what is machine learning?
As you know we are living in a world of humans and machines humans have been evolving and learning from the past experience since millions of years. On the other hand, the era of machines and robots have just begun in today's world.
These machines are the rewards are like they need to be program before they actually follow your instructions but what if the machine started to learn on their own, and this is where machine learning comes into Picture, machine learning is the core of many futuristic technological advancement in our world today. You can see various examples or implementation of machine learning around us such as Tesla's self-driving car, Apple Siri, Sophia's AI robot, many more are there.
So, what exactly is machine learning?
Well,
Machine learning is a subfield of
artificial intelligence that focuses on the design of system that can learn from. Make decisions and predictions based on the experience which is data in the case of machines, machine learning enables computers to act and make data-driven decisions rather than being explicitly programmed to carry out a certain task. 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 are all the same. You know, what they are wrong. Let me clarify things for you.
Artificial intelligence 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 Siri on your phone and you get an answer, you're already very close to it. So this was about artificial intelligence.
Now, coming to the machine learning part. So as I already said, machine learning is a subset or a Didn't application of AI. It is based on the idea that we should be able to give machines access to data and let them learn from done cells. It's a subset of artificial intelligence that deals with the extraction of patterns from data sets.
This means that the machine can not only find the rules for optimal Behavior but also can adapt to the changes in the world. Many of the algorithms involved have been known for decades. Even thanks to the advances in computer science and parallel Computing. They can now scale up to massive Data volumes. So this was about the machine learning part.
Now, coming over to deep learning, deep learning is a subset of machine learning, where it is similar to machine learning. Algorithms are used to train deep neural networks. So as to achieve better accuracy, in those cases, where the former was not performing up to the mark, right. I hope now you understood that machine learning, AI and Deep Learning.
All three are different. Okay, moving on ahead.
Let's see in general how machine learning works. One of the approaches is where the Learning algorithm is trained using a label or unlabeled training data set to produce a model. New input data is introduced to the machine learning algorithm and it makes predictions based on the model, the prediction is evaluated for accuracy. And if the accuracy is acceptable, the machine learning algorithm is deployed.
Now, if the accuracy is not acceptable, the machine learning algorithm is trained again and again, with an argument, a training data set, this was just a high-level example, as there are many more factors and other steps involved in it. Now, let's move on and subcategorize.
The Machine learning into three different types, supervised learning, unsupervised learning and reinforcement learning. And let's see what each of them are, how they work, and how each of them is used in the field of banking, Healthcare, retail, and other domains
What Is Supervised Learning?
What Is Unsupervised Learning?
What Is Reinforcement Learning?
,
