Generative AI Tutorials

Generative AI - Types of Machine Learning

Types of Machine Learning

Supervised Learning: Supervised Learning is the most prevalent sort of machine learning. In supervised learning, the model is trained using a labeled dataset, which implies that each training sample is associated with an output label. Common algorithms include linear regression, decision trees, and support vector machines.

Unsupervised Learning: Unlike supervised learning, this kind of learning uses unlabeled data. The objective is to infer the underlying structure present in a set of data points. Techniques like clustering (K-means) and association (Apriori algorithm) are commonly used.

Reinforcement learning: This approach essentially focuses on how agents should act in a certain environment in order to optimize a cumulative reward notion. It is used in a wide range of industries, such as AI games and robots. Algorithms like Q-learning and Deep Q Networks (DQN) are commonly used in this sector.

 

Key Concepts:

  • Training and Testing Data: To assess the model's performance on unobserved data, data is usually divided into training and testing sets.
  • Overfitting and Underfitting: Overfitting is the result of a model that is too intricate, capturing noise rather than the underlying pattern. When a model is too basic to accurately represent the complexity of the data, underfitting occurs.
  • Evaluation Metrics: The F1-score, ROC-AUC, accuracy, precision, and recall are examples of common metrics.

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Generative AI

Beginner 5 Hours

Types of Machine Learning

Supervised Learning: Supervised Learning is the most prevalent sort of machine learning. In supervised learning, the model is trained using a labeled dataset, which implies that each training sample is associated with an output label. Common algorithms include linear regression, decision trees, and support vector machines.

Unsupervised Learning: Unlike supervised learning, this kind of learning uses unlabeled data. The objective is to infer the underlying structure present in a set of data points. Techniques like clustering (K-means) and association (Apriori algorithm) are commonly used.

Reinforcement learning: This approach essentially focuses on how agents should act in a certain environment in order to optimize a cumulative reward notion. It is used in a wide range of industries, such as AI games and robots. Algorithms like Q-learning and Deep Q Networks (DQN) are commonly used in this sector.

 

Key Concepts:

  • Training and Testing Data: To assess the model's performance on unobserved data, data is usually divided into training and testing sets.
  • Overfitting and Underfitting: Overfitting is the result of a model that is too intricate, capturing noise rather than the underlying pattern. When a model is too basic to accurately represent the complexity of the data, underfitting occurs.
  • Evaluation Metrics: The F1-score, ROC-AUC, accuracy, precision, and recall are examples of common metrics.

Related Tutorials

Frequently Asked Questions for Generative AI

Sequence of prompts stored as linked records or documents.

It helps with filtering, categorization, and evaluating generated outputs.



As text fields, often with associated metadata and response outputs.

Combines keyword and vector-based search for improved result relevance.

Yes, for storing structured prompt-response pairs or evaluation data.

Combines database search with generation to improve accuracy and grounding.

Using encryption, anonymization, and role-based access control.

Using tools like DVC or MLflow with database or cloud storage.

Databases optimized to store and search high-dimensional embeddings efficiently.

They enable semantic search and similarity-based retrieval for better context.

They provide organized and labeled datasets for supervised trainining.



Track usage patterns, feedback, and model behavior over time.

Enhancing model responses by referencing external, trustworthy data sources.

They store training data and generated outputs for model development and evaluation.

Removing repeated data to reduce bias and improve model generalization.

Yes, using BLOB fields or linking to external model repositories.

With user IDs, timestamps, and quality scores in relational or NoSQL databases.

Using distributed databases, replication, and sharding.

NoSQL or vector databases like Pinecone, Weaviate, or Elasticsearch.

With indexing, metadata tagging, and structured formats for efficient access.

Text, images, audio, and structured data from diverse databases.

Yes, for representing relationships between entities in generated content.

Yes, using structured or document databases with timestamps and session data.

They store synthetic data alongside real data with clear metadata separation.



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