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Lessons > Regularization, Dropout & BatchNorm

Regularization, Dropout & BatchNorm

Course Content
Day 1
0/3
ML System Design & Architecture
Model Deployment Strategies
Hands-on: Design a Scalable ML Pipeline Blueprint
Day 2
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Ensemble Learning Techniques
Model Interpretability Tools
Hands-on: Build & Interpret an XGBoost Model on a Real Dataset
Day 3
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Model Optimization & Hyperparameter Tuning
Cross-Validation & Evaluation Techniques
Hands-on: Tune and Evaluate a LightGBM Model
Day 4
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Unsupervised Learning & Anomaly Detection
Dimensionality Reduction in Real Use Cases
Hands-on: Detect Anomalies in eCommerce Transactions
Day 5
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Time-Series Forecasting & ML in Finance
ML for Predictive Analytics
Hands-on: Build a Forecasting Model Using Facebook Prophet
Day 6
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Neural Networks Refresher & Architectures
Regularization, Dropout & BatchNorm
Hands-on: Build a Custom Image Classifier Using CNN (Keras/PyTorch)
Day 7
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Transformers & NLP Models
LLMs in Practice
Hands-on: Fine-Tune a BERT Model for Product Review Sentiment Analysis
Day 8
0/3
AI in Production: MLOps Tools & Workflow
Model Registry & Lifecycle Management
Hands-on: Track and Register a Model Using MLflow
Day 9
0/3
Generative AI Applications
Prompt Engineering + Fine-tuning
Hands-on: Build a Chatbot Using Open Source LLM + Your Dataset
Day 10
0/3
Capstone Day: AI for Your Domain
Presentation + Model Deployment Tips
Bonus: Access to Model Zoo + Case Study Library
Advanced AI & ML for Software Developers & Tech Professionals

Prevent overfitting and stabilize training in deep networks.

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