“A baby learns to crawl, walk and then run. We are in the crawling stage when it comes to applying machine learning.” ~Dave Waters

We are living in a world full of Humans and Machines. We the humans are learning and evolving from our past experiences for billions of years, on the other hand, the era of machines and robots has just begun.

In today’s world, these machines or robots need to be instructed to perform, but what if machines started to learn on their own and this is where machine learning comes into the picture.

In this blog, we will see the intuition behind Machine Learning and types of Machine Learning in detail.

So, let’s start with the formal understanding of Machine Learning.

What is Machine Learning?

  • Machine learning is a subset of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed.

How does Machine Learning work?

Traditionally, humans have analyzed data and adapted their culture from their past data. However, as the volume of data exceeds the ability of the human approach, it becomes necessary to quickly and automatically produce models that can analyze bigger, more complex data and deliver faster, more accurate results even on a very large scale.

For the process of learning, we need to have available some observations or data (samples) in order to explore potential underlying patterns, hidden in our data. These learned patterns are nothing more than some functions or decision boundaries.

Difference between Conventional programming and Machine Learning

  • In conventional programming, we feed data and programs to the computer and get the desired output.

Types of Machine Learning

Machine Learning is divided into three broad categories. They are:

  • Supervised Learning

Let’s see them one by one.

Supervised Learning

  • In supervised learning, we train our model on a labelled dataset which means we have both raw input data as well as its results.

We can understand this from an example. Suppose we are feeding raw inputs as an image of tomato to the algorithm. We have a supervisor who keeps on correcting the machine or who keeps on training the machine that yes it is a tomato or no it is not a tomato, things like that. So this process keeps on repeating until we get a final trained model, once the model is ready it can easily predict the correct output of a never-seen input.

  • This model performs fast because the training time taken is less as we already have desired results in our dataset.

Supervised models can be further grouped into regression and classification:

Regression: A regression problem is when the output variable is a real continuous value e.g. stock price prediction, house rent prediction, etc.

Classification: A classification problem is when the output variable is a category e.g. “disease” / “no disease”, “tomato” / “no tomato” etc.

Steps involved in supervised ML modeling

Supervised ML modeling consists of the following steps:

  • Defining the problem statement: It is the first step towards building an ML model which is necessary for getting an accurate model.

Applications of Supervised Learning

We can use regression and classification algorithms in various cases. Some of them are as follows:


  • Real estate prediction


  • Credit card fraud detection

Let’s proceed to the next one.

Unsupervised Learning

  • In unsupervised learning, the information used to train is neither classified nor labeled in the dataset.

As we have already discussed that in unsupervised learning our dataset is not labeled, So if we are feeding apple, avocado, and orange as raw input data then our model will distinguish all three but it cannot tell whether a given cluster is of apple or not as it is unlabelled but any new data will automatically fit into the clusters that are formed.

“Clustering” is the process of grouping similar entities together. The goal of this unsupervised machine learning technique is to find similarities in the data point and group similar data points together.

Applications of Unsupervised Learning

We are using unsupervised learning methods in the following cases:

  • Document identification

Reinforcement learning

The third type of machine learning technique is reinforcement learning.

  • It is neither based on supervised learning nor unsupervised learning.

In reinforcement learning, there is a learning agent for which, there is always a start state and an end state.

  • In Reinforcement Learning Problem an agent tries to manipulate the environment.

In this way, the agent learns from the environment.

Let’s understand the reinforcement learning mechanism from the below example.

Consider the scenario of teaching new tricks to your Dog.

  • First, the trainer issues a command, which the dog observes and tries to respond to in many different ways.

We can understand how Reinforcement Learning works from the above example.

In this case, the dog is an agent that is exposed to the environment. An example of a state could be our dog sitting, and we use a specific word for the dog to walk. Our agent reacts by performing an action transition from one “state” to another “state.” For example, Our dog goes from sitting to walking. After the transition, it may get a reward or penalty in return.

Applications of Reinforcement learning

We can use reinforcement learning algorithms in the following cases:

  • Traffic light control


Machine learning uses algorithms to parse data, learn from that data, and make informative decisions based on what it has learned.

To sum up, we can say that,

  • In Supervised Learning, the goal is to generate a formula based on input and output values.

That’s all in this blog. I will extend this further in the upcoming blogs.

  • In Machine Learning we feed data and output to computer and based on that we get the required program.

Aspiring Data Scientist | Machine Learning | NLP | Time Series | Python, Tableau & SQLExpert | Storyteller | Blogger | Data Science Trainee at AlmaBetter