Overview
The Machine Learning and API with Python Training course is designed for individuals who want to gain proficiency in both machine learning and API development using Python. This comprehensive course covers the foundations of machine learning algorithms and techniques, as well as the practical aspects of building APIs to serve machine learning models.
Objectives
- Gain a solid understanding of machine learning algorithms, techniques, and their practical applications.
- Develop proficiency in building and evaluating machine learning models using Python libraries such as scikit-learn and TensorFlow.
- Learn the fundamentals of API development and understand the importance of RESTful architecture.
- Build robust APIs using Python frameworks like Flask and Django for serving machine learning models.
- Implement authentication, authorization, and input validation in APIs to ensure security and reliability.
- Integrate databases and perform CRUD operations within the API development process.
- Understand API documentation, versioning, and best practices for designing scalable and maintainable APIs.
- Deploy machine learning models as APIs and monitor their performance for real-world usage.
- Explore advanced topics in machine learning, such as deep learning, natural language processing, and reinforcement learning.
- Gain hands-on experience through practical exercises and projects that combine machine learning and API development.
Audience
- Data scientists, analysts, and researchers interested in expanding their knowledge of machine learning and API development.
- Software developers looking to incorporate machine learning capabilities into their applications.
- Engineers and technical professionals seeking to learn the practical aspects of building APIs with Python.
- IT professionals and enthusiasts aiming to enhance their skills in machine learning and API development.
Pre- requisites
- Familiarity with Python programming language.
- Basic understanding of machine learning concepts is recommended but not mandatory.
- Basic knowledge of web development concepts would be beneficial.
Duration: 5 days – 35 hrs
Course Content
Day 1: Introduction to Machine Learning
Introduction to Machine Learning
- Understanding the basics of machine learning
- Supervised, unsupervised, and reinforcement learning
- Real-world applications of machine learning
Data Preprocessing and Feature Engineering
- Data cleaning and handling missing values
- Feature selection and extraction techniques
- Data normalization and scaling
- Dealing with categorical variables
Supervised Learning Algorithms
- Linear regression and logistic regression
- Decision trees and random forests
- Support Vector Machines (SVM)
- Evaluation metrics for classification and regression
Model Selection and Evaluation
- Cross-validation techniques
- Hyperparameter tuning and grid search
- Evaluating model performance and metrics
- Overfitting and underfitting
Day 2: Advanced Machine Learning Techniques
Ensemble Methods
- Bagging and boosting techniques
- Random Forests and Gradient Boosting
- Stacking and ensemble model evaluation
Introduction to Deep Learning
- Neural networks and activation functions
- Feedforward, convolutional, and recurrent neural networks
- Introduction to TensorFlow and Keras
Deep Learning for Computer Vision
- Image classification using deep learning
- Transfer learning and pre-trained models
- Object detection and image segmentation
Natural Language Processing (NLP)
- Text preprocessing and tokenization
- Word embeddings and text classification
- Sentiment analysis and language generation
Day 3: Introduction to API Development
Overview of API Development
- Introduction to APIs and their importance
- Understanding RESTful architecture
- Exploring different API design principles
Building APIs with Flask
- Setting up Flask environment
- Handling requests and responses
- Authentication and authorization with Flask-JWT
- Implementing API endpoints and resource routing
Database Integration with SQLAlchemy
- Working with databases using SQLAlchemy
- Database models and migrations
- Performing CRUD operations with APIs
API Documentation and Testing
- Generating API documentation with Swagger
- Unit testing and integration testing of APIs
- Test-driven development (TDD) approach
Day 4: Advanced API Development
Scaling and Performance Optimization
- Caching and optimizing API responses
- Load balancing and scaling strategies
- API performance monitoring and profiling
Error Handling and Validation
- Handling errors and exceptions in APIs
- Input validation and request parsing
- Custom error responses and status codes
Versioning and Deployment
- API versioning and backward compatibility
- Containerization and Docker
- Deploying APIs to cloud platforms (e.g., AWS, Heroku)
Security and Authentication
- API security best practices
- Implementing OAuth 2.0 for authentication
- Role-based access control (RBAC)
Day 5: Building and Deploying ML APIs
Integrating Machine Learning Models with APIs
- Saving and loading machine learning models
- Building API endpoints for model predictions
- Preprocessing data for model input
Model Deployment and Monitoring
- Deploying ML models with Flask or Django
- Containerizing ML models with Docker
- Monitoring model performance and retraining
Advanced Topics in ML and API Development
- Reinforcement learning and applications
- Handling streaming data with APIs
