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      • Week 1 — Neural Networks and Deep Learning
      • Week 2 — Basics of Neural Network programming
      • Week 3 — One hidden layer Neural Networks
      • Week 4 — L Layers Deep Neural Networks
      • Week 1 — The ML project lifecycle
      • Week 2 — Key challenges in Modelling
      • Week 3 — Data
      • Week 1 — Collecting, labeling, and validating data
      • Week 2 — Feature Engineering
      • Week 3 — Data Journey
      • Week 1 — Hyperparameter tuning: searching for the best architecture
      • Week 2 — Dimensionality Reduction
      • Week 3 — High Performance Modeling
      • Week 4 — Model Performance Analysis
      • Week 1 — Intro to Model Serving and Infrastructure
      • Week 2 — Model Serving Architecture, Scaling Infrastructure and More.
      • Week 3 — ML Experiment Management and Workflow Automation & MLOps Methodology
      • Week 4 — Model monitoring, Logging & model decay, GDPR, and Privacy

Intro to ML in Production

Week 1 — The ML Project Lifecycle →
Week 2 — Key Challenges in Modelling →
Week 3 — Data →
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