Introduction to Machine Learning
Machine Learning (ML) is a subset of artificial intelligence (AI) that enables computers to learn patterns from data and make predictions or decisions without being explicitly programmed. It is widely….
Tag: ML Algorithms
Model Evaluation Metrics (Accuracy, Precision, Recall, F1-Score)
Model Evaluation Metrics in Machine Learning Evaluating a machine learning model is crucial for ensuring its effectiveness. Model evaluation metrics provide a way to measure performance, compare models, and fine-tune….
Underfitting vs Overfitting
Underfitting vs Overfitting in Machine Learning Introduction One of the biggest challenges in machine learning is building a model that can generalize well to unseen data. The two common problems….
Bias-Variance Tradeoff
Bias-Variance Tradeoff in Machine Learning Introduction The bias-variance tradeoff is a fundamental concept in machine learning that describes the tradeoff between two sources of error that affect model performance: Understanding….
Train-Test Split and Cross-Validation
Train-Test Split and Cross-Validation in Machine Learning In machine learning, evaluating the performance of a model is crucial to ensure it generalizes well to unseen data. Two widely used techniques….
Types of Machine Learning (Supervised, Unsupervised, Reinforcement)
Types of Machine Learning Machine Learning (ML) is classified into three main types: Each type has its own approach, methodologies, and applications. Below, we will explore them in detail, covering….