Explore advanced machine learning techniques for retail analysis. Learn how to analyze customer data, optimize inventory, and enhance marketing strategies using AI-driven insights.

Learn how to use spaCy to build advanced natural language understanding systems, using both rule-based and machine learning approaches.

The course explains advanced RAG concepts, showcasing their implementation through LangChain for enhanced practical learning and application.

Learn how to use advanced topic modeling and named entity recognition (NER) for text analytics, covering the math and code behind topic modeling and NER algorithms (e.g., transformer-based) and how to integrate both in your workflows.

You’ve got a basic understanding of LLMs and may have built a proof of concept or two. This course will demonstrate deeper engineering techniques and help you build a strategy to assess and improve the fluency, relevancy, and precision of your LLMs.

Study adversarial search techniques in AI for developing competitive agents in games and strategic decision-making.

This session is dedicated to entreprenurs, CxO's (CIO, CTO, Chief Data Officer, Chief Digital Officer, CEO, L&D) where we explain the importance of AI and data strategy for enabling their top line business initiatives for driving growth.

Dive into the intersection of AI, ML, and biomedical sciences in this workshop for iBest 2024. Explore cutting-edge technologies transforming healthcare, research, and drug discovery.

In this course we will go over the principles that make AI ethical, and how those principles vary in the United States of America and the European Union.

Dive into the world of AI in biomedical applications. Learn how machine learning and AI are transforming healthcare, from diagnosis to drug development and personalized medicine.

Get a clear understanding of AI concepts in this comprehensive course. Explore machine learning, deep learning, and other AI fundamentals essential for modern technology.

The interplay between AI, cloud, and edge is a rapidly evolving domain. Currently, many IoT solutions are based on basic telemetry. The telemetry function captures data from edge devices and stores it in a data store. Our approach extends beyond basic telemetry. We aim to model problems in the real world through machine learning and deep learning algorithms and implement the model through AI and Cloud on to edge devices. The model is trained in the cloud and deployed on the edge device. The deployment to the edge provides a feedback loop to improve the business process (digital transformation).