Category: AI & Data Skills

Transfer Learning: Reusing Knowledge to Build Smarter ML Models Faster 🔄 What Is Transfer Learning? Transfer learning is a machine learning technique where a model trained on one task is repurposed on a second, related task. Rather than starting from…

What Is Reinforcement Learning? Reinforcement Learning (RL) is a type of machine learning where an agent learns to make decisions by interacting with an environment, aiming to maximize cumulative rewards over time. Unlike supervised learning, where the model learns from…

Essential Data Preprocessing in ML One-Hot Encoding, Normalization, and Dimensionality Reduction 🔤 One-Hot Encoding One-hot encoding is a technique used to convert categorical variables into a numerical format suitable for machine learning models. Why It’s Needed: Many ML models require…

Machine Learning Glossary / Data Structure Elements Understanding Labels, Instances, and Target Variables in ML labels instances target variables 🔎 What Is a Label? A label is the actual outcome or category assigned to a data instance. In supervised learning,…

Model Architecture Explained: Features, Weights, and Bias in ML Features, Weights & Biases in Machine Learning: A Complete Guide Learn how features, weights, and bias parameters work together to shape model predictions in machine learning. features weights bias 🧠 What…

Understanding Dataset Splits in Machine Learning Training, Validation, and Test Sets 📂 What Are Dataset Splits? In machine learning, dataset splitting is a fundamental step to evaluate how well a model generalizes to unseen data. The dataset is typically divided…

What Is Bias in Machine Learning? Bias refers to the error introduced by approximating a real-world problem, which may be complex, with a simplified model. In statistical terms, it’s the difference between the expected prediction of the model and the…

What Is Feature Engineering? Feature engineering is the process of creating, selecting, transforming, or enriching input variables (features) to improve a machine learning model’s performance. It combines domain knowledge with statistical and algorithmic techniques to expose patterns that algorithms can…

What Is AutoML? Automating Machine Learning for Everyone AutoML (Automated Machine Learning) refers to technologies and tools that automate the entire process of applying machine learning to real-world problems. This includes tasks like data preprocessing, model selection, hyperparameter tuning, and…

Gradient Descent Explained How Machines Learn by Failing Forward What Is Gradient Descent? Gradient Descent is an optimization algorithm used to minimize a function by iteratively moving in the direction of the steepest descent as defined by the negative of…

🧠 Machine Learning Glossary Unified and Expanded A–E F-L M-R S-Z A–E Algorithm A step-by-step procedure or formula for solving a problem. In ML, algorithms define how models learn from data. Artificial Intelligence (AI) A broad field of computer science…