Machine Learning and API with Python – Hands-on Training
Course Overview
The Machine Learning and API with Python course is designed to equip participants with the knowledge and skills required to develop and deploy machine learning models while building powerful APIs using Python. This intensive, hands-on training covers both theoretical and practical aspects, enabling professionals to create scalable and secure machine learning APIs.
By integrating machine learning models into APIs, participants will learn how to serve predictions in real time, build RESTful APIs with Flask and Django, and implement authentication and security measures. This training is ideal for software developers, data scientists, engineers, and IT professionals who want to enhance their expertise in machine learning and API development.
Key Learning Outcomes
- Understand the fundamentals of Machine Learning and API with Python.
- Develop machine learning models using Python libraries such as Scikit-learn and TensorFlow.
- Build RESTful APIs with Flask and Django for deploying ML models.
- Implement authentication, authorization, and API security best practices.
- Deploy machine learning models to production environments.
- Optimize and monitor API performance for real-world applications.
Who Should Attend?
- Data scientists and analysts looking to expand into API development.
- Software developers integrating machine learning into applications.
- Engineers and IT professionals aiming to enhance their skills in AI-powered APIs.
Course Prerequisites
- Basic Python programming knowledge.
- Understanding of machine learning concepts (recommended but not mandatory).
- Basic familiarity with web development and APIs.
Course Curriculum
Day 1: Introduction to Machine Learning and API Development
Understanding Machine Learning
- Introduction to machine learning concepts.
- Types of machine learning: supervised, unsupervised, and reinforcement learning.
- Key Python libraries: Scikit-learn, Pandas, NumPy.
Data Preprocessing and Feature Engineering
- Data cleaning, missing value handling, and feature selection.
- Feature scaling and normalization techniques.
- Encoding categorical variables.
Introduction to API Development
- Understanding RESTful APIs and HTTP methods.
- Basic API structure using Flask and Django.
- Consuming and testing APIs with Postman.
Day 2: Machine Learning Model Development
Supervised and Unsupervised Learning
- Implementing classification and regression models.
- Using logistic regression, decision trees, and SVM.
- Evaluating model accuracy and performance metrics.
Building APIs for Machine Learning Models
- Creating API endpoints for model training and predictions.
- Handling API requests and responses.
- Deploying machine learning models using Flask.
Day 3: Advanced API Development
Building Scalable APIs with Flask and Django
- Setting up Flask and Django frameworks.
- Handling GET, POST, PUT, DELETE requests.
- Using Flask-RESTful and Django REST Framework.
Integrating Machine Learning Models into APIs
- Loading and serving pre-trained ML models.
- Processing incoming data for API requests.
- Returning predictions via API responses.
Day 4: Security and Authentication in APIs
API Security Best Practices
- Understanding API vulnerabilities and threats.
- Implementing HTTPS and secure headers.
- Monitoring API security with logging and alerts.
Authentication and Authorization
- Implementing token-based authentication.
- Using OAuth 2.0 for secure access control (OAuth 2.0 Authentication).
- Role-based access control (RBAC) implementation.
Day 5: Deploying and Monitoring Machine Learning APIs
Deploying APIs on Cloud Platforms
- Containerizing APIs with Docker.
- Deploying APIs to AWS, Google Cloud, and Azure.
- Using CI/CD pipelines for continuous deployment.
Monitoring and Optimizing API Performance
- Measuring API response times and scalability.
- Implementing logging and error handling.
- Optimizing API throughput and load balancing.
Advanced Topics in Machine Learning and APIs
- Streaming data processing with Kafka and WebSockets.
- Deploying deep learning models as APIs.
- Real-time AI applications and edge computing.
Conclusion
In this Machine Learning and API with Python course, participants gain hands-on expertise in Python API Development and ML Model Deployment. By integrating machine learning models into scalable APIs, attendees can build secure, high-performance solutions for real-world applications, enhancing their AI capabilities and professional growth in data science and software development.
