A level-wise progressive path from core mathematical foundations to production-ready MLOps systems
STAGE 01
Core Foundations
4-6 Weeks
Master the theoretical math, statistics, and essential cleaning processes before training any algorithms.
- Linear Algebra & SVD Decompositions
- Multi-Dimensional Jacobians & Hessians
- MLE & Hypothesis Significance Testing
- Dimensionality Reduction (PCA, UMAP)
STAGE 02
Traditional ML
6-8 Weeks
Learn classic classification and regression methods for tabular data along with robust evaluation splits.
- SVM Dual Formulations & Kernels
- Ensemble Boosting (XGBoost, LightGBM)
- EM Algorithms & Gaussian Mixtures
- Cross-Validation & ROC-AUC Curves
STAGE 03
Deep Learning & AI
8-12 Weeks
Transition to deep representations, self-attention mechanisms, and state-of-the-art vision and language models.
- Backpropagation & He Initialization
- Transformers & Rotary Embeddings (RoPE)
- Parameter-Efficient Tuning (LoRA/QLoRA)
- NeRF & 3D Gaussian Splatting
STAGE 04
MLOps & Production
4-6 Weeks
Bridge research and production. Containerize, monitor performance, and automate model pipelines.
- Docker/Kubernetes & ONNX Serving
- Drift Detection (PSI & KS Statistics)
- Dual-Storage Feature Stores (Feast)
- Pipeline Orchestration (MLflow, Airflow)