Course Outline
Introduction to AI in Software Development
- Distinguishing between Generative AI and Predictive AI.
- The role of AI in coding, analytics, and automation.
- An overview of LLMs, transformers, and deep learning models.
AI-Assisted Coding and Predictive Development
- Utilizing AI for code completion and generation (e.g., GitHub Copilot, CodeGeeX).
- Predicting code bugs and vulnerabilities prior to deployment.
- Automating code reviews and generating optimization suggestions.
Constructing Predictive Models for Software Applications
- Comprehending 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 Creation
- Working with GPT, LLaMA, and other Large Language Models.
- Generating synthetic data, text summaries, and technical documentation.
- Producing AI-generated images and videos using diffusion models.
Deploying AI Models in Real-World Scenarios
- Hosting AI models via Hugging Face, AWS, and Google Cloud.
- Building API-based AI services for business use cases.
- Fine-tuning pre-trained AI models for domain-specific tasks.
AI for Predictive Business Insights and Decision-Making
- Leveraging AI for 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.
Hands-On Workshops and Case Studies
- Applying predictive analytics to real-world datasets.
- Developing an AI-powered chatbot with text generation capabilities.
- Deploying an LLM-based application for automation tasks.
Summary and Next Steps
- Recap of key learning outcomes.
- AI tools and resources for continued learning.
- Final Q&A session.
Requirements
- A solid understanding of fundamental software development concepts.
- Proficiency in any programming language (Python is recommended).
- Knowledge of machine learning or AI basics (recommended, though not mandatory).
Target Audience
- Software developers.
- AI/ML engineers.
- Technical team leads.
- Product managers with an interest in 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
