Dive into the world of deep belief networks (DBNs) and discover their significance. This course will teach you about restricted Boltzmann machines (RBMs) and DBNs, provide real-world data challenges, and expand your deep learning knowledge.

This course covers the strategic implementation of generative AI in business operations.

Using TigerGraph and some Python machine-learning, we show you how anyone can build a payment fraud system that halves the number of frauds and reduces the number of inappropriately blocked transactions.

Unleash ChatGPT's Power for Business Success

Learn dynamic programming techniques in Python to optimize algorithms and improve program efficiency, covering key concepts and practical examples.

Learn how to implement easy and accurate speech-to-text systems using Python. Explore libraries and techniques for converting audio into text for real-time transcription applications.

Our future is here and it relies on data. Medical robots, smart homes and cities, predictive policing, artificial intelligences – all are fuelled by data and all promise new benefits to society. But will these innovations benefit everyone? Who stands to gain and who is put at risk? How can we ensure that data is part of a just and sustainable world?

Learn to create AI applications with prompt engineering in this introductory online course. Go beyond AI basics, and learn technical and practical skills to harness the power of this emerging technology. Some experience with JavaScript, Node.js, and APIs is recommended.

This course provides a practical introduction to ChatGPT, from signing up to mastering its advanced features. Topics covered include conversing with ChatGPT, customizing it, using it for productivity and building chatbots, as well as advanced applications like language translation and generating creative content. Best practices and tips for using ChatGPT are also included.

Creating data models using machine learning requires effective training data. This course will teach you how to feed your data model’s training process using data labeling for supervised training and unlabeled data for semi-supervised training.

Explore efficient fine-tuning of neural networks using LoRA (Low-Rank Adaptation) and PyTorch. Learn how to apply LoRA for parameter-efficient training of complex models in real-world scenarios.

Explore I2C communication protocols and their applications in integrating sensors with embedded systems.