Building and Optimizing AI Models

Building and Optimizing AI Models introduces the foundational engineering practices required to design, train, and optimize machine learning models for modern AI systems. In this course, you will explore statistical machine learning methods, neural network architectures, and deep learning optimization techniques used to develop high-performing predictive models. You will begin by applying supervised and unsupervised algorithms to train and evaluate predictive models. Next, you will design custom neural network architectures and experiment with different layer configurations to improve model accuracy and efficiency. The course also introduces transfer learning and deep learning optimization strategies that help adapt pretrained models to domain-specific tasks. Finally, you will analyze algorithm performance and benchmark model implementations to understand trade-offs between accuracy, latency, and computational cost. By the end of this course, you will be able to design neural networks, optimize deep learning workflows, and evaluate model performance using industry-standard metrics. Tools and technologies covered include Python, TensorFlow, neural network frameworks, and model performance benchmarking techniques

• Train and evaluate machine learning models with supervised and unsupervised algorithms
• Design custom neural network architectures
• Optimize deep learning models via transfer learning
• Benchmark model efficiency, accuracy, and computational cost

• Basic familiarity with machine learning concepts
• Willingness to engage in applied exercises