A GENTLE INTRODUCTION TO MACHINE LEARNING CONCEPTS

 

Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Machine learning focuses on the development of computer programs that can access data and use it to learn for themselves.

The process of learning begins with observations or data, such as examples, direct experience, or instruction, in order to look for patterns in data and make better decisions in the future based on the examples that we provide. The primary aim is to allow the computers learn automatically without human intervention or assistance and adjust actions accordingly.

Some Machine Learning Methods

Machine learning algorithms are often categorized as supervised or unsupervised. Let's see them in brief with some illustrations.

Supervised Learning

As the name indicates, supervised learning involves machine learning algorithms that learn under the presence of a supervisor. Learning under supervision directly translates to being under guidance and learning from an entity that is in charge of providing feedback through this process.

 When training a machine, supervised learning refers to a category of methods in which we teach or train a machine learning algorithm using data, while guiding the algorithm model with labels associated with the data. 

Example: Is it a cat or a dog?

Image classification is a popular problem in the computer vision field. Here, the goal is to predict what class an image belongs to. In this set of problems, we are interested in finding the class label of an image. More precisely: is the image of a car or a plane? A cat or a dog?

Unsupervised Learning

In supervised learning, the main idea is to learn under supervision, where the supervision signal is named as target value or label. In unsupervised learning, we lack this kind of signal. Therefore, we need to find our way without any supervision or guidance. This simply means that we are alone and need to figure out what is what by ourselves. 

Imagine you are in a foreign country and you are visiting a food market, for example. You see a stall selling a fruit that you cannot identify. You don’t know the name of this fruit. However, you have your observations to rely on, and you can use these as a reference. In this case, you can easily the fruit apart from nearby vegetables or other food by identifying its various features like its shape, color, or size.

Example: Finding customer segments in marketing data 

Clustering is commonly used for determining customer segments in marketing data. Being able to determine different segments of customers helps marketing teams approach these customer segments in unique ways. (Think of features like gender, location, age, education, income bracket, and so on.)

 

Reinforcement Learning

Reinforcement Learning is defined as a Machine Learning method that is concerned with how software agents should take actions in an environment. Reinforcement Learning is a part of the deep learning method that helps you to maximize some portion of the cumulative reward

Example:Training your Dog in a house environment .

Consider the scenario of teaching new tricks to your cat

• As cat doesn't understand English or any other human language, we can't tell her directly what to do. Instead, we follow a different strategy.

• We emulate a situation, and the cat tries to respond in many different ways. If the cat's response is the desired way, we will give her fish.

• Now whenever the cat is exposed to the same situation, the cat executes a similar action with even more enthusiastically in expectation of getting more reward(food).

• That's like learning that cat gets from "what to do" from positive experiences.

• At the same time, the cat also learns what not do when faced with negative experiences.

 

The curiosity and buzz around the most talked-about technology “Artificial Intelligence” , have experts and technophiles busy decoding its exciting future applications. Of course, the use of AI and machine learning is already pervasive in our daily lives, as we see AI in our day to day life . 

 

 

“AI is likely to be either the best or worst thing to happen to humanity”

- Stephen Hawking