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The Essential AI course provides essential coverage of predictive AI and generative AI concepts, benefits, challenges and risks.
It is suitable for IT and business professionals that would like to receive a fundamental understanding of how contemporary AI works and how it can be applied in the real world.
Upon completing the course you will receive a digital certificate of completion, as well as a digital training badge from Acclaim/Credly. Upon getting certified you will also receive an official AI Professional digital accreditation certificate and certification badge from Acclaim/Credly, along with an account that can be used to verify your certification status.
The Essential AI course is comprised of the following 2 course modules, each of which has an estimated completion time of 10 hours:
Module 1: Fundamental Predictive AI
Module 4: Fundamental Generative AI
20 hours of Workbook Lessons & Exercises
Supporting Video Lessons
Course Completion Certificates & Badges
Certification Exam & Practice Questions
Feature-Rich eLearning Platform
Interactive Graded Exercises, Self-Test
Printable PDFs
Lifetime Access
Mind Map Poster
Symbol Legend Poster
Lab Exercise Booklet (if applicable)
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This course module illustrates how predictive AI can be used and applied in a range of business applications, as well as essential coverage of predictive AI practices and systems. The module explores the most common learning approaches and functional areas that AI systems are used for. All of the content is authored in easy-to-understand, plain English.
Predictive AI Business and Technology Drivers
Predictive AI Benefits, Common Risks and Challenges of Using Predictive AI
Business Problem Categories Addressed by AI
Types of Predictive AI
Common Predictive AI Learning Approaches
Understanding Predictive AI Learning and Model Training
Step-by-Step Training Loop Process
Supervised Learning, Unsupervised Learning, Continuous Learning
Heuristic Learning, Semi-Supervised Learning, Reinforcement Learning
Common Predictive AI Functional Designs, Computer Vision, Pattern Recognition
Robotics, Natural Language Processing (NLP)
Speech Recognition, Natural Language Understanding (NLU)
Understanding AI Models and Neural Networks
This course module explores a range of the most important and relevant technology-related topics that pertain to contemporary cloud computing platforms. The module content does not get into implementation or programming details, but instead keeps coverage at a conceptual level, focusing on topics that address cloud service architecture, cloud security threats and technologies, virtualization and containerization.
Cloud Computing Mechanisms that Establish Architectural Building Blocks
Virtual Servers, Containers, Ready-Made Environments, Failover Systems and Pay-Per-Use Monitors
Automated Scaling Listeners, Multi-Device Brokers and Resource Replication
Understanding How Individual Cloud Computing Mechanisms Support Cloud Characteristics
An Introduction to Containerization, Container Hosting and Logical Pod Containers
A Comparison of Containerization and Virtualization
Cloud Balancing and Cloud Bursting Architectures
Common Risks, Threats and Vulnerabilities of Cloud-based Services and Cloud-hosted Solutions
Cloud Security Mechanisms used to Counter Threats and Attacks
Understanding Cloud-Based Security Groups and Hardened Virtual Server Images
Cloud Service Implementation Mediums (including Web Services and REST Services)
Cloud Storage Benefits and Challenges, Cloud Storage Services, Technologies and Approaches
Non-Relational (NoSQL) Storage Compared to Relational Storage
Cloud Service Testing Considerations and Testing Types
Service Grids and Autonomic Computing
Cloud Computing Industry Standards Organizations
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