Understand and Apply AI Fundamentals with AWS
Keep adding new skills with 10,000+ programs for $239 (usually $399). Save now.
Understand and Apply AI Fundamentals with AWS
This course is part of Apply AI Foundations with Python and AWS Specialization
Included with
Ask Coursera
Recommended experience
Recommended experience
What you'll learn
Explain core AI, machine learning, and deep learning concepts and how AI systems work.
Analyze AI applications such as NLP, computer vision, generative AI, and ethical AI practices.
Evaluate and apply AWS AI services to design real-world, scalable AI solutions.
Skills you'll gain
- Model Training
- Convolutional Neural Networks
- Machine Learning
- Machine Learning Algorithms
- Artificial Neural Networks
- AI literacy
- Deep Learning
- Unsupervised Learning
- Machine Learning Methods
- Supervised Learning
- Artificial Intelligence
- Responsible AI
- Data Ethics
- Artificial Intelligence and Machine Learning (AI/ML)
- Natural Language Processing
- LLM Application
Tools you'll learn
Details to know
January 2026
24 assignments
See how employees at top companies are mastering in-demand skills
Build your subject-matter expertise
- 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
There are 6 modules in this course
By the end of this course, learners will be able to explain core artificial intelligence concepts, analyze machine learning approaches, apply deep learning principles, and evaluate AI solutions using AWS services.
This comprehensive course is designed to build strong foundations in AI fundamentals and core concepts, progressing from classical AI techniques and machine learning to deep learning, generative models, and real-world AI applications. Learners will gain a clear understanding of how AI systems reason, learn, and make decisions, supported by practical insights into NLP, computer vision, reinforcement learning, and ethical AI practices. What makes this course unique is its balanced blend of theory, practice, and cloud-based implementation. It not only explains how AI works, but also how AI is applied at scale using AWS AI services such as SageMaker, Lex, Polly, Rekognition, and foundation models. Structured modules, lesson-wise objectives, practice quizzes, and graded assessments ensure progressive mastery and exam readiness. This course is ideal for beginners, aspiring AI practitioners, and professionals preparing for the AWS Certified AI Practitioner exam, enabling learners to confidently apply AI concepts to real-world business and industry use cases.
This module introduces the fundamental concepts of Artificial Intelligence, including its history, importance, ethical considerations, and the logical and statistical foundations that support intelligent systems.
What's included
7 videos4 assignments
7 videos•Total 42 minutes
- Definition and Brief History of AI Part 1•8 minutes
- Definition and Brief History of AI Part 2•8 minutes
- Importance and Applications of AI•3 minutes
- AI Ethics and Societal Impacts•10 minutes
- Introduction•2 minutes
- Logic and Reasoning•7 minutes
- Probability and Statistics•5 minutes
4 assignments•Total 60 minutes
- Graded-Foundations of Artificial Intelligence•30 minutes
- Understanding Artificial Intelligence•10 minutes
- AI in Society•10 minutes
- Core Reasoning Foundations•10 minutes
This module explores classical AI techniques such as search algorithms, knowledge representation, and foundational machine learning approaches, focusing on how machines solve problems and identify patterns.
What's included
6 videos4 assignments
6 videos•Total 33 minutes
- Search Algorithms•3 minutes
- Knowledge Representation and Reasoning•5 minutes
- Introduction to Machine Learning AI•4 minutes
- Supervised Learning•6 minutes
- Unsupervised Learning•6 minutes
- Clustering•9 minutes
4 assignments•Total 60 minutes
- Graded-Classical AI and Problem-Solving•30 minutes
- Search and Knowledge Representation•10 minutes
- Machine Learning Overview•10 minutes
- Unsupervised Learning Fundamentals•10 minutes
This module provides an in-depth understanding of core machine learning techniques, including similarity measures, dimensionality reduction, pattern discovery, and reinforcement learning methods.
What's included
8 videos4 assignments
8 videos•Total 70 minutes
- Distance Measures•5 minutes
- Dimensionality Reductio Part 1•6 minutes
- Dimensionality Reductio Part 2•8 minutes
- Association Rule Learning•12 minutes
- Reinforcement Learning•11 minutes
- Types of Reinforcement Learning Part 1•13 minutes
- Types of Reinforcement Learning Part 2.1•6 minutes
- Types of Reinforcement Learning Part 2.2•9 minutes
4 assignments•Total 60 minutes
- Graded-Machine Learning Techniques•30 minutes
- Data Similarity and Reduction•10 minutes
- Pattern Discovery•10 minutes
- Reinforcement Learning Types•10 minutes
This module introduces deep learning concepts, neural network architectures, and advanced AI paradigms such as generative models and transfer learning used in modern AI applications.
What's included
11 videos4 assignments
11 videos•Total 84 minutes
- Neural Networks Basics Part 1•6 minutes
- Neural Networks Basics Part 2•9 minutes
- Deep Learning Introduction•8 minutes
- Convolutional Neural Networks (CNNs) Part 1•6 minutes
- Convolutional Neural Networks (CNNs) Part 2•8 minutes
- Recurrent Neural Networks (RNN) Part 1•6 minutes
- Recurrent Neural Networks (RNN) Part 2•7 minutes
- Recurrent Neural Networks (RNN) Part 3•7 minutes
- Generative Models Part 1•7 minutes
- Generative Models Part 2•12 minutes
- Transfer Learning and Fine Tuning•9 minutes
4 assignments•Total 60 minutes
- Graded-Deep Learning and Advanced AI•30 minutes
- Neural Network Foundations•10 minutes
- Specialized Neural Architectures•10 minutes
- Modern Learning Paradigms•10 minutes
This module focuses on practical AI applications across industries and introduces AWS AI services, enabling learners to understand how AI solutions are deployed at scale.
What's included
8 videos4 assignments
8 videos•Total 12 minutes
- Introduction to AWS Certified AI Practitioner•0 minutes
- Understanding AI and ML•3 minutes
- Natural Language Processing (NLP)•1 minute
- Computer Vison (CV)•2 minutes
- Applications of AI in Various Industries•2 minutes
- Supervised vs Unsupervised Machine Learning•1 minute
- Algorithms of Supervised and Unsupervised Machine Learning•2 minutes
- Reinforcement Learning (RL)•1 minute
4 assignments•Total 60 minutes
- Graded-AI Applications and AWS AI Services•30 minutes
- AI in Practice•10 minutes
- AI Domains and Industry Use•10 minutes
- Machine Learning Comparisons•10 minutes
This module covers the end-to-end AI lifecycle, including model building, deployment, ethical best practices, AWS implementation, prompt engineering, and exam readiness.
What's included
27 videos4 assignments
27 videos•Total 92 minutes
- Principal Component Analysis (PCA)•1 minute
- Basic Questions•9 minutes
- Introduction to AWS AI Services•0 minutes
- Amazon SageMaker•4 minutes
- Aws DeepLens•3 minutes
- Amazon Comprehend•3 minutes
- Case Studies•2 minutes
- Intermediate Questions Part 1•7 minutes
- Intermediate Questions Part 2•8 minutes
- Implementing AI Solutions with AWS•4 minutes
- Woring with Amazon SageMaker•6 minutes
- Using AWS Lex•2 minutes
- Using AWS Polly•1 minute
- AWS Rekognition•1 minute
- Combining AWS Services•2 minutes
- Understanding Foundation Models•1 minute
- Model Selection and Architecture•1 minute
- Data Preperation and Preprocessing•1 minute
- Model Training and Optimization•1 minute
- Model Evaluation and Deployment•1 minute
- Summary•0 minutes
- Ethical Considerations and Best Practices in AI-ML•3 minutes
- Introduction to Prompt Engineering•6 minutes
- Continuous Improvement•0 minutes
- Exam Overview•4 minutes
- Advanced Questions Part 1•9 minutes
- Advanced Questions Part 2•8 minutes
4 assignments•Total 60 minutes
- Graded-Building, Deploying, and Mastering AI Solutions•30 minutes
- AWS AI Services Essentials•10 minutes
- AWS AI Tools and Implementation•10 minutes
- Deployment, Ethics, and Exam Readiness•10 minutes
Earn a career certificate
Add this credential to your LinkedIn profile, resume, or CV. Share it on social media and in your performance review.
Explore more from Machine Learning
- Status: Free TrialA
Amazon Web Services
Course
- Status: Preview
Course
- Status: PreviewK
KodeKloud
Course
- Status: FreeA
Amazon Web Services
Course
Why people choose Coursera for their career
Frequently asked questions
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.
When you enroll in the course, you get access to all of the courses in the Specialization, 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.
Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.
More questions
Financial aid available,