Machine Learning Types and Algorithms

Study Notes

Supervised Learning: The algorithm is trained on labeled data, and the goal is to learn a mapping between input data and output labels.
Unsupervised Learning: The algorithm is trained on unlabeled data, and the goal is to discover patterns or structure in the data.
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.
Decision Trees: A tree-based model that splits data into subsets based on features.
Random Forest: An ensemble model that combines multiple decision trees.
Support Vector Machines (SVMs): A model that finds a hyperplane that maximally separates classes.
Neural Networks: A model composed of interconnected nodes (neurons) that learn complex patterns.
K-Means Clustering: A clustering algorithm that groups data into K clusters based on similarity.

Accuracy: The proportion of correctly classified instances.
Precision: The proportion of true positives among all positive predictions.
Recall: The proportion of true positives among all actual positive instances.
F1 Score: The harmonic mean of precision and recall.
Mean Squared Error (MSE): The average squared difference between predicted and actual values.

Overfitting: When a model is too complex and performs well on training data but poorly on new data.
Underfitting: When a model is too simple and performs poorly on both training and new data.

Model Selection: The process of choosing the best model for a given problem.
Hyperparameter Tuning: The process of optimizing model hyperparameters to improve performance.
Cross-Validation: A technique used to evaluate model performance on unseen data.

Image and Speech Recognition: Machine learning is used in applications such as facial recognition, object detection, and speech-to-text systems.
Natural Language Processing (NLP): Machine learning is used in applications such as language translation, sentiment analysis, and text summarization.
Recommendation Systems: Machine learning is used in applications such as personalized product recommendations and content suggestions.

Supervised learning involves training on labeled data to learn a mapping between input data and output labels.
Unsupervised learning involves training on unlabeled data to discover patterns or structure in the data.
Reinforcement learning involves learning by interacting with an environment and receiving feedback in the form of rewards or penalties.

Linear regression is a linear model that predicts a continuous output variable.
Decision trees split data into subsets based on features.
Random forest is an ensemble model that combines multiple decision trees.
Support vector machines (SVMs) find a hyperplane that maximally separates classes.
Neural networks are composed of interconnected nodes (neurons) that learn complex patterns.
K-means clustering groups data into K clusters based on similarity.

Accuracy measures the proportion of correctly classified instances.
Precision measures the proportion of true positives among all positive predictions.
Recall measures the proportion of true positives among all actual positive instances.
F1 score is the harmonic mean of precision and recall.
Mean squared error (MSE) measures the average squared difference between predicted and actual values.

Overfitting occurs when a model is too complex and performs well on training data but poorly on new data.
Underfitting occurs when a model is too simple and performs poorly on both training and new data.

Model selection involves choosing the best model for a given problem.
Hyperparameter tuning involves optimizing model hyperparameters to improve performance.
Cross-validation is a technique used to evaluate model performance on unseen data.

Image and speech recognition applications include facial recognition, object detection, and speech-to-text systems.
Natural language processing (NLP) applications include language translation, sentiment analysis, and text summarization.
Recommendation systems applications include personalized product recommendations and content suggestions.