A Guide to Machine Learning Algorithms and Applications

Rahul S
21 min readJun 12, 2023

(this article will be continuously updated)


The provided text is a comprehensive guide that discusses various aspects of machine learning algorithms and their applications. It starts by introducing the three main types of machine learning: unsupervised learning, supervised learning, and semi-supervised learning.

The guide then delves into dimensionality reduction, which is useful when dealing with large datasets or numerous features. It explains the concept of dimension reduction algorithms and discusses three popular algorithms: Principal Component Analysis (PCA), Singular Value Decomposition (SVD), and Linear Discriminant Analysis (LDA). Each algorithm is explained in terms of its purpose, applications, and suitability for different types of data.

The text also covers clustering techniques, which group similar data points together based on their intrinsic characteristics. It mentions the DBSCAN algorithm for clustering non-labeled data and provides insights into three commonly used algorithms for clustering with a specified number of clusters: K-Modes, K-Means, and Gaussian Mixture Model (GMM).

The guide then shifts focus to regression, a powerful machine learning algorithm used for predicting continuous numerical values as outcomes. It explains linear regression and its applications in various fields. It also discusses other regression algorithms, including Neural Networks, Gradient Boosting Trees, and Random Forest, highlighting their strengths and applications.

The text concludes by emphasizing the importance of considering specific requirements and evaluating trade-offs between speed and accuracy when selecting a regression algorithm. It also emphasizes the need for careful preprocessing, hyperparameter tuning, and monitoring to achieve optimal performance and adapt to changing data patterns.

Overall, the guide provides valuable insights into different types of machine learning algorithms, their applications, and considerations for selecting the most suitable algorithm for specific tasks.


Machine Learning is a subfield of artificial intelligence (AI) and computer science that leverages data and algorithms to mimic human learning processes, continuously improving its accuracy.