Engineering
ML Engineer
Master the art of deploying production-ready models and build a high-growth career as a Machine Learning Engineer at top global tech firms and Indian startups.
Instructor: Priya Sharma
About this course
This comprehensive course bridges the gap between data science and software engineering. You will dive deep into the end-to-end ML lifecycle, starting from robust data engineering and model selection to hyperparameter tuning and model optimization. We cover industry-standard tools and frameworks like TensorFlow and PyTorch, ensuring you understand both the mathematics and the practical implementation. Designed specifically for Indian engineering students and freshers, this program is ideal for those who have a basic understanding of Python and want to specialize in AI. Whether you are aiming for placements at Tier-1 product companies or looking to build your own AI-driven startup, this course provides the technical rigor and practical exposure needed to stand out in a competitive job market. By the end of this course, you will be able to build, scale, and monitor ML systems in production environments. You will master MLOps principles, learn to containerize applications using Docker, and deploy models on major cloud platforms. You will graduate with a portfolio of real-world projects, ready to tackle complex engineering challenges and excel in technical interviews.
What you'll cover
- 1Introduction to ML Engineering & Lifecycle
- 2Advanced Data Preprocessing and Feature Engineering
- 3Supervised and Unsupervised Learning Algorithms
- 4Deep Learning with Neural Networks
- 5Model Evaluation and Hyperparameter Tuning
- 6Working with Large Datasets and Spark
- 7Model Deployment with Flask and FastAPI
- 8Introduction to MLOps and Data Versioning
- 9Containerization with Docker and Kubernetes
- 10Scaling Models on AWS/GCP Cloud
- 11Monitoring and Maintaining Models in Production
- 12Capstone Project: End-to-End ML Pipeline