The course begins with an introduction to AI engineering, outlining the skills and knowledge required to succeed in the field. Learners are guided through hyperparameter tuning, model optimization, and practical approaches for building high-performance machine learning models. Early sections emphasize hands-on exercises, including grid search, Bayesian optimization, regularization, and cross-validation to ensure a solid foundation in AI model development. The curriculum then dives deep into deep learning architectures, starting with Convolutional Neural Networks (CNNs) for image processing and Recurrent Neural Networks (RNNs) for sequence modeling. Learners gain proficiency in building, training, and regularizing models using Keras, TensorFlow, and PyTorch, with real-world projects such as image classification, sentiment analysis, and text generation. The course further explores transformer models, including BERT and GPT, positional encoding, and NLP fine-tuning. The final modules focus on production-ready AI workflows, emphasizing transfer learning, AI agents, and MLOps. Learners explore deployment pipelines using Docker, Kubernetes, and cloud platforms such as AWS, GCP, and Azure. Practical exercises allow students to containerize and deploy models, manage AI agents, and operationalize machine learning solutions efficiently.