Handling large datasets efficiently is a crucial skill for data scientists and engineers. Large datasets often do not fit into memory, making traditional data manipulation techniques impractical. In this guide,….
Feature engineering is one of the most crucial steps in the data preprocessing pipeline. It involves creating new features or modifying existing ones to improve the performance of machine learning….
Exploratory Data Analysis (EDA) is a crucial step in the data analysis process where you analyze the dataset to summarize its main characteristics, often visualizing them to identify patterns, trends,….
Gradient Boosting (XGBoost, LightGBM, CatBoost) in Machine Learning 1. Introduction to Gradient Boosting Gradient Boosting is a powerful ensemble learning technique used in machine learning for classification and regression tasks…..
k-Nearest Neighbors (k-NN) Algorithm in Machine Learning 1. Introduction to k-Nearest Neighbors (k-NN) k-Nearest Neighbors (k-NN) is a supervised learning algorithm used for classification and regression tasks. It is a….
Support Vector Machines (SVM) in Machine Learning 1. Introduction to Support Vector Machines (SVM) Support Vector Machine (SVM) is a supervised learning algorithm used for classification and regression problems. SVM….
Random Forests in Machine Learning 1. Introduction to Random Forests Random Forest is a Supervised Machine Learning algorithm that is used for both Classification and Regression tasks. It is an….
Decision Trees in Machine Learning 1. Introduction to Decision Trees A Decision Tree is a Supervised Learning algorithm used for both classification and regression problems. It mimics human decision-making by….
Logistic Regression in Machine Learning 1. Introduction to Logistic Regression Logistic Regression is a Supervised Learning algorithm used for classification problems. Unlike Linear Regression, which predicts continuous values, Logistic Regression….
Polynomial Regression in Machine Learning 1. Introduction to Polynomial Regression Polynomial Regression is an extension of Linear Regression that models the relationship between the independent variable (X) and the dependent….
