Data Science and Machine learning in Python
Essentials for efficient AI
Recording available until December 22nd
DAYS
HOURS
MINUTES
SECONDS
Data science and machine learning in Python
Essentials for efficient AI
Recording available until December 22
DAYS
HOURS
MINUTES
SECONDS
What is it about?
A smarter way to learn Machine Learning (ML) and AI is to learn by doing. The AI and Machine Learning Essentials Workshop focuses on building up your practical skills so that you can understand how to develop simple models in Python or even build an advanced model with effective modern AI and machine learning methods. You’ll learn from real examples that lead to real results. Throughout The AI and machine Learning workshop, you’ll take an engaging step-by-step approach to understanding ML and data science. You won’t have to sit through any unnecessary theory.
Speakers
Dr. Neda Navidi
I have completed my Ph.D. in AI from École de Technologie Supérieure (ÉTS), and postdoctoral studies from McGill University, HEC Montréal, and Polytechnique Montréal. I have been a machine learning (ML) researcher, applied research scientist, and data scientist in different research teams at various companies. I am also an expert in deep learning, reinforcement learning, imitation learning, supervised / unsupervised learning, natural language processing, computer vision, and time-series data. I have been invited for several talks and oral/tutorial presentations as tutor and speaker in various summits, conferences and workshops related to machine learning. I now work as a professional AI researcher at ÉTS.
Dr. Majid Baa Wafaa
I have completed my Ph.D. in electrical engineering (optimization techniques and operational technology) from École de Technologie Supérieure (ÉTS), and postdoctoral studies from HEC Montréal, and ÉTS. I have been an optimization researcher and operational technology scientist in different research teams at various companies. I have both academic and industrial experiences in the field of operational technology from 2005. Although I enjoy all aspects of this field, my top four areas of research interest are optimization, AI application, system modeling and simulation. I now work as a system developer at OSI company.
Der zweite Teil ist weiterführenden Themen gewidmet. Zustandsverwaltung ist der erste Themenbereich, der anhand eines komplexen Beispiels illustriert wird. Zweitens geht es um SvelteKit, das die Rolle der wichtigsten Infrastruktur innehat: alles, was eine komplette Anwendung außer reiner Komponenten sonst noch braucht.
- Zustandsverwaltung: der reaktive Svelte-Weg
- Stores und Subscriptions
- Kontexte für geteilte Informationen – oder APIs!
- Rahmenwerk für die vollständige Anwendung: SvelteKit
- Routing
- Seiten und Endpunkte
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