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Course Outline
Introduction to AI for Software Development
- Distinguishing between Generative AI and Predictive AI.
- 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 (e.g., 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.
- Constructing API-based AI services for business solutions.
- Fine-tuning pre-trained AI models for specialized, domain-specific 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 purposes.
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).
Audience
- Software developers.
- AI/ML engineers.
- Technical team leads.
- Product managers interested in integrating 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)
