Machine Learning and API with Python

Duration 5 days – 35 hrs

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.

Prerequisites

• 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.

Course Content

Day 1: Introduction to Machine Learning

• Introduction to Machine Learning: Understanding the basics of machine learning.
• Data Preprocessing and Feature Engineering: Techniques for data cleaning, handling missing values, feature selection, normalization, and dealing with categorical variables.
• Supervised Learning Algorithms: Overview of linear regression, logistic regression, decision trees, random forests, and SVM.
• Model Selection and Evaluation: Cross-validation techniques, hyperparameter tuning, and evaluation metrics.

Day 2: Advanced Machine Learning Techniques

• Ensemble Methods: Understanding bagging, boosting, random forests, and gradient boosting techniques.
• Introduction to Deep Learning: Basics of neural networks, including feedforward, convolutional, and recurrent neural network structures.
• Deep Learning for Computer Vision: Techniques for image classification, transfer learning, object detection, and image segmentation.
• Natural Language Processing (NLP): Text preprocessing, tokenization, word embeddings, text classification, sentiment analysis, and language generation.

Day 3: Introduction to API Development

• Overview of API Development: Understanding the importance and principles of RESTful architecture.
• Building APIs with Flask: Setting up a Flask environment, handling requests and responses, and implementing authentication with Flask-JWT.
• Database Integration with SQLAlchemy: Working with databases, performing CRUD operations, and understanding database models and migrations.
• API Documentation and Testing: Creating API documentation with Swagger and implementing unit and integration testing.

Day 4: Advanced API Development

• Scaling and Performance Optimization: Techniques for caching, load balancing, and API performance monitoring.
• Error Handling and Validation: Managing errors and exceptions, input validation, and custom error responses.
• Versioning and Deployment: API versioning strategies, containerization with Docker, and deployment to cloud platforms.
• Security and Authentication: Implementing security best practices and OAuth 2.0 for authentication.

Day 5: Building and Deploying ML APIs

• Integrating Machine Learning Models with APIs: Techniques for saving/loading models, building prediction endpoints, and preprocessing data.
• Model Deployment and Monitoring: Deploying ML models with Flask or Django, containerizing with Docker, and monitoring model performance.
• Advanced Topics in ML and API Development: Exploring reinforcement learning, handling streaming data with APIs, and more.