[In Depth] Linear Regression: Concept And Application
Imagine you are a real estate agent trying to estimate the price of a house based on its size. You've...
Probability Calibration in Machine Learning
Probability calibration is the key to unlocking the true potential of machine learning models, ensuring that the predicted probabilities align...
[In depth] Naive Bayes: Concept And Application
In this tutorial, we will learn our next machine-learning model called Naive Bayes. Naive Bayes is widely recognized as a...
[In Depth] Random Forests & Ensemble Learning: Concept And Application
In this tutorial, we are going to learn an advanced concept related to decision trees called random forests. The idea...
[In Depth] Decision Trees: Concept And Application
In this post, we will learn about our next machine-learning algorithm, Decision Trees. Imagine you're trying to decide what movie...
[In Depth] Support Vector Machine: Concepts And Applications
In this post, we will learn about our next machine learning algorithm called support vector machine or SVM or support...
Machine Learning: An Introduction
At its core, Machine Learning (ML) is like having a super-smart computer that learns from experience. Imagine teaching a child...
[In Depth] Linear Discriminant Analysis: Concepts And Application
In the previous post, we learned about PCA and how to use PCA in machine learning for various applications. In...
[In Depth] Principal Components Analysis: Concepts And Application
In the previous post, we learned about SVD and how to use SVD for low-rank approximation. Building upon the concepts...
[In Depth] Singular Value Decomposition: Concepts And Applications
The singular value decomposition (SVD) is a crucial concept for machine learning that builds the foundation of many algorithms you...