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Machine Learning (ML) Introduction – A Step-by-Step Guide

Introduction to Machine Learning

Machine learning is a subset of AI that enables the ability of machines to perform at ease, where they can learn and develop from the past without being constantly trained. It is mainly used to develop computer programs that get data by themselves and use it for learning purposes. There are two types of machine learning –

  • Supervised learning
  • Unsupervised learning
Supervised learning Unsupervised learning
We already have a subset or column to define that target. In unsupervised learning we don’t have a target.
Classes- Regression, Classification Classes – Clustering, Dimensionality reduction.
We train the dataset according to the target variable to predict it. Training is based on previous learning, not any target variable.
Grouping and clustering is done according to the target to be achieved. We perform grouping and clustering in this unsupervised learning.
Used in image, speech recognition and forecasting. Used to pre-process the data, pre-train supervised learning algorithms.
Eg. For predicting salary. We predict it based on age, expenditure, experience also based on other salary dataset. Eg. Performing a behavior analysis as an experiment to predict them.

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About the Author

Garima Hansa
Garima Hansa
Technical Content Writer

Garima Hansa is an emerging Data Analyst and Machine Learning enthusiast with hands-on experience through academic and independent projects. She specializes in Python, SQL, data visualization, statistical analysis, and machine learning techniques. Known for building efficient, well-documented solutions and translating complex data insights into actionable recommendations, Garima contributes meaningful value to research, analytics, and developer communities.