ML Engineering is engineering first. Master Python, OOP, clean code practices, testing, and version control before touching any ML library.
By the end, you'll be able to
Mini-project
Build a Python package with proper structure, tests, CI, and publish it to PyPI. Even a simple utility — the process matters more than the package.
You need math to understand what your models are actually doing. Focus on linear algebra, calculus (gradients), probability, and statistics.
By the end, you'll be able to
Mini-project
Implement linear regression and logistic regression from scratch using only NumPy. No scikit-learn. Understand every line.
Master the fundamentals: regression, classification, clustering, ensemble methods, and the full pipeline from data prep to model evaluation.
By the end, you'll be able to
Mini-project
Build a credit scoring model: feature engineering, model comparison, hyperparameter tuning, and a detailed evaluation report.
Neural networks, CNNs, RNNs, transformers. Learn PyTorch (industry standard for research and production), training techniques, and GPU computing.
By the end, you'll be able to
Mini-project
Fine-tune a pre-trained model (ResNet or BERT) on a custom dataset. Deploy it as an API endpoint.
Specialize in at least one domain. Learn text preprocessing, embeddings, transformers for NLP, or image processing and object detection for CV.
By the end, you'll be able to
Mini-project
Build a sentiment analysis system for Indian product reviews. Handle Hindi-English code-mixed text. Deploy as a REST API.
Production ML is not Jupyter notebooks. Learn Docker, model serving (FastAPI, TorchServe), experiment tracking (MLflow), and monitoring.
By the end, you'll be able to
Mini-project
Deploy a model with FastAPI + Docker, add MLflow tracking, and set up a basic monitoring dashboard.
ML Engineers need to build data pipelines. Learn ETL, data warehousing basics, Spark/PySpark for big data, and how to work with data engineers.
By the end, you'll be able to
Mini-project
Build a pipeline that ingests data from an API, transforms it with PySpark, and stores it in a database for ML training.
Learn to design ML systems: recommendation engines, search ranking, fraud detection. Understand latency, throughput, and how to scale ML in production.
By the end, you'll be able to
Mini-project
Design and partially implement a recommendation system: data pipeline, feature store, model training, A/B testing framework.
ML Engineer interviews: coding (LeetCode medium), ML theory, system design, and a take-home ML project. Prepare across all four areas.
By the end, you'll be able to
Mini-project
Solve 50 LeetCode problems, study 10 ML system design cases, practice explaining your projects, and apply to 20+ ML roles.
Not sure if this is the right roadmap? Browse all our career paths and find the one that matches your goals.