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How Banks Actually Work - Product, Business Model, Risk
Published 9/2026
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch
Language: English | Duration: 3h 4m | Size: 735.07 MB

The business models, metrics and benchmarks that tell you what is really going on inside a bank

What you'll learn
Understand banking business models
Learn most important financial metrics in banking and finance industry
Understand what are the risks of banking industry and cause of financial crisis in history
Conduct comparison of performance by different banks

Requirements
No economic degrees required, you will learn what is bank and how bank works.

Description
This course contains the use of artificial intelligence.

How Banks Actually Work is a bank analyst's map of an industry that is easy to describe and hard to read. It is built for people who work in or follow finance and want to understand a bank's numbers the way an analyst does, without having done the job. No prior banking knowledge is assumed.

The course starts with what a bank actually is: a balance-sheet business that borrows short and lends long, where the mismatch is the product rather than a defect. From there it separates the industry into its real families, retail, commercial, investment, asset management, custody and payments, and shows why each one funds itself differently and optimises for different things. It then covers the supervisors and central banks that sit above them, and why the same business faces different constraints in different countries.

The second half is the analytical toolkit. You learn the return on equity tree that organises every bank metric, where reported numbers come from and why two identical banks can report differently, and the four profitability ratios with their different benchmarks. Capital, asset quality and liquidity each get a lesson, including the metrics that mattered at Silicon Valley Bank. The course closes with cross-system benchmarks and a method for comparing two banks without fooling yourself.

Who this course is for
People working in banks and financial institutions
Stock market analysts try to learn about banking industry
People who want to know more about banking business model

Homepage

https://www.udemy.com/course/understand-how-banks-actually-work-in-3-hours/

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• views: 92
• date: 17 September 2026
• posted by: 0nelove

Azure RAG Patterns: Azure AI Search & OpenAI Pipelines
Published 9/2026
Created by ACHRAF ER-RAYA
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch
Level: All Levels | Genre: eLearning | Language: English | Duration: 6 Lectures ( 1h 21m ) | Size: 951.8 MB

Master Retrieval Augmented Generation (RAG) on Azure: AI Search, Azure OpenAI, hybrid retrieval & agentic chat.

What you'll learn
⚡ Build automated Python ingestion pipelines to parse, chunk, & index documents with Hybrid Search & Semantic Ranker.
⚡ Design production RAG topologies using Azure AI Foundry SDK, query rewriting, & conversation state management.
⚡ Deploy safe RAG apps to Azure App Service with Azure Content Safety guardrails & automated RAG Triad evaluations.
⚡ Implement end-to-end security, Azure AI Content Safety guardrails, and automated RAG Triad evaluations to safely deploy production RAG apps to Azure.

Requirements
❗ Basic proficiency in Python and an active Azure subscription with access to Azure OpenAI services.

Description
This course contains the use of artificial intelligence.

Build a production-minded Retrieval Augmented Generation application on Azure instead of stopping at a toy chatbot.

This hands-on course takes you from Azure resource foundation to secure document ingestion, hybrid retrieval, cited chat, evaluation, and release review. You will use Microsoft Foundry, Azure AI Search, Azure OpenAI models, managed identity concepts, and practical engineering templates that you can adapt to an internal knowledge assistant.

Who this course is for
⭐ Python developers, AI engineers, and cloud architects looking to build secure, production-ready enterprise RAG applications on Azure.

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• date: 17 September 2026
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Advanced RAG Techniques
Published 9/2026
Created by Start-Tech Academy
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch
Level: All Levels | Genre: eLearning | Language: English | Duration: 40 Lectures ( 4h 43m ) | Size: 2.3 GB

Master Advanced RAG with Hybrid Search, Query Transformation, Self-RAG, CRAG, GraphRAG, and Multimodal Retrieval

What you'll learn
⚡ Design and build advanced RAG pipelines using semantic chunking, retrieval strategies, and optimized search techniques
⚡ Improve RAG retrieval quality using hybrid search, keyword search, cross encoders, and advanced ranking methods
⚡ Enhance retrieval accuracy through multiple query generation, query rewriting, query expansion, and HyDE techniques
⚡ Develop advanced RAG applications using Self-RAG, Corrective RAG, and GraphRAG architectures for reliable information retrieval
⚡ Build multimodal RAG systems that process complex PDFs, tables, embeddings, and multimodal data for effective retrieval
⚡ Evaluate and optimize RAG applications for quality, cost, reliability, and production performance using observability, tracing, and telemetry

Requirements
❗ No prior AI or technical experience is required — just a computer, internet connection, and a willingness to learn advanced RAG techniques.

Description
Are you an AI engineer, machine learning professional, data scientist, software developer, or someone looking to build more accurate, reliable, and production-ready Retrieval-Augmented Generation (RAG) applications?

Imagine being able to improve document retrieval, optimize chunking strategies, enhance user queries, combine multiple search methods, build Self-RAG and Corrective RAG systems, work with complex PDFs and tables, retrieve information from multimodal data, and evaluate and optimize RAG applications for real-world use.

Retrieval-Augmented Generation is rapidly becoming one of the most important techniques for building AI applications that can work with private, domain-specific, and constantly changing information. However, building a basic RAG pipeline is only the beginning. Real-world applications require advanced retrieval strategies, better query understanding, reliable architectures, multimodal capabilities, evaluation, cost optimization, and production observability.

Advanced RAG techniques help overcome many of the limitations found in traditional RAG systems. From semantic chunking and hybrid search to query rewriting, HyDE, Self-RAG, Corrective RAG, and GraphRAG, these techniques can significantly improve the quality and reliability of AI-powered applications.

In this course, you will learn how to build and optimize advanced RAG systems through a practical, hands-on approach. Starting with RAG foundations and architecture, you will explore advanced chunking and retrieval techniques, query transformation, advanced RAG architectures, multimodal retrieval, evaluation, cost optimization, and production monitoring.

What You Will Learn
✨ Understanding RAG architecture and advanced retrieval workflows

✨ Learning different chunking strategies for RAG applications

✨ Implementing fixed-size and semantic chunking techniques

✨ Understanding the limitations of fixed-size chunking

✨ Building semantic chunking pipelines through hands-on implementation

✨ Understanding search algorithms used in RAG systems

✨ Implementing keyword search for information retrieval

✨ Understanding and implementing hybrid search methods

✨ Using cross encoders for improved document ranking

✨ Building practical hybrid search pipelines

✨ Understanding query enhancement and transformation techniques

✨ Generating multiple queries to improve retrieval coverage

✨ Applying query rewriting and query expansion techniques

✨ Understanding HyDE for improving semantic retrieval

✨ Implementing practical query transformation workflows

✨ Understanding advanced RAG architectures and their applications

✨ Building and understanding Self-RAG systems

✨ Understanding Corrective RAG (CRAG) architectures

✨ Implementing practical Corrective RAG workflows

✨ Understanding GraphRAG and graph-based retrieval approaches

✨ Understanding multimodal RAG and multimodal information retrieval

✨ Parsing complex PDFs and extracting information from tables

✨ Understanding embeddings for advanced retrieval workflows

✨ Implementing multimodal retrieval techniques

✨ Building practical multimodal RAG applications

✨ Evaluating the performance and quality of RAG systems

✨ Understanding cost optimization strategies for RAG applications

✨ Monitoring RAG applications using observability techniques

✨ Understanding tracing and telemetry for production RAG systems

✨ Applying practical techniques for building production-ready RAG pipelines

Why This Course Is Important
Basic RAG systems can provide useful results, but real-world applications often face challenges such as poor document retrieval, irrelevant context, incomplete answers, complex documents, high operational costs, and difficulty monitoring system performance.

Advanced RAG techniques provide solutions to these challenges by improving how information is processed, retrieved, ranked, and provided to language models. Techniques such as semantic chunking, hybrid search, query transformation, Self-RAG, Corrective RAG, and GraphRAG allow developers to build more capable and reliable AI applications.

Modern organizations are increasingly using RAG to build AI assistants, knowledge management systems, enterprise search applications, document intelligence solutions, and domain-specific AI tools. Understanding how to optimize these systems is becoming an important skill for professionals working with Generative AI.

This course focuses on the techniques required to move beyond basic RAG implementations and build systems that are more accurate, efficient, scalable, and suitable for real-world applications.

What Makes This Course Unique
This course focuses on practical implementation rather than theory alone. Each major concept is connected to practical RAG development scenarios so that you can understand not only what a technique is, but also when and why it should be used.

You will explore advanced chunking, retrieval, query transformation, advanced RAG architectures, multimodal retrieval, evaluation, cost optimization, and observability through a structured learning path.

The course progressively moves from RAG foundations to advanced techniques, helping you understand how individual components affect the overall performance of a RAG system.

You will work with practical implementations covering semantic chunking, hybrid search, query transformation, Corrective RAG, multimodal retrieval, evaluation, and production monitoring.

The course is designed for learners who want to go beyond basic RAG concepts and develop the skills needed to design and improve advanced RAG applications.

A basic understanding of Generative AI, Large Language Models, embeddings, and RAG concepts will be helpful, but the course progressively explains the key concepts required to understand the advanced techniques.

Start Your Advanced RAG Journey
The future of AI applications is moving beyond simple chatbot experiences toward intelligent systems that can retrieve, reason over, and work with complex sources of information.

Advanced RAG techniques are helping organizations build AI systems that can work with enterprise documents, structured and unstructured data, complex PDFs, tables, images, and knowledge graphs while delivering more relevant and reliable responses.

In this course, you will learn how to move beyond traditional RAG and build advanced retrieval pipelines using modern techniques such as semantic chunking, hybrid search, query transformation, Self-RAG, Corrective RAG, GraphRAG, and multimodal retrieval.

You will also learn how to evaluate, optimize, monitor, and improve RAG applications so they are better prepared for real-world and production environments.

If you are ready to take your RAG skills beyond the basics and learn how to design, optimize, and build advanced RAG systems, enroll now and start your Advanced RAG journey today.

Who this course is for
⭐ AI and Machine Learning Engineers who want to design, develop, and optimize advanced Generative AI and RAG applications.
⭐ Data Scientists and Data Engineers working with retrieval systems, unstructured data, knowledge bases, and AI-powered data applications.
⭐ Software Developers and AI Developers building intelligent search, document intelligence, enterprise knowledge management, and AI assistant solutions.
⭐ Generative AI and NLP Professionals looking to move beyond basic RAG implementations and apply advanced retrieval and generation techniques.
⭐ Cloud and AI Solution Architects interested in designing scalable, reliable, and production-ready RAG architectures for real-world applications.
⭐ Technical Professionals and Researchers exploring Self-RAG, Corrective RAG, GraphRAG, multimodal retrieval, evaluation, and RAG optimization.
⭐ Students and Aspiring AI Professionals pursuing careers in Artificial Intelligence, Machine Learning, Data Science, NLP, or Generative AI who want practical RAG development skills.

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• date: 17 September 2026
• posted by: DrZero

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