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Course Outline
Introduction to AI for Software Development
- Understanding the difference between Generative AI and Predictive AI
- Exploring AI applications 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)
- Predicting code bugs and vulnerabilities prior to deployment
- Automating code reviews and receiving optimization suggestions
Building Predictive Models for Software Applications
- Comprehending 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 solutions
- Fine-tuning pre-trained AI models for specialized 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
- Addressing ethical considerations in AI-assisted decision-making
- Detecting bias and ensuring fairness in AI models
- Establishing best practices for interpretable and responsible AI
Hands-On Workshops and Case Studies
- Implementing predictive analytics on a real-world dataset
- 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 solid understanding of basic software development principles
- Experience with any programming language (Python is recommended)
- Familiarity with machine learning or AI fundamentals (recommended but not mandatory)
Target Audience
- Software developers
- AI/ML engineers
- Technical team leads
- Product managers interested in AI-powered applications
21 Hours
Testimonials (2)
The interactive style, the exercises
Tamas Tutuntzisz
Course - Introduction to Prompt Engineering
A great repository of resources for future use, instructor's style (full of good sense of humor, great level of detail)
