anmol
@avdvh.bsky.social
code, coffee, minimalistic, sarcastic, explorer.
1/ What is Unsupervised Learning?
A machine learning approach where models find patterns in unlabeled data without predefined outputs. It discovers hidden structures, clusters, or relationships. Key applications: clustering, anomaly detection, dimensionality reduction.
A machine learning approach where models find patterns in unlabeled data without predefined outputs. It discovers hidden structures, clusters, or relationships. Key applications: clustering, anomaly detection, dimensionality reduction.
February 11, 2025 at 1:30 PM
1/ What is Unsupervised Learning?
A machine learning approach where models find patterns in unlabeled data without predefined outputs. It discovers hidden structures, clusters, or relationships. Key applications: clustering, anomaly detection, dimensionality reduction.
A machine learning approach where models find patterns in unlabeled data without predefined outputs. It discovers hidden structures, clusters, or relationships. Key applications: clustering, anomaly detection, dimensionality reduction.
1/ What is Supervised Learning?
A machine learning approach where models learn from labeled data.
The goal is to map input variables (X) to output variables (Y) using past observations.
The model adjusts its parameters to minimise prediction errors.
A machine learning approach where models learn from labeled data.
The goal is to map input variables (X) to output variables (Y) using past observations.
The model adjusts its parameters to minimise prediction errors.
February 10, 2025 at 6:07 AM
1/ What is Supervised Learning?
A machine learning approach where models learn from labeled data.
The goal is to map input variables (X) to output variables (Y) using past observations.
The model adjusts its parameters to minimise prediction errors.
A machine learning approach where models learn from labeled data.
The goal is to map input variables (X) to output variables (Y) using past observations.
The model adjusts its parameters to minimise prediction errors.