This repository is a curated study guide for fundamental concepts in Machine Learning (ML). It covers both Supervised and Unsupervised learning methods, following widely accepted definitions and examples, with reference to GeeksforGeeks: Machine Learning.
Machine Learning is a subfield of Artificial Intelligence (AI) that enables systems to learn and improve from experience without being explicitly programmed. This study guide organizes ML concepts into two main categories:
- Supervised Learning
- Unsupervised Learning
In supervised learning, the model is trained on a labeled dataset, meaning the input comes with the correct output. The aim is to learn a mapping from inputs to outputs.
-
Linear Regression
Predicts a continuous value using a linear relationship between input and output variables. -
Logistic Regression
A classification algorithm used to predict discrete values (e.g., 0 or 1). -
Decision Trees
Tree-based models that split the dataset based on features to make predictions. -
Random Forest
An ensemble of decision trees that improves accuracy and reduces overfitting. -
Support Vector Machines (SVM)
Finds the optimal hyperplane that separates data into different classes. -
K-Nearest Neighbors (KNN)
A lazy learner algorithm that classifies a new data point based on similarity to k neighbors. -
Naive Bayes
A probabilistic classifier based on Bayes' Theorem with strong independence assumptions. -
Boosting Algorithms
Boosting is an ensemble learning technique that builds a strong predictive model by combining multiple weak learners (typically decision trees). It works sequentially, where each new model focuses on correcting the errors made by the previous ones.
Unsupervised learning deals with unlabeled data. The model tries to identify patterns, groupings, or structures within the data without guidance.
-
K-Means Clustering
Partitions the data into k clusters based on feature similarity. -
Principal Component Analysis (PCA) A dimensionality reduction technique that transforms data into a new coordinate system.
-
Reinforcement Learning Reinforcement Learning is a type of machine learning where an agent learns to make decisions by interacting with an environment to maximize some notion of cumulative reward over time.