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 Specialization?

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.

Is financial aid available?

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.

Understand and Apply AI Fundamentals with AWS

Ends tomorrow! Save on skills that make you shine with 40% off 3 months of Coursera Plus. Save now

Understand and Apply AI Fundamentals with AWS

This course is part of Apply AI Foundations with Python and AWS Specialization

Instructor: EDUCBA

Included with

Learn more

6 modules

Gain insight into a topic and learn the fundamentals.

Beginner level

Recommended experience

1 week to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

6 modules

Gain insight into a topic and learn the fundamentals.

Beginner level

Recommended experience

1 week to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

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

Tools you'll learn

Details to know

Shareable certificate

Add to your LinkedIn profile

See how employees at top companies are mastering in-demand skills

Learn more about Coursera for Business

logos of Petrobras, TATA, Danone, Capgemini, P&G and L'Oreal

Build your subject-matter expertise

This course is part of the Apply AI Foundations with Python and AWS Specialization

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

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.

Module details

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 videosTotal 42 minutes

Definition and Brief History of AI Part 18 minutes
Definition and Brief History of AI Part 28 minutes
Importance and Applications of AI3 minutes
AI Ethics and Societal Impacts10 minutes
Introduction2 minutes
Logic and Reasoning7 minutes
Probability and Statistics5 minutes

4 assignmentsTotal 60 minutes

Understanding Artificial Intelligence10 minutes
AI in Society10 minutes
Core Reasoning Foundations10 minutes
Graded-Foundations of Artificial Intelligence30 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 videosTotal 33 minutes

Search Algorithms3 minutes
Knowledge Representation and Reasoning5 minutes
Introduction to Machine Learning AI4 minutes
Supervised Learning6 minutes
Unsupervised Learning6 minutes
Clustering9 minutes

4 assignmentsTotal 60 minutes

Search and Knowledge Representation10 minutes
Machine Learning Overview10 minutes
Unsupervised Learning Fundamentals10 minutes
Graded-Classical AI and Problem-Solving30 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 videosTotal 70 minutes

Distance Measures5 minutes
Dimensionality Reductio Part 16 minutes
Dimensionality Reductio Part 28 minutes
Association Rule Learning12 minutes
Reinforcement Learning11 minutes
Types of Reinforcement Learning Part 113 minutes
Types of Reinforcement Learning Part 2.16 minutes
Types of Reinforcement Learning Part 2.29 minutes

4 assignmentsTotal 60 minutes

Data Similarity and Reduction10 minutes
Pattern Discovery10 minutes
Reinforcement Learning Types10 minutes
Graded-Machine Learning Techniques30 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 videosTotal 84 minutes

Neural Networks Basics Part 16 minutes
Neural Networks Basics Part 29 minutes
Deep Learning Introduction8 minutes
Convolutional Neural Networks (CNNs) Part 16 minutes
Convolutional Neural Networks (CNNs) Part 28 minutes
Recurrent Neural Networks (RNN) Part 16 minutes
Recurrent Neural Networks (RNN) Part 27 minutes
Recurrent Neural Networks (RNN) Part 37 minutes
Generative Models Part 17 minutes
Generative Models Part 212 minutes
Transfer Learning and Fine Tuning9 minutes

4 assignmentsTotal 60 minutes

Neural Network Foundations10 minutes
Specialized Neural Architectures10 minutes
Modern Learning Paradigms10 minutes
Graded-Deep Learning and Advanced AI30 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 videosTotal 12 minutes

Introduction to AWS Certified AI Practitioner0 minutes
Understanding AI and ML3 minutes
Natural Language Processing (NLP)1 minute
Computer Vison (CV)2 minutes
Applications of AI in Various Industries2 minutes
Supervised vs Unsupervised Machine Learning1 minute
Algorithms of Supervised and Unsupervised Machine Learning2 minutes
Reinforcement Learning (RL)1 minute

4 assignmentsTotal 60 minutes

AI in Practice10 minutes
AI Domains and Industry Use10 minutes
Machine Learning Comparisons10 minutes
Graded-AI Applications and AWS AI Services30 minutes

This module covers the end-to-end AI lifecycle, including model building, deployment, ethical best practices, AWS implementation, prompt engineering, and exam readiness.