# Become an ML engineer

Source: https://freeaicourses.com/paths/ml-engineer

## Steps

### Step 1: Kaggle Intro to Machine Learning

Confirms the fundamentals are solid before you go further.

You're confident training and validating basic models.

### Step 2: Machine Learning Specialization

The clearest foundations course for classic machine learning; check for a no-certificate option when you enrol.

You can build and tune linear models, trees, neural networks and clustering models.

### Step 3: Practical Deep Learning for Coders

Adds the deployment and productionisation mindset fast.ai is known for.

You can train, evaluate and ship a deep learning model end to end.

## Related pages

- [Kaggle Intro to Machine Learning](https://freeaicourses.com/courses/kaggle-intro-machine-learning)
- [Machine Learning Specialization](https://freeaicourses.com/courses/machine-learning-specialization)
- [Practical Deep Learning for Coders](https://freeaicourses.com/courses/practical-deep-learning-for-coders)
- [AI learning paths free to follow](https://freeaicourses.com/paths)
- [All free AI courses, rated](https://freeaicourses.com/courses)
- [ML engineer](https://freeaicourses.com/roadmaps/ml-engineer)
- [AI learning path for non-coders](https://freeaicourses.com/paths/ai-for-non-coders)
- [Use AI tools well at work](https://freeaicourses.com/paths/ai-at-work)
- [Python for AI, from zero](https://freeaicourses.com/paths/python-for-ai)
- [Machine learning foundations](https://freeaicourses.com/paths/ml-foundations)
- [The maths ML actually uses](https://freeaicourses.com/paths/maths-for-ml)
- [Deep learning, hands-on](https://freeaicourses.com/paths/deep-learning)
- [Build apps with LLMs](https://freeaicourses.com/paths/build-with-llms)
- [From data analysis to AI](https://freeaicourses.com/paths/data-science-to-ai)
- [Kaggle Learn](https://freeaicourses.com/platforms/kaggle)
- [DeepLearning.AI](https://freeaicourses.com/platforms/deeplearning-ai)
- [fast.ai](https://freeaicourses.com/platforms/fast-ai)