Intro to Machine Learning


written by: Joydip Kanjilal

Machine learning (ML) is the process of predicting patterns, classifying, and grouping data sets in both supervised and unsupervised ways. Machine Learning techniques enable computers to train on data inputs and then apply statistical analysis to produce results within a specific range. In other words, Machine Learning allows computers to build models from sample data, which in turn helps automate the decision-making processes that rely on data inputs.

There has been a massive surge in machine learning (ML) usage amongst the developer community worldwide. This article talks about Machine Learning, how it works, and its types in detail.

Why is Machine Learning Important?

The need for individuals with experience in Machine Learning is rising daily as the quantity of data being generated across the globe continues to increase. Machine learning is essential because of the range of its applications and its incredible ability to adapt to and solve complex problems quickly, effectively, and effectively. 

Machine Learning has been used in the design and development of a plethora of different applications. Without machine learning, data predictability would be rendered ineffective. Machine learning is critical due to the breadth of its applications and its remarkable capability to adapt and provide solutions to complex problems efficiently, effectively, and rapidly.

Here are some of the Machine Learning use cases:

  • Energy production

  • Computational Biology

  • Automotive, Manufacturing

  • Retail

  • Healthcare

  • Natural Language Processing

  • Financial Services

How Does Machine Learning Work?

Machine Learning works by analyzing data and detecting patterns, with little or no human involvement required. Machine learning methods are classified into two categories: supervised learning and unsupervised learning. While the former involves training a model based on known input and output data to predict future outputs, the latter identifies hidden patterns or inherent structures in input data.

Artificial Intelligence vs. Machine Learning vs. Deep Learning

It is worth noting that the terms “Artificial Intelligence” and “Machine Learning,” though used interchangeably, are not identical; there are subtle differences between them. Artificial Intelligence is a broader term that refers to using computers to do activities based on specific algorithms. 

Machine Learning is a subset of Artificial Intelligence concerned with a computer system’s capacity to learn for itself given a data collection. Another concept worth mentioning here is Deep Learning; this subset of Machine Learning deals with deep neural networks. While Machine Learning uses algorithms to interpret data, develop and train models from it, and make choices based on those models, Deep Learning builds artificial neural networks and makes intelligent decisions independently.

Types of Machine Learning

Here is a quick overview on the types of Machine Learning:

Supervised Learning

Supervised learning or supervised machine learning is a field of machine learning where the algorithms are designed to learn by example. In other words, Supervised Learning is a technique in which a computer system is trained on labeled input data. It is characterized using labeled datasets to train algorithms capable of correctly classifying data and predicting outcomes. When new data is introduced into the model during the cross-validation process, the model changes its weights in response to the new data until the model has been fitted correctly. 

When it comes to classification and regression issues, supervised learning is very effective, such as identifying which category a news item belongs to or forecasting the number of sales for a specific product in a particular quarter. The model is trained until it can find the underlying patterns and connections in the input data, allowing it to predict labels for previously unseen data accurately. Supervised learning helps solve a range of real-world issues at scale, such as identifying spam and placing it in a different folder from your email.

The following are the major types of Supervised Learning:

  • Classification – this is a type of Supervised Learning in which the output contains defined labels

  • Regression – this is a type of Supervised Learning in which the output has continuous value

Here are some of the areas where Supervised Learning have been used:

  • Sentiment Analysis

  • Recommendations

  • Bio-Informatics

  • Speech Recognition

  • Spam Detection

 Unsupervised Learning

In Unsupervised learning, the data is not labeled, and the learning algorithm needs to discover similarities in the input data. As unlabeled data is found in more abundance than labeled data, Machine Learning algorithms that provide support for unsupervised learning are extremely important. Contrary to Supervised Learning, Unsupervised Learning has no valid output values. Unsupervised learning may discover hidden patterns within a dataset which enables the computer system to find the representations necessary to categorize raw data automatically.

Some of the applications of Unsupervised Learning include the following:

  • Document Clustering

  • Data Reduction

The following are the major types of Unsupervised Learning:

  • Clustering – this is a technique applied to data based on different patterns

  • Association – this is a rule-based machine learning method that uncovers some valuable relationships between parameters in a big data set

Reinforcement Learning

Reinforcement learning is a field of Machine Learning in which your models learn by interacting with the environment. It is a way of educating machine learning models to make decisions without any human intervention. It is a Machine Learning technique that enables software agents and machines to automatically identify the optimal behavior for maximizing performance in a given context. 

However, it lacks a labeled dataset or outcomes connected with the data, which means that the only way to perform a task is through experience. In reinforcement Learning, the model improves its performance through learning via Reward Feedback. Every time we provide information, the training data is updated to include it. And the more it’s trained, the better it becomes.

Applications of Reinforcement Learning include the following

  • Robotics

  • Recommendations

  • Traffic Light Management System

  • Self-driving Cars


Semi-Supervised Learning

Semi-supervised machine learning combines the best of both worlds – it includes both supervised and unsupervised machine learning. In other words, semi-supervised learning is a technique that utilizes a small quantity of labeled data in conjunction with a massive volume of unlabeled data. You train a machine learning system with a “labeled” dataset that includes outcome information for each record.

Why do we need Semi-Supervised Machine Learning?

When you do not have enough labeled data and lack the expertise or resources to collect more data, you may enhance your training data using semi-supervised techniques. The model, for example, might be developed to identify fraud for a large bank.

To retrain the model, you may use a semi-supervised learning method and label the data. Then you can apply the retrained model to the latest data and then take advantage of Supervised Machine Learning methods to detect fraud more efficiently.


Machine Learning is a quantum leap ahead in how computers may learn in a human-like manner without being explicitly programmed. Machine Learning-enabled Artificial Intelligence systems have been making headlines across the world.


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