AI+ Prompt Engineer Level 1™
Master AI Prompts: Elevate Your Engineering Skills Foundational Knowledge: Covers generative AI, ML, NLP, and neural networks essentials Hands-on Learning: Offers practical tr...
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Skills You’ll Gain
- 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.
- Understand AI basics and how AI is used – no technical skills required.
- Willingness to think creatively to generate ideas and use AI tools effectively.
Duration:90 minutes
Passing Score:70% (35/50)
Format:50 multiple-choice/multiple-response questions
Delivery Method: Online via proctored exam platform (flexible scheduling)
- Course Introduction
- 1.1 Introduction to Artificial Intelligence
- 1.2 History of AI
- 1.3 Machine Learning Basics
- 1.4 Deep Learning and Neural Networks
- 1.5 Natural Language Processing (NLP)
- 1.6 Prompt Engineering Fundamentals
- 2.1 Introduction to the Principles of Effective Prompting
- 2.2 Giving Directions
- 2.3 Formatting Responses
- 2.4 Providing Examples
- 2.5 Evaluating Response Quality
- 2.6 Dividing Labor
- 2.7 Applying The Five Principles
- 2.8 Fixing Failing Prompts
- 3.1 Understanding AI Tools and Models
- 3.2 Deep Dive into ChatGPT
- 3.3 Exploring GPT-4
- 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
- 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
- 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
- 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
- 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
- 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
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