Expert in Data Science and ai – Implement Machine Learning Into Praxis
Don't just follow the trend - apply AI in practice! The recent wave of innovation in Artificial intelligence (AI) has enormous disruptive potential, but there is a decided shortage of professionals capable of harnessing the power of the latest modelling techniques and moving AI from the drawing board into real life.
Solid foundation in data modelling The programme Certified Expert in Data Science and AI aims to give practitioners a solid foundation in data modelling, as well as an in-depth understanding of the latest AI methods and solutions (especially Deep Learning). The course covers the theoretical foundations of statistical modelling, the detailed analysis of neural models – along with associated machine learning procedures – and includes a technical introduction to and practice in Python programming using the general “Data Science Stack” (Numpy, SciPy, Pandas, Scikit-Learn), as well as TensorFlow for Deep Learning.
Latest AI methods and solutions After successfully finishing the course, practitioners will not only be familiar with the state of the art in AI, they will also be capable of implementing the latest machine-learning models in practice.
The course is conducted by two trainers who are also available as points of contact throughout the on-campus study period, ensuring that you receive the highest possible standard of mentoring and guidance. Please note that the two trainers are specialists in different areas of expertise, meaning that each will contribute his or her own perspective to the learning process.
The programme Certified Expert Data Science and Artificial Intelligence consists of two 5-day weeks which are divided by a weekend. The course will take the form of interactive “lab” sessions during which participants will implement solutions (under guidance). Passing the automatically evaluated programming assignments is a necessary prerequisite for certification.
Data Science an Modelling Foundations (Week 1)
- Foundational definitions, historical overview
- Task settings, tasks of AI models
- Data science pipeline
- Visualization, representation and embedding
- Clustering methods, anomaly detection models, classification methods
- Regression, metrics, measurements of models
Neural Networks and Deep Learning (Week 2)
- Training and setup for neural networks
- Neural network basics
- Current neural architectures and their application
- Memory networks, unsupervised learning with neural models, transfer learning
- Peak into what else is there?
- Advice on deployment of Machine Learning models
Who should attend
Programmers, data scientists and business analysts, as well as individuals with experience in programming and/or data analysis seeking an in-depth understanding of and practical exposure to the latest AI technologies (especially Deep Learning)