Overview

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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.

Syllabus

Optimize AI: Build & Evaluate Predictive Models: Train and Validate Predictive Models with Supervised and Unsupervised Algorithms

You will apply supervised and unsupervised algorithms to train predictive models using structured datasets. You will implement cross-validation techniques to validate model reliability and interpret results to ensure robust performance.

Optimize AI: Build & Evaluate Predictive Models: Improve Model Performance Through Metric-Driven Feature Engineering

You will evaluate model performance using accuracy and F1 metrics, identify weaknesses, and refine features systematically. You will iterate on feature engineering decisions to meet defined performance targets

Design and build custom neural networks.: Selecting the Right Neural Network Architecture

You will analyze candidate neural network topologies such as CNNs, RNNs, and Transformers. You will evaluate task requirements, data characteristics, and compute constraints to select the most appropriate architecture.

Design and build custom neural networks.: Building Custom Neural Network Architectures

You will create custom neural-network architectures by composing layers, activations, and regularization techniques. You will test architectural decisions to improve generalization and training stability.

Optimize Deep Learning Models for Peak AI: Transfer Learning Foundations

You will apply transfer-learning workflows to fine-tune pretrained models on domain-specific datasets. You will experiment with freezing and unfreezing layers to improve model adaptation.

Optimize Deep Learning Models for Peak AI: Evaluate Deep Model Configurations for Accuracy and Efficiency

You will evaluate deep model configurations by comparing accuracy, latency, and memory usage. You will balance performance and efficiency to determine the most suitable production-ready configuration.

Optimize and Benchmark AI Algorithms for Speed: Choosing Faster Approaches Using Complexity and Data Structures

You will analyze the computational complexity of algorithms and evaluate how data structures affect performance. You will select optimal approaches based on scalability and workload demands.

Optimize and Benchmark AI Algorithms for Speed: Prototype, Measure, and Benchmark Algorithms

You will create prototype algorithms and design structured benchmarks to measure latency, throughput, and memory usage. You will interpret benchmark results to evaluate performance trade-offs and justify implementation decisions.