AI+ Prompt Engineer Level 1™

Target group

Research Scientists: Advance your research with AI by creating and utilizing effective prompts to explore new scientific data and solve complex problems.

Data Scientists & Analysts: Enhance your ability to optimize machine learning models by mastering prompt engineering for better data analysis and insights.

Developers & Programmers: Learn to build, refine, and deploy AI-driven applications by creating efficient prompts for improved AI system performance.

Business Leaders & Strategists: Gain the skills to incorporate AI solutions into business strategies, optimizing processes and decision-making.

Machine Learning Engineers: Strengthen your expertise by learning how to fine-tune AI prompts to enhance the performance of machine learning models.

Course structure

Course Overview

Course Introduction Preview

Module 1: Foundations of Artificial Intelligence (AI) and Prompt Engineering

1.1 Introduction to Artificial Intelligence Preview

1.2 History of AI Preview

1.3 Machine Learning Basics Preview

1.4 Deep Learning and Neural Networks

1.5 Natural Language Processing (NLP)

1.6 Prompt Engineering Fundamentals

Module 2: Principles of Effective Prompting

2.1 Introduction to the Principles of Effective PromptingPreview

2.2 Giving DirectionsPreview

2.3 Formatting ResponsesPreview

2.4 Providing Examples

2.5 Evaluating Response Quality

2.6 Dividing Labor

2.7 Applying The Five Principles

2.8 Fixing Failing Prompts

Module 3: Introduction to AI Tools and Models

3.1 Understanding AI Tools and Models Preview

3.2 Deep Dive into ChatGPT Preview

3.3 Exploring GPT-4 Preview

3.4 Revolutionizing Art with DALL-E 2

3.5 Introduction to Emerging Tools using GPT

3.6 Specialized AI Models

3.7 Advanced AI Models

3.8 Google AI Innovations

3.9 Comparative Analysis of AI Tools

3.10 Practical Application Scenarios

3.11 Harnessing AI’s Potential

Module 4: Mastering Prompt Engineering Techniques

4.1 Zero-Shot Prompting

4.2 Few-Shot Prompting

4.3 Chain-of-Thought Prompting

4.4 Ensuring Self-Consistency in AI Responses

4.5 Generate Knowledge Prompting

4.6 Prompt Chaining

4.7 Tree of Thoughts: Exploring Multiple Solutions

4.8 Retrieval Augmented Generation

4.9 Graph Prompting and Advanced Data Interpretation

4.10 Application in Practice: Real-Life Scenarios

4.11 Practical Exercises

Module 5: Mastering Image Model Techniques

5.1 Introduction to Image Models

5.2 Understanding Image Generation

5.3 Style Modifiers and Quality Boosters in Image Generation

5.4 Advanced Prompt Engineering in AI Image Generation

5.5 Prompt Rewriting for Image Models

5.6 Image Modification Techniques: Inpainting and Outpainting

5.7 Realistic Image Generation

5.8 Realistic Models and Consistent Characters

5.9 Practical Application of Image Model Techniques

Module 6: Project-Based Learning Session

6.1 Introduction to Project-Based Learning in AI

6.2 Selecting a Project Theme

6.3 Project Planning and Design in AI

6.4 AI Implementation and Prompt Engineering

6.5 Integrating Text and Image Models

6.6 Evaluation and Integration in AI Projects

6.7 Engaging and Effective Project Presentation

6.8 Guided Project Example

Module 7: Ethical Considerations and Future of AI

7.1 Introduction to AI Ethics

7.2 Bias and Fairness in AI Models

7.3 Privacy and Data Security in AI

7.4 The Imperative for Transparency in AI Operations

7.5 Sustainable AI Development: An Imperative for the Future

7.6 Ethical Scenario Analysis in AI: Navigating the Complex Landscape

7.7 Navigating the Complex Landscape of AI Regulations and Governance

7.8 Navigating the Regulatory Landscape: A Guide for AI Practitioners

7.9 Ethical Frameworks and Guidelines in AI Development

Optional Module: AI Agents for Prompt Engineering

1. What Are AI Agents

2. Applications and Trends of AI Agents for Prompt Engineers

3. How Does an AI Agent Work

4. Core Characteristics of AI Agents

5. Importance of AI Agents

6. Types of AI Agents

Certification

50 questions, 70% passing, 90 minutes, online proctored exam