Machine Learning Fundamentals

Machine learning is a subset of artificial intelligence (AI) that involves training algorithms to enable them to learn from data and make predictions or decisions without being explicitly programmed.

Supervised Learning: The algorithm is trained on labeled data, where the correct output is already known, to learn the mapping between input and output.
Unsupervised Learning: The algorithm is trained on unlabeled data, and it must find patterns or relationships in the data on its own.
Reinforcement Learning: The algorithm learns by interacting with an environment and receiving feedback in the form of rewards or penalties.

Linear Regression: A linear model that predicts a continuous output variable based on one or more input features.
Decision Trees: A tree-based model that splits data into subsets based on feature values and makes predictions.
Random Forests: An ensemble learning method that combines multiple decision trees to improve prediction accuracy.
Neural Networks: A model inspired by the structure and function of the human brain, composed of layers of interconnected nodes (neurons).

Image Recognition: Machine learning algorithms can be trained to recognize objects, people, and patterns in images.
Natural Language Processing (NLP): Machine learning algorithms can be used for text analysis, sentiment analysis, and language translation.
Predictive Maintenance: Machine learning algorithms can be used to predict equipment failures and schedule maintenance.
Recommendation Systems: Machine learning algorithms can be used to recommend products or services based on user behavior and preferences.

Overfitting: When a model is too complex and performs well on training data but poorly on new, unseen data.
Underfitting: When a model is too simple and fails to capture the underlying patterns in the data.
Bias and Variance: The trade-off between the error introduced by simplifying a model (bias) and the error introduced by fitting the noise in the data (variance).
Explainability and Interpretability: The difficulty of understanding why a machine learning model is making a particular prediction or decision.

Machine learning is a subset of artificial intelligence that enables algorithms to learn from data and make predictions or decisions without being explicitly programmed.

Supervised Learning: Trained on labeled data to learn input-output mapping.
Unsupervised Learning: Trained on unlabeled data to find patterns or relationships.
Reinforcement Learning: Learns by interacting with an environment and receiving feedback in the form of rewards or penalties.

Linear Regression: Predicts continuous output variable based on input features.
Decision Trees: Splits data into subsets based on feature values and makes predictions.
Random Forests: Combines multiple decision trees to improve prediction accuracy.
Neural Networks: Composed of layers of interconnected nodes (neurons) inspired by the human brain.

Image Recognition: Recognizes objects, people, and patterns in images.
Natural Language Processing (NLP): Used for text analysis, sentiment analysis, and language translation.
Predictive Maintenance: Predicts equipment failures and schedules maintenance.
Recommendation Systems: Recommends products or services based on user behavior and preferences.

Overfitting: Performs well on training data but poorly on new data due to model complexity.
Underfitting: Fails to capture underlying patterns in data due to model simplicity.
Bias and Variance: Trade-off between error introduced by simplifying a model (bias) and error introduced by fitting noise in data (variance).
Explainability and Interpretability: Difficulty in understanding why a machine learning model makes a particular prediction or decision.