Course
digicode: H36105
Master Class Machine Learning Engineer
Course facts
Download as PDF- Gaining a deep understanding of data processing, algorithms, and data models, and establishing yourself as an expert in complex data analysis
- Learning how to build, monitor, and manage machine learning pipelines and confidently apply the relevant tools and infrastructure
- Combining business and data knowledge with IT expertise and software development skills to successfully implement AI projects
- Gaining qualifications in a high-demand field and positioning yourself as a driver of digital transformation within the company
The Master Class consists of the following four courses, which build on one another, as well as an exam:
1 Machine Learning and Data Mining: Concepts, Models, Learning Methods
- Machine learning and data mining
- Data understanding and data preparation
- Modeling and evaluation
- An overview of deep learning in neural networks
2 Deep Learning and Neural Networks with Python, Pandas, Keras, and TensorFlow
- Introduction to deep learning
- Data preparation and feature extraction
- Specialized neural networks
- Deploying models and transfer learning
3 Natural Language Processing and Generative Models with Python and Transformers
- Python techniques for text processing
- Introduction to Natural Language Processing (NLP)
- Text classification and text analysis
- Topic modeling and Long Short-Term Memory
- Transformers and attention
- Transfer learning and fine-tuning with Hugging Face
- Practical project: training your own chatbot
4 AI systems with your own data: Retrieval-Augmented Generation (RAG) with LLM
- Introduction and basics
- Basics of Retrieval-Augmented Generation
- Chunking and embeddings in practice
- Retrieval, reranking, and generation
- Evaluation and optimization
- Deployment and MLOps
- Monitoring and drift
5 Exam: Master Class Machine Learning Engineer
- E-exam
- Practical exam
Consists of the following modules
Your training is designed as a varied, part-time learning journey. Live training sessions with experienced experts are combined with digital self-study modules, hands-on exercises, and interaction within the learning community. This allows you to flexibly build new knowledge, deepen your understanding together with others, and directly apply what you’ve learned to specific challenges in your day-to-day work. Practical coaching sessions help you apply what you’ve learned effectively within your company. Your learning journey lasts approximately three months.
Do you have a technical or analytical background and want to learn machine learning in a focused way? Here, you’ll build your skills from data modeling all the way to production-ready applications. If you’ve ever written a script, worked with packages, or have a basic understanding of programming, getting started will be even easier for you.
This training course is right for you if you:
- have had some initial experience working with data and code
- are a data professional, such as a BI analyst, data analyst, or data engineer, and want to specialize in ML
- are a software developer who wants to implement AI projects from a technical perspective
- have a STEM background and want to build a career in the field of ML
- are interested in technology and enjoy working with data
You have an interest in data and technical issues. Some experience working with data or code is helpful. If you’ve already written a script, worked with packages, or acquired basic programming skills, you’ll find it easier to get started. In-depth knowledge of machine learning is not required.
No exam date selection is required for this exam. You can take the exam at a time of your choosing in your learning environment. It consists of two parts:
E-exam: 20 multiple-choice questions covering the material from the four Master Class courses.
Practical exam: Development of an ML use case using a provided dataset. Depending on the preparatory work completed in the four Master Class courses, you should plan on spending approximately 3–4 hours on this exam.
We recommend booking at least 14 days before the seminar date so that you can receive any documents by post in good time.