Master the mathematical foundations, model architectures, and end-to-end operational pipelines (MLOps) required to design, train, and deploy production-grade intelligent systems. This course covers supervised and unsupervised learning algorithms, deep learning neural network architectures (CNNs, RNNs, and Transformers), statistical optimization techniques (gradient descent variants, loss function design, and regularization), automated feature engineering, and model evaluation metrics. You will develop the technical capabilities needed to build and fine-tune models using industry-standard frameworks (such as PyTorch, TensorFlow, and Scikit-learn), implement data preprocessing and distributed training workflows, containerize and serve models as low-latency microservices using Docker and FastAPI, establish CI/CD and MLOps pipelines for continuous model monitoring and drift detection, and optimize inference performance through model quantization and pruning for deployment across cloud and edge computing environments.