Role Roadmap * 7 Modules * 3-6 Months to Job-Ready
Your path to becoming an ML Engineer
From Python fundamentals to production ML systems -- a structured, opinionated roadmap built around what top companies actually hire for. No filler, no guesswork.
ML Engineer
Build, train, and scale machine learning systems that power real products
$115k
Avg US Salary
High
Job Demand
6-12mo
Time to Job-ready
Python
Primary Language
Skills You'll Build
✓ Python
✓ Scikit-learn
✓ PyTorch
✓ SQL
+ TensorFlow
+ Docker
+ FastAPI
+ MLflow
+ AWS SageMaker
Kubernetes
Spark
Transformers
Ray
Essential
Strongly Recommended
Nice to Have
Salary Range (UK)
$70-90k
Junior
0-2 years
$95-120k
Mid-level
2-5 years
$125-175k
Senior
5+ years
7 Modules
01
Python & Math Foundations
Build rock-solid Python fluency and the mathematical intuition needed for ML. We cover only what you actually use -- not a maths degree in disguise.
Python data structures & OOP
NumPy & vectorised operations
Pandas for data manipulation
Linear algebra essentials
Statistics & probability basics
Calculus intuition for ML
Resources
Python for Data Analysis -- Wes McKinneyBook
3Blue1Brown -- Essence of Linear AlgebraVideo
NumPy + Pandas Exercises -- KaggleLab
Mini Project: EDA on a real dataset
Perform exploratory data analysis on a public dataset (housing prices or Titanic). Clean, visualise, and summarise key findings in a Jupyter notebook.
02
Core ML Algorithms
The ML algorithms you'll actually use on the job. Understand them intuitively, implement them from scratch, then use Scikit-learn efficiently.
Linear & logistic regression
Decision trees & random forests
Gradient boosting (XGBoost)
K-means & clustering
Model evaluation & metrics
Cross-validation & pipelines
Resources
Hands-On ML -- Aurélien GéronBook
Scikit-learn Official DocsDocs
Kaggle Titanic CompetitionProject
Project: End-to-end ML Pipeline
Build a complete classification pipeline: data ingestion -> feature engineering -> model training -> evaluation. Use Scikit-learn Pipelines and log metrics.
03
Deep Learning & Neural Networks
PyTorch from scratch. Understand backprop, build CNNs, and train models on real GPU hardware.
Neural network architecture
Backpropagation & optimisers
PyTorch tensors & autograd
CNNs for vision tasks
Regularisation techniques
Transfer learning
Project: Image Classifier with PyTorch
Fine-tune a pretrained ResNet on a custom image dataset. Deploy it as a simple FastAPI endpoint.
04
NLP & Transformers
Attention mechanisms, BERT, GPT, and building RAG pipelines with HuggingFace Transformers.
Tokenisation & embeddings
Attention & transformers
HuggingFace ecosystem
Fine-tuning BERT / LLaMA
RAG pipeline architecture
Vector databases
05
MLOps & Model Deployment
Docker, CI/CD for ML, MLflow experiment tracking, and deploying models to cloud APIs.
Docker & containerisation
FastAPI model serving
MLflow tracking
CI/CD with GitHub Actions
AWS SageMaker basics
Model monitoring & drift
06
System Design for ML
How to design real-time inference systems, feature stores, and ML platforms from scratch.
07
Interview Prep & Portfolio
Crack the ML interview: coding rounds, ML theory questions, system design, and how to present your portfolio.
Career Planning
Ready to build your personalized AI career plan?