What will I learn in the Building and Optimizing AI Models course?

In this course, you will learn how to design, train, and optimize machine learning and deep learning models. You will explore neural network architectures, predictive modeling techniques, and performance optimization strategies used in modern AI systems.

Do I need prior machine learning experience to take Building and Optimizing AI Models?

Yes. This course is designed for learners with basic knowledge of Python programming and machine learning concepts. Familiarity with regression, classification, and neural network fundamentals will help you succeed in this course.

What tools and technologies will I use in the Building and Optimizing AI Models course?

You will work with Python and deep learning frameworks such as TensorFlow while learning techniques for neural network design, model evaluation, and performance benchmarking used in modern AI development.

When will I have access to the lectures and assignments?

To access the course materials, assignments and to earn a Certificate, you will need to purchase the Certificate experience when you enroll in a course. You can try a Free Trial instead, or apply for Financial Aid. The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.

What will I get if I subscribe to this Certificate?

When you enroll in the course, you get access to all of the courses in the Certificate, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile.

Building and Optimizing AI Models

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Building and Optimizing AI Models

This course is part of Transformers Unleashed: Master the Architecture of Modern AI Professional Certificate

Instructor: Professionals from the Industry

Included with

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8 modules

Gain insight into a topic and learn the fundamentals.

Intermediate level

Recommended experience

8 hours to complete

Flexible schedule

Learn at your own pace

8 modules

Gain insight into a topic and learn the fundamentals.

Intermediate level

Recommended experience

8 hours to complete

Flexible schedule

Learn at your own pace

What you'll learn

Train and evaluate predictive machine learning models using supervised and unsupervised algorithms
Design custom neural network architectures for AI applications
Optimize deep learning models using transfer learning and performance tuning
Benchmark AI algorithms to evaluate efficiency, accuracy, and computational cost

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Build your Machine Learning expertise

This course is part of the Transformers Unleashed: Master the Architecture of Modern AI Professional Certificate

When you enroll in this course, you'll also be enrolled in this Professional Certificate.

Learn new concepts from industry experts
Gain a foundational understanding of a subject or tool
Develop job-relevant skills with hands-on projects
Earn a shareable career certificate from Coursera

There are 8 modules in this course

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.

Module details

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.

What's included

3 videos1 reading2 assignments

3 videosTotal 17 minutes

Welcome and What You’ll Learn4 minutes
Supervised vs. Unsupervised Modeling: When to Use Each5 minutes
Walkthrough: Training Logistic Regression and K-Means in scikit-learn8 minutes

1 readingTotal 10 minutes

How Cross-Validation Improves Model Reliability10 minutes

2 assignmentsTotal 22 minutes

Hands-On Activity: Train Two Models and Run 5-Fold CV15 minutes
Practice Quiz: Model Fit Check7 minutes

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

What's included

3 videos1 reading3 assignments

3 videosTotal 15 minutes

Why Metrics Drive Better Modeling4 minutes
Interpreting Accuracy, Precision, Recall, and F17 minutes
Demo: Interaction Features Improve F14 minutes

1 readingTotal 10 minutes

Feature Engineering Fundamentals: Transform, Combine, Improve10 minutes

3 assignmentsTotal 42 minutes

Graded Quiz: Build, Validate, and Improve a Predictive Model20 minutes
Hands-On Activity: Improve a Model’s F1 Score with New Features15 minutes
Practice Quiz: Fix the Model7 minutes

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.

What's included

3 videos1 reading2 assignments

3 videosTotal 8 minutes

Welcome and Why Architecture Choices Matter2 minutes
Comparing Neural Network Topologies3 minutes
How to Evaluate Architecture Fit in Practice3 minutes

1 readingTotal 10 minutes

Understanding Task, Data, and Compute Constraints10 minutes

2 assignmentsTotal 22 minutes

Hands-on Activity: Choose the Best Architecture Under Real Constraints15 minutes
Practice Quiz: Architecture Selection Mini-Review7 minutes

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.

What's included

3 videos1 reading3 assignments

3 videosTotal 9 minutes

Why Build Custom Architectures2 minutes
Layers, Activations, and Regularization2 minutes
Screencast: Constructing a Custom Model in PyTorch5 minutes

1 readingTotal 10 minutes

Designing a Custom Network Step by Step10 minutes

3 assignmentsTotal 42 minutes

Graded Assessment: Custom Neural Network Architechture Evaluation20 minutes
Hands-on Activity: Build Your Own Network Architecture15 minutes
Practice Quiz: Improve a Baseline Model With Regularization7 minutes

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.

What's included

3 videos1 reading2 assignments

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.

What's included

3 videos1 reading3 assignments

3 videosTotal 17 minutes

Accuracy vs. Efficiency: The Real Trade-Offs6 minutes
Hyperparameter Sweeps: Comparing Configurations Fairly (Optuna Example)5 minutes
Quantization as a Configuration Choice: Speed vs. Accuracy (TensorRT Example)6 minutes

1 readingTotal 10 minutes

Practical Model Training Tips for Reliable Machine Learning Performance10 minutes

3 assignmentsTotal 40 minutes

Graded Assessment: Model Optimization Decision Challenge20 minutes
Hands-On Activity: Run a Mini Optimization Comparison15 minutes
Practice Quiz: Evaluating Model Performance Trade-Offs5 minutes

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.

What's included

3 videos1 reading2 assignments

3 videosTotal 15 minutes

Welcome and Why Speed Matters in Real AI Systems4 minutes
Understanding Complexity: From Big-O to Practical Speed5 minutes
Hidden Costs: Constants, Cache Effects, and Real-World Slowdowns6 minutes

1 readingTotal 10 minutes

Data Structures That Scale: Trees, Hash Maps, and Heaps10 minutes

2 assignmentsTotal 20 minutes

Hands-On Activity: Complexity Match-Up: Predict the Faster Method10 minutes
Practice Quiz: Test Your Complexity and Data Structure Skills10 minutes

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.