What is Machine Learning? Explained

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What is Machine Learning

We all know how important is machine learning these days, but what is it, in reality, is the actual question. In this blog, we will understand what is machine learning along with some practical examples that will make your understanding crystal clear. Consider an example, you have some animals and you want to sort them into groups like cows and horses. But a human doing it might be very inefficient. Wouldn’t it be great if some machine would do it? Certainly! But for that, you will have to teach the machine how to differentiate between a cow and a horse. This is a very simple example of machine learning.

What is Machine Learning

There are some basic common theories, however, the best-explained connection is explained by Arthur Samuel way back in 1959:

“It is the field of study that provides computers with the power to find out without being explicitly programmed.”

A sheer example of machine learning according to Tom Mitchell.

“A computer virus is claimed to find out from its experience E regarding some task T and a few performance measure P, if its performance on T, as measured by P, improves with experience.”

Here comes the greatest question of all times:

What is the difference between AI and ML?

To answer this question, please go through the table provided below:

Among the various sorts of ML tasks, an important distinction is drawn between supervised and unsupervised learning:

• Supervised machine learning: The program is “trained” or in easier words taught on a pre-defined set of “training set of examples”, which then facilitate its ability to achieve an accurate conclusion when given new data.

• Unsupervised machine learning: The program is given a bunch of knowledge and must find patterns and relationships therein.
Let’s dive in deeper to gather more knowledge about these.

Supervised Machine Learning

In this case, the machine has a “supervisor” or a “teacher” who gives the machine all the answers, like whether it’s a cow in the picture or a horse. The teacher has already divided (labelled) the data into cows and horses, and the machine is using these examples to learn.

Supervised learning is when the model is getting trained on a labelled data-set. A labelled dataset is one that has both input and output parameters. In this type of learning both training and validation, datasets are labelled.

Training the system:

While training the model, data is typically split within the ratio of 80:20 i.e. Most of the data is used as training data and rest as testing data. In training data, we feed input also as output for 80% data. The model learns from training data only. We use different machine learning algorithms to build our model. By learning, it means the model will build some logic of its own. Once the model is prepared then it’s good to be tested. At the time of testing, the input is fed from the remaining 20% data which the model has never seen before, the model will predict some value and we will check it with the expected output, to calculate the accuracy.

Supervised learning classified into two categories of algorithms:

Classification: A classification problem is when the output variable may be a category, like “Girl” or “Boy” or “bird” and “no bird”.

Regression: A regression problem is when the output variable may be a real value, like “fruits” or “colors”.

Unsupervised Machine Learning

Unsupervised learning means the machine is left on its own with a pile of animal photos and a task to seek out out who’s who. Data isn’t labelled, there is no teacher, the machine is trying to seek out any patterns on its own.

Thus, the machine has no idea about the features of horses and cows so we can’t categorize it in horses and cows. But it can categorize them according to their similarities, patterns, and differences. One part may contain all pics having horses in it and the second part may contain all pics having cows in it. Here you didn’t learn anything before, which suggests no training data or examples.

Unsupervised learning classified into two categories of algorithms:

  • Clustering: A clustering problem is where you would like to get the inherent groupings within the data, like grouping customers by purchasing behavior.
  • Association: An association rule learning problem is where you would like to get rules that describe large portions of your data, like folks that buy X also tend to buy Y.

All right! But is there something in between? There surely is. And that is known as semi-supervised learning. Let’s have a glance at it then.

Just imagine if you want to train a machine in such a way that it should be able to distinguish between fruits and vegetables in a picture. If we go for supervised learning, that means loads and loads of hard work to build a data set that is best suited. Now you may pick unsupervised learning as your choice for this job, but just think if there is no proper training the machine may tell you that lemon is an orange!

Then what are our options? To get rid of these disadvantages, the foundation of Semi-Supervised Learning was laid. during this sort of learning, the algorithm is trained upon a mixture of labelled and unlabeled data. Typically, this mix will contain a really bit of labelled data and a great deal of unlabeled data. the essential procedure involved is that first, the programmer will cluster similar data using an unsupervised learning algorithm then use the prevailing labelled data to label the remainder of the unlabeled data. the standard use cases of such sort of algorithm have a standard property among them – The acquisition of unlabeled data is comparatively cheap while labeling the said data is extremely expensive.

A Semi-Supervised algorithm assumes the subsequent about the info –

  1. Continuity Assumption: The algorithm assumes that the points which are closer to every other are more likely to possess an equivalent output label.
  2. Cluster Assumption: the info is often divided into discrete clusters and points within the same cluster are more likely to share an output label.
  3. Manifold Assumption: the info lies approximately on a manifold of a way lower dimension than the input space. This assumption allows the utilization of distances and densities which are defined on a manifold.

One more sort of machine learning is reinforcement learning. That needless to say is extremely interesting and widely applied.

Reinforcement learning

It is learning by trial. If today I cause you to sit with a handicapped person, within the beginning, you’ll face issues in communication. But in some days, you’ll learn gestures which can enable you to carry a conversation thereupon person. that’s reinforcement learning within the simplest terms.

Technically reinforcement learning is that the training of machine learning models to form a sequence of selections. The agent learns to realize a goal in an uncertain, potentially complex environment. In reinforcement learning, AI faces a game-like situation, the PC employs trial and error to return up with an answer to the matter. To urge the machine to try to do what the programmer wants, AI gets either rewards or penalties for the actions it performs. Its goal is to maximize the entire reward.

Although the designer sets the reward policy–that is, the principles of the sport –he gives the model no hints or suggestions for a way to unravel the game. It depends on the model as to how it performs the task to maximize the reward, ranging from totally random trials and finishing with sophisticated tactics and superhuman skills. By leveraging the facility of search and lots of trials, reinforcement learning is currently the foremost effective thanks to hint the machine’s creativity. In contrast to the citizenry, AI usually feeds from thousands of parallel gameplays if a reinforcement learning algorithm is run on sufficiently powerful computer infrastructure. this, in turn, helps it make the right move at the right time

Machine Learning is an incredibly powerful tool. within the coming years, it definitely will extend its hands to assist solve a number of our most pressing problems, also as open up whole new worlds of opportunity for data science firms. A few years later machine learning will become one among the foremost integral parts of our lives!

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