Principal Component Analysis (PCA) Start writing here... Principal Component Analysis (PCA) is a popular dimensionality reduction technique used in machine learning and statistics. It is primarily used to simplify data while retaining ...
Decision Trees Start writing here... Decision Trees are a popular, easy-to-understand machine learning algorithm used for both classification and regression tasks. They work by recursively splitting the dataset into...
Logistic Regression Start writing here... Absolutely! Logistic Regression is a popular algorithm used for classification , not regression—despite its name! 🔐 What is Logistic Regression? Logistic regression models the pr...
Linear Regression Start writing here... Absolutely! Linear Regression is one of the most fundamental and widely used algorithms in machine learning, especially for regression tasks. 📈 What is Linear Regression? Linear ...
Loss Functions (MSE, Cross-Entropy, etc.) Start writing here... Absolutely! Loss functions are at the heart of training machine learning models—they tell the model how wrong its predictions are, so it can adjust and improve. 🧮 What is a Loss ...
Feature Scaling (Normalization & Standardization) Start writing here... Absolutely! Feature scaling is a crucial preprocessing step in machine learning, especially for models that rely on distance or gradient-based optimization (like k-NN, SVMs, logi...
Feature Engineering Start writing here... Absolutely! Feature engineering is one of the most important steps in building a successful machine learning model. ⚙️ What is Feature Engineering? Feature engineering is the pro...
Cross-Validation Start writing here... Sure! Cross-validation is a powerful technique used to evaluate how well a machine learning model will perform on unseen data — especially when your dataset is limited. 🔁 What is...
Training, Validation, and Test Sets Start writing here... Absolutely! Understanding the roles of training , validation , and test sets is essential for building and evaluating machine learning models effectively. 📂 1. Training Set Purpo...
Bias-Variance Tradeoff Start writing here... Absolutely! The Bias-Variance Tradeoff is a key concept in understanding how machine learning models generalize to unseen data. 📊 Bias-Variance Tradeoff 🔷 Bias Definition : Error...
Overfitting & Underfitting Start writing here... Great question! Overfitting and underfitting are two common problems in machine learning that affect how well a model performs, especially on new, unseen data. 🔴 Overfitting Defi...
Reinforcement Learning Start writing here... Reinforcement learning (RL) is a type of machine learning where an agent learns by interacting with an environment , receiving feedback in the form of rewards or penalties based ...