Intelligent Applications Advanced

• 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

Intelligent Applications are advanced software systems that leverage artificial intelligence, machine learning, and data analytics to perform complex tasks, make predictions, and automate decision-making processes.

This instructor-led, live training (online or onsite) is aimed at intermediate-level to advanced-level data scientists, engineers, and AI practitioners who wish to master the intricacies of intelligent applications and leverage them to solve complex, real-world problems.

By the end of this training, participants will be able to:

• Implement and analyze deep learning architectures.
• Apply machine learning at scale in a distributed computing environment.
• Design and execute reinforcement learning models for decision-making.
• Develop sophisticated NLP systems for language understanding.
• Utilize computer vision techniques for image and video analysis.
• Address ethical considerations in the development and deployment of AI systems.

Format of the Course

• Interactive lecture and discussion.
• Lots of exercises and practice.
• Hands-on implementation in a live-lab environment.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

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