MAchine Learning Methods:
1.Supervised Learning:
1a. Classification: Assigns labels to input data.
1b. Regression: Predicts continuous outcomes.
2.Unsupervised Learning:
2a. Clustering: Groups similar data points together.
2b. Dimensionality Reduction: Reduces the number of features while preserving essential information.
2c. Association Rule Learning: Discovers interesting relationships in data.
3.Semi-supervised Learning: Combines both labeled and unlabeled data for training.
4. Reinforcement Learning: Focuses on learning optimal decision-making strategies by interacting with an environment.
5. Deep Learning:
Artificial Neural Networks (ANNs): Inspired by the human brain's structure, consisting of layers of interconnected nodes (neurons).
Convolutional Neural Networks (CNNs): Specialized for processing grid-like data, such as images.
Recurrent Neural Networks (RNNs): Suitable for sequential data, like time series or natural language.
Generative Adversarial Networks (GANs): Comprising two networks (generator and discriminator) that compete against each other to generate realistic data.
6. Natural Language Processing (NLP): Deals with the interaction between computers and humans through natural language.
Sentiment Analysis: Determines the sentiment expressed in textual data.
Named Entity Recognition (NER): Identifies and classifies named entities (e.g., names of people, organizations) in text.
Machine Translation: Translates text from one language to another.
7. Computer Vision:
Object Detection: Identifies and localizes objects within images or video frames.
Image Segmentation: Divides an image into segments to simplify its representation.
Facial Recognition: Recognizes and identifies faces in images or videos.
8. Anomaly Detection: Identifies outliers or deviations from normal behavior within data.
9. Ensemble Learning: Combines multiple models to improve prediction accuracy and robustness.
10. Bayesian Methods: Utilizes Bayesian probability for inference and decision-making.
11. Meta-Learning: Focuses on learning to learn, where models improve their learning process over time.
12. Time Series Analysis: Analyzes data points collected, recorded, or observed over time.
13. Optimization Algorithms: Techniques for optimizing model parameters to minimize or maximize an objective function.
14. Explainable AI (XAI): Focuses on making machine learning models and their decisions interpretable and understandable to humans.