Machine Learning and Deep Learning for Software Engineers

This specialization empowers software engineers, backend developers, and full-stack professionals to integrate, deploy, and maintain machine learning models within production software systems. You will approach ML through an engineering lens — emphasizing software design, APIs, scalability, and maintainability rather than theory alone. Starting with applied ML fundamentals, you will build and train models using Scikit-learn, TensorFlow, and PyTorch while writing modular, testable ML code. As you progress, you will convert ML models into production-ready APIs using FastAPI and Flask, design scalable microservices for inference, and manage model versioning and performance optimization. The third course introduces MLOps foundations — covering reproducibility, experiment tracking, and version control using Git, DVC, and MLflow. The final course brings everything together with CI/CD pipelines, continuous delivery of models, monitoring inference performance and data drift, and implementing retraining and rollback strategies. By the end, you will have the engineering competencies to build, serve, operate, and maintain ML-powered applications across the full production lifecycle.