Thank you for sending your enquiry! One of our team members will contact you shortly.
Thank you for sending your booking! One of our team members will contact you shortly.
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 (2)
examples and exercises
Kamil
Course - Introduction to Data Science and AI using Python
Machine Translated
All the information presented
