reward. In reinforcement learning, software agents are typically presented with a sequence of observations. And perform actions in response to these observations. These actions are sometimes random but usually are based on a set of “if-then” rules that the agent has learned from the environment.
At each point in time, the agent receives a “reward” based on the state of the environment, which might be positive or negative.
Conclusion.
AI is a topic that has been discussed for decades. But it has only recently become a reality. We’ve all seen movies where machines and robots are trying to destroy humanity. But the reality is that artificial intelligence and machine learning will only make our lives easier.
Artificial intelligence (AI) has been around for a very long time. Way back in the 1940s, we saw the emergence of the first truly intelligent machines. These machines were completely unaided by humans and were designed to mimic and even surpass the human brain. These machines were so intelligent they could learn and recognize objects, sounds, and voices, and even started teaching themselves. Artificial intelligence (AI) is a machine’s ability to think and act intelligently. It is the science of making machines behave in a way that mirrors human intelligence. Artificial intelligence is not just a science fiction concept anymore. It is a crucial part of the future. Exactly how artificial intelligence will impact our world is uncertain, but there is no doubt that it will have a significant effect.
