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Course Outline
Advanced Neural Networks
- Deep learning architectures
- Convolutional and recurrent neural networks
- Generative models and unsupervised learning
Machine Learning at Scale
- Big data analytics
- Distributed computing for ML
- Advanced optimization techniques
Reinforcement Learning and Decision Making
- Markov decision processes
- Policy gradient methods
- Multi-agent systems and game theory
Natural Language Processing and Understanding
- Advanced NLP techniques
- Sentiment analysis and text classification
- Language models and transformers
Computer Vision and Perception
- Image recognition and object detection
- Video analysis and action recognition
- 3D reconstruction and augmented reality
AI Ethics and Society
- Bias and fairness in AI systems
- AI governance and policy
- Future societal impacts of AI
Lab Project
- Implementing advanced ML models
- Analyzing large datasets
- Collaborating on a group research project
Summary and Next Steps
Requirements
- A solid understanding of basic AI and ML concepts
- Proficiency in Python and familiarity with data science toolkits
- Completion of an introductory course in AI or equivalent experience
Audience
- Data scientists
- Engineers
- AI practitioners
21 Hours
Testimonials (3)
examples and exercises
Kamil
Course - Introduction to Data Science and AI using Python
Machine Translated
All the information presented
Jose Victor - si
Course - Artificial Intelligence (AI) for Managers
The training definitely backfilled some of the gaps in my knowledge left by reading the OptaPlanner userguide. It gave me a good broad understanding of how to approach using OptaPlanner in our projects going forward.
