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Course Outline
Introduction to AI for Software Development
- What is Generative AI vs 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 before 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
- An understanding of basic software development concepts
- Experience with any programming language (Python recommended)
- Familiarity with machine learning or AI fundamentals (recommended but not required)
Audience
- Software developers
- AI/ML engineers
- Technical team leads
- Product managers interested in AI-powered applications
21 Hours
Testimonials (2)
Going over the various use cases and application of AI was helpful. I enjoyed the walkthrough of the various AI Agents.
Axel Schulz - CANARIE Inc
Course - Microsoft 365 Copilot: AI Productivity Across Word, Excel, PowerPoint, Outlook, and Teams
I liked that trainer had a lot of knowledge and shared it with us
