← Back to Hub: https://github.com/Ram-466/ml-hub
A clear, practical roadmap to become a Machine Learning Engineer. This roadmap is implementation-first and portfolio-driven.
- Learn the concept
- Implement it in code
- Write notes in the corresponding
ml-notes-*repo - Build or extend a project
- Git & GitHub workflow
- Python environment (venv / conda)
- VS Code, Jupyter
- Linux + CLI basics
Status: ⬜ Not started
- Variables, data types
- Control flow
- Functions
- OOP basics
- Error handling
- File handling
- Writing clean, readable code
Target repo:
ml-notes-python
Status: ⬜ Not started
- Linear algebra (vectors, matrices, dot product)
- Probability basics
- Statistics (mean, variance, distributions)
- Gradient intuition
Target repo:
ml-notes-math-stats
Status: ⬜ Not started
- NumPy
- Pandas
- Data cleaning
- Exploratory Data Analysis
- Visualization (Matplotlib)
Target repo:
ml-notes-data
Status: ⬜ Not started
- Train / validation / test split
- Feature engineering
- Metrics & evaluation
- Models:
- Linear & Logistic Regression
- KNN
- Naive Bayes
- SVM
- Decision Trees
- Random Forest
- Gradient Boosting
- Hyperparameter tuning
Target repo:
ml-notes-ml
Status: ⬜ Not started
- Neural network fundamentals
- PyTorch basics
- CNNs
- Transfer learning
- Optimization & regularization
Target repo:
ml-notes-deep-learning
Status: ⬜ Not started
- Text preprocessing
- Embeddings
- Transformers
- Fine-tuning pretrained models
Target repo:
ml-notes-nlp
Status: ⬜ Not started
- Experiment tracking
- Model versioning
- APIs (FastAPI)
- Docker
- CI/CD basics
- Monitoring & drift detection
Target repo:
ml-notes-mlops
Status: ⬜ Not started
Minimum recommended:
- 1 classic ML project
- 1 NLP project
- 1 Computer Vision project
- 1 End-to-end deployed project
Target repos:
ml-project-*
Status: ⬜ Not started
Be job-ready as an ML Engineer with:
- Strong fundamentals
- Clean GitHub
- 4–6 high-quality projects
- Ability to explain decisions in interviews