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Duration 21 hours
Course Outline
Introduction to AI in Software Development
- Distinguishing between Generative AI and Predictive AI.
- Exploring AI applications in coding, analytics, and automation.
- An overview of LLMs, transformers, and deep learning architectures.
AI-Assisted Coding and Predictive Development
- Using AI-driven code completion and generation tools (such as GitHub Copilot and CodeGeeX).
- Identifying code bugs and security vulnerabilities prior to deployment.
- Automating code reviews and generating optimization suggestions.
Constructing Predictive Models for Software Apps
- Comprehending time-series forecasting and predictive analytics.
- Building AI models for demand forecasting and anomaly detection.
- Utilizing Python, Scikit-learn, and TensorFlow for predictive modeling tasks.
Generative AI for Text, Code, and Image Creation
- Interacting with GPT, LLaMA, and other major LLMs.
- Producing synthetic data, text summaries, and technical documentation.
- Generating AI-driven images and videos using diffusion models.
Deploying AI Models in Real-World Scenarios
- Hosting AI models via Hugging Face, AWS, and Google Cloud.
- Creating API-based AI services for business integration.
- Fine-tuning pre-trained AI models for specialized domain tasks.
Leveraging AI for Business Insights and Decisions
- Harnessing AI for business intelligence and customer analysis.
- Forecasting market trends and understanding consumer behavior.
- Streamlining workflow optimizations through AI automation.
Ethical AI and Development Best Practices
- Navigating ethical concerns in AI-assisted decision-making.
- Detecting bias and ensuring fairness within AI models.
- Adopting best practices for interpretable and responsible AI systems.
Practical Workshops and Case Studies
- Applying predictive analytics to a real-world dataset.
- Developing an AI-powered chatbot featuring text generation capabilities.
- Deploying an LLM-based application to automate processes.
Conclusion and Future Steps
- Recap of essential concepts and takeaways.
- Recommended AI tools and resources for continued learning.
- Closing Q&A session.
Requirements
- A solid grasp of fundamental software development principles.
- Proficiency in any programming language (Python is preferred).
- Knowledge of machine learning or AI basics (recommended but not mandatory).
Target Audience
- Software developers.
- AI/ML engineers.
- Technical team leaders.
- Product managers looking to integrate AI-powered solutions.
Testimonials (4)
Understanding of AI and LLM. How it works and ehat the watchouts and opportunities are!
Henk Jan Beltman - genuine business
Course - Advanced AI Concepts with Perplexity
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
