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Duration 21 hours
Course Outline
Advanced Neural Networks
- Deep learning architectures
- Convolutional and recurrent neural networks
- Generative models and unsupervised learning
Scaling Machine Learning
- Big data analytics
- Distributed computing for ML
- Advanced optimization techniques
Reinforcement Learning & Decision Making
- Markov decision processes
- Policy gradient methods
- Multi-agent systems and game theory
Natural Language Processing & Understanding
- Advanced NLP techniques
- Sentiment analysis and text classification
- Language models and transformers
Computer Vision & Perception
- Image recognition and object detection
- Video analysis and action recognition
- 3D reconstruction and augmented reality
AI Ethics & 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 & Next Steps
Requirements
- A strong grasp of foundational AI and ML principles
- Proficiency in Python alongside familiarity with data science toolkits
- Successful completion of an introductory AI course or equivalent professional experience
Target Audience
- Data scientists
- Engineers
- AI practitioners
Testimonials (1)
Step by step training with a lot of exercises. It was like a workshop and I am very glad about that.
