Ancient Egyptian: rnpt Mj iqr nTrt Ml Lrning (Machine Learning)

Machine Learning

Definition Machine Learning (ML) is a subset of Artificial Intelligence (AI) that involves training machines to learn from data and make predictions or decisions without being explicitly programmed.

Types of Machine Learning

1. Supervised Learning: The machine is trained on labeled data to learn the relationship between input and output.
2. Unsupervised Learning: The machine is trained on unlabeled data to discover patterns or structure.
3. Reinforcement Learning: The machine learns by interacting with an environment and receiving rewards or penalties.

Machine Learning Algorithms

Supervised Learning Algorithms

1. Linear Regression: Predicts continuous outcomes based on linear relationships.
2. Logistic Regression: Predicts binary outcomes based on logistic functions.
3. Decision Trees: Classifies data using decision rules.
4. Random Forest: Combines multiple decision trees for improved accuracy.

Unsupervised Learning Algorithms

1. K-Means Clustering: Groups similar data points into clusters.
2. Hierarchical Clustering: Builds a hierarchy of clusters.
3. Principal Component Analysis (PCA): Reduces dimensionality of data.

Applications of Machine Learning

1. Image Recognition: Classifies images into categories.
2. Natural Language Processing (NLP): Analyzes and generates human language.
3. Recommendation Systems: Suggests products or services based on user behavior.

Challenges in Machine Learning

1. Overfitting: Models are too complex and perform poorly on new data.
2. Underfitting: Models are too simple and fail to capture patterns.
3. Bias and Variance: Models struggle to balance simplicity and accuracy.