Machine Learning is one of the highest-paying and fastest-growing fields in technology. This roadmap will guide you from complete beginner to production ML engineer in a structured, efficient path.

Phase 1: Mathematics & Python Foundations (4–6 weeks)

• Linear Algebra: Vectors, matrices, matrix multiplication, eigenvalues
• Statistics: Probability distributions, hypothesis testing, confidence intervals
• Python: NumPy, Pandas, Matplotlib for data manipulation and visualisation

Phase 2: Core Machine Learning (8–10 weeks)

• Supervised Learning: Linear regression, logistic regression, SVMs, decision trees, random forests
• Unsupervised Learning: K-means clustering, PCA, DBSCAN
• Model Evaluation: Cross-validation, ROC-AUC, confusion matrix, bias-variance tradeoff

Phase 3: Deep Learning (6–8 weeks)

• Neural Network fundamentals, backpropagation, activation functions
• CNNs for computer vision (image classification, object detection)
• RNNs and LSTMs for sequential data (NLP, time-series)

Phase 4: Specialisation (6 weeks)

Choose one: Computer Vision (OpenCV, YOLO), NLP (BERT, GPT fine-tuning), Generative AI (LangChain, RAG), or MLOps (MLflow, Docker, FastAPI).

Phase 5: Portfolio & Deployment

4 portfolio projects deployed on GitHub + Streamlit Cloud or HuggingFace Spaces. Include a Kaggle competition result to stand out.