Udemy - FastAPI Build a Banking API that has AI/ML Fraud Detection

voska89 · May 28, 2025

Free Download Udemy - FastAPI Build a Banking API that has AI/ML Fraud Detection

Published 5/2025
Created by Alpha Omondi Ogilo
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch

Level: Intermediate | Genre: eLearning | Language: English | Duration: 86 Lectures ( 9h 28m ) | Size: 6.66 GB
Learn FastAPI, MLFlow, AI/ML, Docker, Celery etc, to build a banking API with transaction fraud protection
What you'll learn
You will learn how to integrate Docker with Celery, Redis,RabbitMQ, FlowermMLFlow and FastAPI
You will learn how to use scikit learn,numpy and pandas for machine learning, to create a transaction analysis and Fraud detection system
You will learn how to use mlflow to create machine learning training pipelines and lifecycle management
You will learn how to use Reverse Proxies and load balancing with TRAEFIK
You will learn how manage multiple Docker containers with Portainer in development and in Production
You will learn how to use Loguru for comprehensive Logging
You will learn how to use Redis,RabbitMQ and celery for background machine learning task processing.
Requirements
This course is NOT for absolute beginners.
This course is targeted at Python Developers with at least 1 year of web development experience or more
You should be familiar with the basic concepts surrounding shell scripts, Docker, and FastAPI.
You should be familiar with concepts surrounding asynchronous python.
Description
Welcome to this comprehensive course on building a banking API with FastAPI with an AI-powered/machine learning transaction analysis and fraud detection system. This course goes beyond basic API development to show you how to architect a complete banking system that's production-ready, secure, and scalable.What Makes This Course Unique:Learn to build a real-world banking system with FastAPI and SQLModelImplement AI/ML-powered fraud detection using MLflow and scikit-learnMaster containerization with Docker Master reverse proxying and load balancing with TraefikHandle high-volume transactions with Celery, Redis, and RabbitMQSecure your API with industry-standard authentication practicesYou'll Learn How To:✓ Design a robust banking API architecture with domain-driven design principles✓ Implement secure user authentication with JWT, OTP verification, and rate limiting✓ Create transaction processing with currency conversions and fraud detection✓ Build a machine learning pipeline for real-time transaction risk analysis✓ Deploy with Docker Compose and manage traffic with Traefik✓ Scale your application using asynchronous Celery workers✓ Monitor your system with comprehensive logging using Loguru✓ Train, evaluate, and deploy ML models with MLflow✓ Work with PostgreSQL using SQLModel and Alembic for migrationsKey Features in This Project:Core Banking Functionality: Account creation, transfers, deposits, withdrawals, statementsVirtual Card Management: Card creation, activation, blocking, and top-upsUser Management: Profiles, Next of Kin information, KYC implementationAI/ML-Powered Fraud Detection: ML-based transaction analysis and fraud detectionBackground Processing: Email notifications, PDF generation, and ML trainingAdvanced Deployment: Container orchestration, reverse proxying, and high availabilityML Ops: Model training, evaluation, deployment, and monitoringThis course is perfect For:• Backend developers with at least 1 year of experience, looking to build secure fintech solutions.• Tech leads planning to architect fintech solutions.By the end of this course, you'll have built a production-ready banking system with AI capabilities that you can showcase in your portfolio or implement in real-world projects.Technologies You'll Master:FastAPI & SQLModel: For building high-performance, type-safe APIsDocker & Traefik: For containerization and intelligent request routingCelery & RabbitMQ: For distributed task processingPostgreSQL & Alembic: For robust data storage and schema migrationsScikit-learn: For machine learning.MLflow: For managing the machine learning lifecyclePydantic V2: For data validation and settings managementJWT & OTP: For secure authentication flowsCloudinary: For handling image uploadsRate Limiting: For API protection against abuseNo more basic tutorials - let's build something real!
Who this course is for
Python Developers,curious about building a Fintech API's
Intermediate Python Developers with at least 1 year of experience, more is better
Intermediate Python Develpers curious about machine learning applications in real world projects.
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https://www.udemy.com/course/fastapi-banking-with-ai-ml-fraud-detection/

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Udemy - FastAPI Build a Banking API that has AI/ML Fraud Detection

voska89

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