Machine Learning Engineer roadmap

Train models and turn them into reliable products. Competitive at entry level, so plan a realistic path.

What employers usually expect

Skill	Level	How to prove it
Python	Must have	Write clean code with NumPy and pandas.
Maths foundations	Must have	Linear algebra, probability and the intuition behind gradient descent.
Classical machine learning (scikit-learn)	Must have	Regression, classification, trees and clustering, with proper evaluation.
Model evaluation	Must have	Choose the right metric, use cross-validation and avoid data leakage.
SQL	Must have	Pull and prepare data from databases.
Deep learning (PyTorch or TensorFlow)	Strong plus	Train and fine-tune a neural network on one task.
Deployment	Strong plus	Serve a model through an API in a container.
Git	Must have	Keep experiments reproducible.

Courses often cover algorithms and neural networks in theory, but not the full pipeline: cleaning data, evaluating honestly and deploying.
Many student projects are copied Kaggle notebooks with high accuracy on easy data.
Few students learn to explain model limits, which employers value.
Reality check: roles titled Machine Learning Engineer rarely go to freshers. Data analyst, junior data scientist and software developer roles that use ML are more common first steps. Build the ML skills while targeting those.

Courses differ between colleges. Use this as a checklist against your own syllabus, not as a judgement of it.

Assumes about 10 hours a week. Move the checkpoints if you have exams.

Weeks 1 to 4

Done when: You can load, clean and explore a dataset and describe what you found.

Weeks 5 to 8

Study linear and logistic regression, decision trees, random forests and clustering with scikit-learn.
Learn train/test splits, cross-validation, precision, recall, F1 and ROC-AUC.
Practise spotting data leakage and class imbalance.

Done when: You can build a baseline model, evaluate it properly and say where it fails.

Weeks 9 to 12

Fine-tune a pretrained model for a text or image task with PyTorch or TensorFlow.
Wrap one model in an API and run it in Docker.
Write a short report for each project, including limitations.

Done when: Three projects, one of them deployed, each with an honest write-up.

Predict something useful, such as customer churn or house prices: explore the data, build a baseline, compare models and analyse errors.

Show on your resume: Your baseline, the improvement, the metric you chose and why.

Fine-tune a pretrained model on a small dataset, such as support-ticket types or plant diseases.

Show on your resume: Results on held-out data, examples it gets wrong and the data you used.

Serve one of your models through an API with a simple form, in a container, with logging.

Show on your resume: A live demo or screen recording, and a short model card listing limits and risks.

Change the dataset, the idea or the features so the project is yours. Interviewers can tell a copied tutorial in two questions.

Bias versus variance, overfitting and how to reduce it.
Precision, recall, F1 and ROC-AUC: when each one matters.
What is data leakage, with an example?
How gradient descent works, in plain words.
Explain one of your projects from raw data to final result, including what went wrong.

Kaggle Learn · fast.ai · The scikit-learn user guide · Official PyTorch tutorials. Check that each is still free and current before you begin.