Coursera MOOC / Non-credit 0

Machine Learning and Deep Learning for Software Engineers

Name: Machine Learning and Deep Learning for Software Engineers
Price: 49 USD
Availability: InStock

About this course

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.

75/100

CourseAsk score

What the provider tells you: 39/45
Who stands behind it: 20/35
How complete the listing is: 16/20

Scores how much the provider publishes and who stands behind it — not how well it is taught.

What you'll learn

understand applied machine learning fundamentals
build and train models using Scikit-learn, TensorFlow, and PyTorch
create production-ready APIs with FastAPI and Flask
design scalable microservices for ML inference
implement MLOps practices including experiment tracking and model versioning
manage CI/CD pipelines for continuous delivery of ML models

Course objectives

enable professionals to integrate ML models into existing software systems
emphasize scalable software design for machine learning applications
provide practical experience with model deployment and performance optimization

Machine Learning Software Engineering #scikit-learn #full-stack development #machine learning #mlops #model optimization #backend development #ci/cd #pytorch #fastapi #tensorflow #flask #model versioning

$49.00

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