Difference between Cognitive Computing and Machine Learning


Cognitive computing and machine learning are two buzzwords that are frequently used interchangeably in the field of artificial intelligence (AI). Yet, there are important differences between the two, and businesses and organisations looking to use AI to achieve a competitive edge must comprehend these differences. We shall thoroughly examine the distinctions between cognitive computing and machine learning in this article.


What is Cognitive Computing?

The goal of cognitive computing, a branch of artificial intelligence, is to build machines that can reason, learn, and communicate with people in a natural way. Cognitive computing systems use advanced algorithms, natural language processing (NLP), and machine learning to simulate human thought processes, in contrast to traditional computing systems, which are rule-based.

Cognitive computing systems can decipher unstructured data, such as pictures, text, and speech, and draw valuable conclusions from it. These systems are also capable of reasoning, decision-making, and experience-based learning. They can communicate with people in natural language, comprehend situations, and give personalized answers.

Natural language processing, sentiment analysis, speech recognition, and image recognition are a few typical cognitive computing applications. Systems that use cognitive computing include Google Assistant and IBM Watson, for instance.

What is Machine Learning?

Machine learning, a branch of artificial intelligence, allows computers to learn from data without explicit programming. In other words, machine learning algorithms enable computers to continuously improve their performance by autonomously learning from data.

The three types of machine learning algorithms are supervised learning, unsupervised learning, and reinforcement learning. In supervised learning, the machine learning algorithm learns from labelled data to make predictions about new, new dataset. Unsupervised learning is the process through which a machine learning system finds patterns and relationships in unlabeled data. Reinforcement learning uses rewards or penalties to provide feedback to the machine learning algorithm as it learns through trial and error.

Applications for machine learning include fraud detection, natural language processing, picture identification, and recommendation systems. Decision trees, logistic regression, neural networks, and linear regression are a few common machine learning algorithms.

Differences between Cognitive Computing and Machine Learning

Although machine learning and cognitive computing are both subsets of artificial intelligence, they differ greatly from one another. The following are some significant variations −


Making devices that can copy human thought processes, communicate with people in natural language, and offer customized responses is the goal of cognitive computing. The goal of machine learning is to give machines the ability to learn from data and statistically improve their performance.


Natural language processing, machine learning, and computer vision are just a few of the methods used in cognitive computing to mimic human thought processes. On the other hand, machine learning heavily relies on algorithms that learn from data.

Data Types

Cognitive computing systems are capable of comprehending and analysing unstructured data, including speech, images, and text. Both organized and unstructured data can be used by machine learning algorithms, but structured data is often more productive.


Cognitive computing is often used in applications that require natural language processing, sentiment analysis, and personalized responses. Machine learning is used in a wide range of applications, including image recognition, fraud detection, recommendation systems, and predictive analytics.

User Interaction

Cognitive computing systems are intended to communicate with users in natural language, comprehend context, and offer tailored responses. Often, user-facing interactions between machine learning systems and users are limited.

Training Data

Large amounts of training data are necessary for cognitive computing systems to learn from. While machine learning algorithms still need training data, they frequently have a smaller learning curve.

Here are the differences briefed in tabular form −

Cognitive Computing

Machine Learning

It Mimics the human thinking

It mainly Learns from the data

Uses natural language processing, and machine learning with computer vision

It Relies primarily on ML algorithms

It Can analyzes unstructured data

Can analyze with structured and unstructured data

Used for natural language processing, sentiment analysis, personalized responses

Used for image recognition, fraud detection, recommendation systems, predictive analytics

Interacts with users in a natural language

Does not interact with users directly

Requires large amounts of training data

Can learn from smaller amounts of data

less interpretable

More interpretable than Cognitive

Focuses on cognition and perception

Focuses on prediction and optimization

This Emulates human reasoning processes

It Automates decision-making processes

Combines one or more AI techniques

Primarily depends on statistical models

Designed for complex tasks that require contextual understanding

Designed for specific tasks based on predefined criteria

Involves higher levels of human-machine interaction

Involves lower levels of human-machine interaction

Examples of cognitive: IBM Watson, Google Assistant

Examples of ML: TensorFlow, Keras, Scikit-learn


To conclude, cognitive computing and machine learning are two potent AI methodologies that are applied to a range of challenging issues. Machine learning uses statistical models to make predictions and optimize results, whereas cognitive computing aims to replicate human thought and perception. For companies and organisations looking to integrate AI solutions into their operations, it is essential to comprehend the distinctions between these two methodologies. Organizations may utilise the full potential of AI to achieve their goals and spur innovation in their particular industries by selecting the best AI technique based on their unique demands.