Understanding Vectors What is a vector? vectors are objects which have both length (magnitude) and direction. You can consider it as a directed line segment.…
In this article, we are going to walkthrough a basic but one golden rule that most of us ignored in practice is “Tell, Don’t Ask”.…
Why using int, String or boolean everywhere is a problem and How Java 21 Helps fix it!! The Problem: Primitive Obsession This code smell appears…
Design complexity can be identified using Coupling & Cohesion. In this article, we will discuss about Coupling.
Explore how inheritance may go wrong and how to carefully address the associated issues using design principles especially with Liskov Substitution Principle (LSP).
what, why and how to do Feature Scaling, Normalization Vs Standardization, common mistakes a beginner may do on Feature Scaling
5 Part series of most important Data Pre-Processing Techniques of Machine Learning: Encode Categorical Values, One-Hot Encoding, Ordinal Encoding, Categorical Data types explained with examples
Major imputation techniques in Machine learning, US census data, Listwise Deletion, Impute NaN with mean, Trimmed Mean, median, mode, Drop columns if >60% of data is missing
This is a 5 Part series of most important Data Pre-Processing Techniques of Machine Learning. Part 1 – Verify data types of the variables/features.
How to impute missing values using SimpleImputer and ColumnTransformer, Disadvantages of SimpleImputer, Advantage of ColumnTransformer