Month 1–2: Core ML
Learn Python + ML libraries (NumPy, Pandas, Scikit‑Learn).
Practice regression, classification, clustering.
Small projects: housing price prediction, customer segmentation.
📅 Month 3–4: Deep Learning
Learn PyTorch/TensorFlow basics.
Build CNNs (images), RNNs/Transformers (text).
Projects: MNIST digit classifier, sentiment analysis with BERT.
📅 Month 5: Deployment & MLOps
Serve models with FastAPI/Flask.
Containerize with Docker.
Automate with CI/CD + monitor with Prometheus/Grafana.
Project: Deploy a sentiment API.
📅 Month 6: Specialization
Pick a focus: CV, NLP, or RL.
Explore pretrained models + fine‑tuning.
Learn distributed training + cloud ML platforms.
Capstone: End‑to‑end ML pipeline (data → training → deployment → monitoring).
