Machine Learning

What is Machine Learning?

Machine Learning, or ML, is the science that enables computers to function without being programmed explicitly. It is because of Machine Learning algorithm that we are able to see significant advancements in Artificial Intelligence in the present times.

Artificial Intelligence, in a broad sense, can be referred to as machines that can learn, reason and act for themselves.

Machine Learning deals with the development of computer programs capable of accessing data and use it to learn for themselves. The primary objective of ML is to allow computers to learn on their own without any human intervention.

Real-world examples of Machine Learning

In an age where technology is omnipresent, most of us are familiar with names, including Netflix, Google, Amazon, Facebook, and Twitter. One must have noticed that when one runs a search on any one of these platforms, related searches and recommendations are also displayed.

Let us take the example of YouTube. Suppose you logged in to YouTube and searched for food-related blogs. Now when you log in the next time, you will notice that YouTube shows related videos related to food blogging. Now, the question arises how does this happen?

The answer is simple; it is because of Machine Learning.

In the applications mentioned above, each platform collects as much data as possible based on your taste, preference, the choice of link. Assessing the data, the application guesses about what you would want to search next.

How does a machine learn?

There are different ways in which a machine learns. These include supervised, unsupervised, and reinforcement learning.

Supervised Learning:

Supervised learning is a Machine Learning method, in which the models are trained based on the labelled data.

As the name suggests, Supervised learning requires supervision to train the model.

Y = f(X)

In the above function, X is the independent variable, while Y is a dependent variable. Whatever we input as variable X, we get the respective output as Y.

For example, we have two vegetables, and the supervised learning model is expected to identify each vegetable and classify them. We provide the input data as well as the respective output. It means that we would train the model based on features including shape, color, and size, among others.

Let us consider two vegetable: potato and okra. Potato has an uneven shape and has a brown color skin while okra is slim and green in color. Data related to each vegetable is provided as input. Here the name of the vegetable is “label” and size, and the color is the “features”.

Once the training is complete, the model is tested by providing a set of new veggies. The machine then recognizes the vegetables.

Unsupervised learning:

Unsupervised learning refers to learning with unlabeled data, also known as unsupervised data. In this, the system can infer a function to describe the hidden structure from unlabeled data. The system looks for data and derives a conclusion from dataset to describe the hidden structure from the unlabeled dataset.

Let us understand this using an example:

Suppose we have some bank data to detect fraudulent activity in a transaction and flag any fraud detection. In this case, the suspicious transactions are not defined. Hence there are no labels of “fraud” and “not a fraud”. The model attempts to find outliers by looking at abnormal transactions and signals them as a fraud.

Reinforcement Learning:

Reinforcement Learning is reward-based learning. This means that the model works on the principle of feedback. Let us consider an example where you provide the system with an image of a mango. However, the output provided by the system was the image of an apple, which is incorrect. You then give feedback to the machine that the image is of a mango. Next, in case a similar image comes, the system is able to identify the image as that of a mango quickly.

How does Machine Learning work?

Machine Learning drives inspiration from how the human brain functions. Just like humans learn from experience, the machine also learns similarly. The more you know, the better it is easy to predict.

The main objective of Machine Learning is to learn and provide inference. The learning process is done through the discovery of the pattern, possible through the massive data available. An essential step in this process is choosing and providing relevant data to the machine. The machine then filters the list of attributes required to solve the problem, known as the feature vector. The device uses some algorithm to simplify the data and then prepares a model.

Application of Machine Learning:

Below are some of the fields where we could see the application of Machine Learning:

• Self-driving cars
• Product recommendation
• Traffic Prediction
• Speech Recognition
• Online Fraud Detection
• Virtual Assistance
• Image Recognition
• Stock market trading
• Medical diagnosis
• Automatic language translations
• Filtering of spam data from mail account.

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Top Five Programming Languages for Machine Learning:

• Python
• Java
• C++
• R
• JavaScript

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