Phase 4: Advanced ML & MLOps
Days 55–67 | 13 Lessons
Explore advanced ML techniques including time series forecasting, transformers, and production deployment patterns with MLOps best practices.
Lessons in This Phase
| Day | Title | Description |
|---|---|---|
| 55 | Day 55 – Advanced Unsupervised Learning | Density-based clustering, hierarchical approaches, and modern embeddings unlock structure within |
| 56 | Day 56 – Time Series and Forecasting | Seasonality-aware forecasting keeps plans grounded in data rather than gut feel. The lesson |
| 57 | Day 57 – Recommender Systems | Recommender systems pair users with relevant products when catalogues explode. This lesson covers |
| 58 | Day 58 – Transformers and Attention | Transformers dominate modern sequence modelling. This lesson demonstrates how to: |
| 59 | Day 59 – Generative Models | Generative models synthesise data, compress signals, and enable controllable creativity. In this |
| 60 | Day 60 – Graph and Geometric Learning | Graph neural networks capture relational structure beyond Euclidean grids. This lesson focuses on: |
| 61 | Day 61 – Reinforcement and Offline Learning | Reinforcement learning (RL) balances exploration and exploitation while offline evaluation keeps |
| 62 | Day 62 – Model Interpretability and Fairness | Explainable and responsible AI practices underpin trustworthy analytics. After this lesson you will: |
| 63 | Day 63 – Causal Inference and Uplift Modeling | Understand how experimentation and counterfactual reasoning quantify impact. After this lesson you |
| 64 | Day 64 – Modern NLP Pipelines | Connect discrete NLP components into a reproducible workflow. After this lesson you will: |
| 65 | Day 65 – MLOps Pipelines and CI/CD Automation | Day 50 introduced model persistence. Day 65 expands that foundation into production-grade automation |
| 66 | Day 66 – Model Deployment and Serving Patterns | Production machine learning systems expose predictions through a variety of runtime patterns. |
| 67 | Day 67 – Model Monitoring and Reliability Engineering | The final instalment of the MLOps arc closes the loop from deployment to operations. After mastering |