Course Outline
Introduction to AI for Software Development
- Distinguishing Generative AI from Predictive AI
- Applications of AI in coding, analytics, and automation
- Overview of LLMs, transformers, and deep learning architectures
AI-Assisted Coding and Predictive Development
- AI-powered code completion and generation (GitHub Copilot, CodeGeeX)
- Anticipating code bugs and vulnerabilities prior to deployment
- Automating code reviews and generating optimization suggestions
Building Predictive Models for Software Applications
- Understanding time-series forecasting and predictive analytics
- Implementing AI models for demand forecasting and anomaly detection
- Utilizing Python, Scikit-learn, and TensorFlow for predictive modeling
Generative AI for Text, Code, and Image Generation
- Working with GPT, LLaMA, and other large language models
- Generating synthetic data, text summaries, and documentation
- Creating AI-generated images and videos using diffusion models
Deploying AI Models in Real-World Applications
- Hosting AI models via Hugging Face, AWS, and Google Cloud
- Developing API-based AI services for business applications
- Fine-tuning pre-trained AI models for specialized tasks
AI for Predictive Business Insights and Decision-Making
- AI-driven business intelligence and customer analytics
- Forecasting market trends and consumer behavior
- Automating workflow optimizations with AI
Ethical AI and Best Practices in Development
- Ethical considerations in AI-assisted decision-making
- Detecting bias and ensuring fairness in AI models
- Best practices for interpretable and responsible AI deployment
Hands-On Workshops and Case Studies
- Implementing predictive analytics on real-world datasets
- Building an AI-powered chatbot with text generation capabilities
- Deploying an LLM-based application for automation
Summary and Next Steps
- Review of key takeaways
- AI tools and resources for continued learning
- Final Q&A session
Requirements
- A foundational understanding of software development concepts
- Experience with at least one programming language (Python is recommended)
- Familiarity with machine learning or AI fundamentals (recommended, though not mandatory)
Target Audience
- Software developers
- AI/ML engineers
- Technical team leads
- Product managers interested in developing AI-powered applications
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
