quantx73 / unsupervisedlearningportfolio Goto Github PK

Unsupervised learning is a type of machine learning where the model is trained using data that is not labeled, meaning the model tries to identify patterns directly from the input data without any reference to known or labeled outcomes. There are several types of unsupervised learning techniques:

• Clustering: This is one of the most common unsupervised learning methods. It groups a set of objects in such a way that objects in the same group (cluster) are more similar to each other than to those in other groups. Examples include K-means clustering, hierarchical clustering, and DBSCAN.

• Association Rule Learning: This technique is used to discover interesting relations between variables in large databases. It is often used for market basket analysis to find products that are frequently bought together. A common algorithm used here is the Apriori algorithm.

• Principal Component Analysis (PCA): PCA is a technique used to emphasize variation and bring out strong patterns in a dataset. It's often used to make data easy to explore and visualize. It's also used for dimensionality reduction.

• Anomaly Detection: This involves identifying rare items, events or observations which raise suspicions by differing significantly from the majority of the data. This is commonly used in fraud detection, network security, and fault detection.

• Neural Networks and Deep Learning: Techniques such as autoencoders, which are a type of artificial neural network used to learn efficient codings, fall under this category. They are used for tasks like feature extraction, image reconstruction, and generative models.

https://github.com/AnalyticalHarry/DeepLearningForDigitClassification

• Self-Organizing Maps (SOMs): SOMs are a type of artificial neural network used for feature mapping and dimensionality reduction, often visualized in two dimensions.

• t-Distributed Stochastic Neighbor Embedding (t-SNE): t-SNE is a non-linear dimensionality reduction technique particularly well suited for embedding high-dimensional data for visualization in a low-dimensional space of two or three dimensions.