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