How does machine learning work?

We hear about machine learning these days and we are probably using it through our smartphone. Machine learning and Artificial intelligence are making strides all over the world. But, what is machine learning and how it works? we will explain here in detail.

Tensor flow machine learning platform
Tensor flow machine learning platform

Machine learning is a new way to teach computers to learn and make decisions on learning. Its like teaching computers to take decisions on large datasets. It is just like how humans learn and take decisions. Hence, computers can predict more accurately over time and keep on improving the accuracy based on fresh new data.

What is machine learning?

Machine learning is basically teaching computers how to learn things, and take actions and decisions on their own with the help of a large amount of data.  Its actually studying algorithms and statical models that help computers to improve their performance over time on any given task.

The machine learning algorithms are built on the sample data. These algorithms then work on the larger amount of data related to the specific task. So, instead of programming on a specific task, let the computer program themselves and improve accuracy on their own.

The most important advantage of this type of learning is computers learn on the vast amount of data quickly while in manual programming it will take years to take into account that kind of data. As a result, computers can accurately predict and make decisions faster.

What are the different types of machine learning algorithms?

There are different types of algorithms and the major difference is the approach they take to solve a specific type of task.

Supervised learning: In this type of learning, algorithms are fed with a large amount of labeled data so much so that eventually they are able to associate what it is based on the label and its shape. This type of learning involves large data sets with proper labels or annotations. It involves manual labeling and hence, it is a tedious job.

Supervised learning also yields accuracy and have huge benefits in image recognition systems. The supervised learning has some limitations as unlabelled data cannot be analyzed and hence, ignoring that data is not a good option.

Unsupervised learning: The unsupervised learning works on the exact opposite way. Here, algorithms are designed to find patterns in data and categorized it. These systems can spot the similarities or differences. For example, Google News is sending you emails on your most favorite topic. Here, Google just finds a pattern in data and sends you what you like.

Semisupervised learning: In this type of learning both of the above-mentioned techniques are utilized. At first, a small amount of labeled data is fed to algorithms and then on the basis of that data that algorithms label the unlabeled data. Furthermore, the resulting model then gets trained in both types of data sets to achieve a high level of accuracy. This type of learning is most popular as computing resources can be optimized to gain accurate results instead of wasting them on large unlabeled data sets.

The reinforcement learning

Reinforcement learning is a very different type of learning. In this type, the algorithms are trained like how we did in our childhood to walk, run and play football. It’s like how humans learn to do something. At first, we are nowhere and then eventually we look into actions and then make some relationships in action. This finally ends in working out the final action.

Furthermore, after learning and doing the same action over time we are able to do that action in the best possible way. In the same way, the reinforcement learning works.

The neural networks

The neural networks are a key part in all of these algorithms. These networks work like our brain. These networks are nothing but interconnected layers of algorithms each work on specific function. They work together to solve a specific problem. The output of one layer of the algorithm is actually the input of consequent layer of the algorithm. Hence at some point, the whole neural network can point out the solution to the entire problem.

Advantages of Machine learning

Machine learning helps to solve critical problems very easily as laborious part of coding an entire program can simply be avoided. The machine can understand better and give predictive results. We can achieve the highest level in natural language processing. Furthermore, machines can give highly accurate results than ever imagined. So it’s like a win-win situation for both the machine and humans.

The recent examples are self-driving cars, voice assistants, automated delivery, and more recently virtual assistant taking an appointment at the saloon. So, the applications are undoubtedly much more.

Watch Machine learning in action:

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