What Is Machine Learning and Why Is It Important?

Machine learning (ML) is a component of artificial intelligence (AI) that emphasizes the creation of systems that can learn and improve their performance based on the data they consume. On the other hand AI refers to the simulation of human intelligence by machines. So it is said that ML is a subset of AI. Although ML and AI are frequently discussed together and the terms are sometimes used interchangeably, they are distinct concepts.

Updated on: 01-02-23

Name: Machine Learning

Referred to as: ML

Category: Technical

ML = Machine Learning
ML = Machine Learning

Correct Use: N/A


Machine learning is a branch of artificial intelligence that uses statistical methods to allow computers to learn and make decisions without human intervention. This approach is based on the idea that computers can extract knowledge from data, identify patterns, and make predictions with minimal human supervision. Now machine learning is widely used in the SEO sector. 

There are three main types of machine learning:

1. Supervised Learning:

In supervised learning, the computer is given a dataset with labeled examples (input and the corresponding output) and the task is to train a model that can predict the output for new, unseen inputs. This is the most common type of machine learning, and it is used for tasks such as image classification and spam detection.

The main goal of supervised learning is to establish a relationship between the input variable(s) and the output variable(s). It can be further divided into two main categories:


This category deals with algorithms that are designed to solve classification problems, where the output variable is categorical, for example, yes or no, true or false, male or female, etc. Applications of classification algorithms are found in practical areas such as identifying spam emails and filtering them.


This category encompasses algorithms that are used to solve regression problems, where input and output variables have a linear relationship. Regression algorithms predict continuous output variables, for instance, forecasting weather, analyzing market trends, etc.

2. Unsupervised Learning:

In unsupervised learning, the computer is given a dataset without any labels and the task is to find patterns or structure in the data. This is used for tasks such as anomaly detection and clustering.

Unsupervised learning can be further divided into two types:


This technique involves grouping objects based on similarities or differences between them. For example, clustering customers based on the products they purchase. Common clustering algorithms include K-Means, Mean-Shift, DBSCAN, Principal Component Analysis, and Independent Component Analysis.


Association learning aims to identify relationships between variables in a large dataset. It determines the dependency of different data items and maps associated variables. Applications include web usage mining and market data analysis. Popular algorithms that follow association rules include Apriori, Eclat, and FP-Growth.

3. Reinforcement Learning:

Reinforcement learning involves training a model to make a sequence of decisions. The model is trained to take actions in an environment to maximize a reward. This type of learning is used in robotics and gaming.

Reinforcement learning can be further classified into two types:

Positive reinforcement:

This type of learning involves providing a reinforcing stimulus after a specific behavior of the agent, which increases the likelihood that the behavior will be repeated in the future. An example of this would be providing a reward after a desired behavior.

Negative reinforcement:

Negative reinforcement involves strengthening a specific behavior by avoiding a negative outcome. This type of learning increases the likelihood that the behavior will be repeated in the future to avoid the negative outcome.

Our take:

Machine learning plays a crucial role in the field of data science. It utilizes statistical methods and algorithms to classify, predict and extract valuable insights from data mining projects. These insights can inform and drive decision-making in various applications and businesses, and ultimately lead to improvements in key performance metrics. With the continued growth of big data, the demand for professionals proficient in data science and machine learning is on the rise.

Extra reading:


  • Define the problem clearly: Understand the problem you are trying to solve, and define the task and performance metrics clearly before starting to collect and analyze data.
  • Collect and prepare the data: Collect high-quality, relevant data, and prepare it by cleaning, normalizing and transforming it as necessary.
  • Use appropriate evaluation metrics: Choose evaluation metrics that are appropriate for the problem and the data, and use a separate test set to evaluate the performance of the model.
  • Use ensemble methods: Combine multiple models to improve the overall performance, such as random forests, gradient boosting, and stacking.


  • Bias in the data: If the data used to train a model is biased, the model will also be biased and may make unfair or inaccurate predictions.
  • Overfitting: Overfitting occurs when a model is too complex and is trained too closely on the training data. This can result in poor generalization performance on new, unseen data.
  • Computational resources: Training and running machine learning models can require significant computational resources, including memory and processing power.
  • Difficulty in handling noisy data: Machine learning models might have difficulties in dealing with very noisy data, which can negatively impact their performance.
  • Difficulty in Handling Unstructured Data: Machine learning models may have difficulty handling unstructured data, such as text, images and videos, which need to be preprocessed before being fed to the model.


There are several best practices that should be followed when using machine learning:

  • Monitor and update the model: Monitor the performance of the model over time, and update it as new data becomes available or as the underlying distribution of the data changes.
  • Continuously improve the model: Use techniques such as hyperparameter tuning, feature engineering, and model selection to continuously improve the performance of the model.
  • Use explainable models: Use models that are interpretable and explainable, such as linear models or decision trees, especially when the results will be used to make important decisions.
  • Continuously learn and improve: Machine learning is an iterative process, and it’s important to continuously learn and improve by experimenting with different algorithms, models, and techniques.


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The term machine learning was coined in 1959 by Arthur Lee Samuel, an IBM employee and pioneer in the field of computer gaming and artificial intelligence.

What Google Says: ML