Master RAG Scoring: Advanced Search Algorithms with Python
Focused View
32:05
1. Section 1 - Basic Ranking (Part 1).mp4
05:14
2. Section 1 - Basic Ranking (Part 2).mp4
03:38
3. Section 2 - Advanced Scoring Mechanisms.mp4
03:45
4. Section 3 - Implementation and Evaluation of Ranking Techniques (Pa.mp4
05:03
5. Section 3 - Implementation and Evaluation of Ranking Techniques (Pa.mp4
05:10
6. Section 4 - Optimization and Adaptation for Specific RAG Tasks (Par.mp4
03:38
7. Section 4 - Optimization and Adaptation for Specific RAG Tasks (Par.mp4
05:37
More details
Course Overview
This intensive course teaches cutting-edge ranking and scoring techniques for Retrieval-Augmented Generation (RAG) systems, covering BM25, BERT embeddings, semantic similarity, and ensemble methods using Python.
What You'll Learn
- Implement foundational ranking algorithms like BM25 and cosine similarity
- Apply advanced techniques including BERT embeddings and Sentence Transformers
- Optimize search systems through tuning and domain adaptation
Who This Is For
- Data scientists building sophisticated search systems
- NLP engineers working on RAG applications
- AI developers needing advanced retrieval techniques
Key Benefits
- Hands-on experience with state-of-the-art ranking algorithms
- Practical Python implementation skills for production systems
- Ability to significantly improve search accuracy and performance
Curriculum Highlights
- Foundations of Document Relevance Scoring
- Advanced Semantic Matching Techniques
- System Optimization and Performance Tuning
Focused display
Category
- language english
- Training sessions 7
- duration 32:05
- level preliminary
- English subtitles has
- Release Date 2025/05/10
