Course Outline
AI Foundations for Software Development
- Distinguishing between Generative AI and Predictive AI
- The role of AI in coding, analytics, and automation workflows
- A broad look at LLMs, transformers, and deep learning architectures
AI-Enhanced Coding and Predictive Development
- AI-driven code completion and synthesis (e.g., GitHub Copilot, CodeGeeX)
- Anticipating code bugs and vulnerabilities prior to deployment
- Streamlining code reviews and generating optimization insights
Constructing Predictive Models for Software Solutions
- Exploring time-series forecasting and predictive analytics concepts
- Integrating AI models for demand prediction and anomaly detection
- Leveraging Python, Scikit-learn, and TensorFlow for predictive modeling tasks
Generative AI for Text, Code, and Visuals
- Interacting with GPT, LLaMA, and other major LLMs
- Producing synthetic data, text abstracts, and automated documentation
- Generating AI-driven images and videos via diffusion models
Implementing AI Models in Production Environments
- Hosting AI models on platforms like Hugging Face, AWS, and Google Cloud
- Designing API-centric AI services for enterprise applications
- Fine-tuning pre-trained AI models for specific domain requirements
AI for Strategic Business Insights and Decisions
- AI-enhanced business intelligence and customer analysis
- Forecasting market shifts and consumer behaviors
- Driving workflow efficiency improvements through AI
Ethical AI and Responsible Development Practices
- Navigating ethical challenges in AI-assisted decision processes
- Identifying bias and ensuring fairness in AI models
- Adopting best practices for interpretable and responsible AI usage
Practical Workshops and Case Analysis
- Applying predictive analytics to real-world datasets
- Developing an AI-driven chatbot featuring text generation
- Rolling out an LLM-based application for automation purposes
Conclusion and Future Pathways
- Recap of critical learning outcomes
- Resources and tools for continuing AI education
- Closing Q&A session
Requirements
- Solid grasp of fundamental software development concepts
- Proficiency in at least one programming language (Python is advised)
- Basic knowledge of machine learning or AI principles (advocated but not mandatory)
Target Audience
- Software engineers
- AI/ML specialists
- Technical team leads
- Product managers seeking to integrate AI capabilities
Testimonials (3)
Comprehensiveness about the backround; how to do a prompt.
Olaf Kannt - Helios Klinikum Krefeld
Course - Generative AI and Prompt Engineering in Healthcare
the tips and recommended prompts that we can take away from this training
Lee Mei Lin - ST Engineering IHQ Pte Ltd
Course - InVideo AI: Creating Engaging Short-Form Videos
use of proper and effective prompt
