Build and deploy machine learning models, work with neural networks, and create AI-powered applications with real-world datasets.
Build an end-to-end ML pipeline from data collection to model deployment.
Supervised/unsupervised learning, model evaluation
Data preprocessing, feature selection, dimensionality reduction
Linear models, tree-based methods, ensemble techniques
Perceptrons, backpropagation, activation functions
Building and training neural networks
Image classification, object detection basics
Text processing, sentiment analysis, LLM basics
Hyperparameter tuning, regularization, performance optimization
Model versioning, experiment tracking, CI/CD for ML
End-to-end ML project implementation
API development, cloud deployment, monitoring
Documentation, demo, portfolio packaging
Students with Python basics and some ML fundamentals
Bi-weekly mentor sessions, code reviews, and project guidance
Refund available if requested 48 hours before start. View policy
