This course provides a detailed overview of the field of machine learning. First, you’ll learn the complete workflow—from data preparation and model development to evaluation and interpretation. You’ll explore techniques such as regression, classification, neural networks, generative AI, and natural language processing, as well as model deployment. The course then describes reinforcement learning, one of the three main techniques of machine learning, in which software is trained to achieve optimal results through direct interaction with its environment via trial and error.
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Certificates: Machine Learning" certificate
Reinforcement Learning" certificate -
Examination: Praxisbezogene Projektarbeiten mit Abschlusspräsentationen
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Teaching Times: Full-timeMonday to Friday from 8:30 a.m. to 3:35 p.m. (in weeks with public holidays from 8:30 a.m. to 5:10 p.m.)
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Language of Instruction: German
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Duration: 8 Weeks
Machine Learning
Introduction to Machine Learning and the ML Workflow (approx. 3 days)
Fundamentals, Applications, and Types of Machine Learning
Classification of Modern AI and Machine Learning Methods
Machine Learning Workflow with scikit-learn
Training, validation, and test data
Underfitting, overfitting, and model evaluation
Cross-Validation
Data Preparation and Feature Engineering (approx. 2 days)
Data Quality and Preprocessing
Scaling, Normalization, and Encoding
Feature Engineering and Feature Selection
Handling Missing Values, Outliers, and Imbalanced Datasets
Supervised Learning – Regression and Classification (approx. 5 days)
Regression and Classification Methods
Linear and Regularized Models
Distance-, probability-, and tree-based methods
Ensemble and Boosting Methods
Model Comparison and Hyperparameter Optimization
Evaluation Metrics and Probabilistic Predictions
Feature importance and explainable AI
Unsupervised Learning (approx. 2 days)
Dimension Reduction and Feature Extraction
Clustering Methods and Evaluation of Clustering Results
Anomaly Detection
Time Series Analysis and Forecasting (approx. 1 day)
Fundamentals and Analysis of Time Series Data
Forecasting Methods and Machine Learning for Time Series
Neural Networks and Modern AI (approx. 2 days)
Fundamentals of Neural Networks and Deep Learning
Machine Learning for Text Data and Natural Language Processing
Foundation Models, Large Language Models, and Generative AI
Deployment of ML models (approx. 1 day)
Storage and deployment of trained models
Development of simple ML applications
Interfaces and platforms for machine learning models
Project work (approx. 4 days)
To consolidate the content learned
Presentation of the project results
Reinforcement Learning
Introduction to reinforcement learning (approx. 1 day)
Definition and basic concepts
Differences to other learning methods
Areas of application and examples
Markov Decision Processes (MDPs) (approx. 2 days)
Definition and properties of MDPs
Value functions and policy
Bellman equations
Dynamic Programming Approach
Q-Learning (approx. 2 days)
Definition and algorithm
Exploration vs. exploitation
Convergence and optimization properties
Applications in games, robotics and other areas
Deep reinforcement learning (approx. 3 days)
Deep Q-Learning
Deep Deterministic Policy Gradients (DDPG)
Actor Critical Methods
Policy Gradient Methods
Advanced topics (approx. 4 days)
Model-Based Reinforcement Learning
Multi-Agent Reinforcement Learning
Inverse Reinforcement Learning
Meta Reinforcement Learning
Practical applications (approx. 3 days)
Implementation of reinforcement learning algorithms
Application to selected problems and case studies
Evaluation and tuning of the algorithms
Summary and outlook (approx. 2 days)
Summary of the most important concepts and results
Challenges and future developments in reinforcement learning
Project work (approx. 3 days)
To consolidate the content learned
Presentation of the project results
Changes are possible, the course content is updated regularly.
After completing the course, you will be able to prepare data for machine learning applications, select appropriate supervised and unsupervised learning methods, and train, evaluate, and optimize models. You will be able to interpret model results and implement basic machine learning applications. You will also be familiar with current developments in the fields of deep learning and generative AI.
You will also understand the basic concepts of reinforcement learning and know the differences to other learning methods. You are familiar with Markov decision processes, Q-learning and deep reinforcement learning and can apply advanced topics such as multi-agent and model-based reinforcement learning.
Computer science, mathematics, electrical engineering and people with a degree in (business) engineering.
Machine learning is used across various industries to analyze large amounts of data, identify patterns, optimize processes, and make predictions. Such skills are applied in fields such as IT, manufacturing, logistics, finance, and marketing. Proficiency in Python, as well as in the development, evaluation, and application of machine learning models, opens up career opportunities in data- and AI-driven fields.
With reinforcement learning, you will also gain cross-industry knowledge that is often used in robotics and automation technology, but also in the automotive industry, e.g. for driver assistance functions, or in the development and optimization of autonomous transport systems.
Your meaningful certificate provides a detailed insight into the qualifications you have acquired and improves your career prospects.
Didactic concept
Your lecturers are highly qualified both professionally and didactically and will teach you from the first to the last day (no self-study system).
You will learn in effective small groups. The courses usually consist of 6 to 25 participants. The general lessons are supplemented by numerous practical exercises in all course modules. The practice phase is an important part of the course, as it is during this time that you process what you have just learned and gain confidence and routine in its application. The final section of the course involves a project, a case study or a final exam.
Virtual classroom alfaview®
Lessons take place using modern alfaview® video technology - either from the comfort of your own home or at our premises at Bildungszentrum. The entire course can see each other face-to-face via alfaview®, communicate with each other in lip-sync voice quality and work on joint projects. Of course, you can also see and talk to your connected trainers live at any time and you will be taught by your lecturers in real time for the entire duration of the course. The lessons are not e-learning, but real live face-to-face lessons via video technology.
The courses at alfatraining are funded by Agentur für Arbeit and are certified in accordance with the AZAV approval regulation. When submitting a Bildungsgutscheinor Aktivierungs- und Vermittlungsgutschein, the entire course costs are usually covered by your funding body.
Funding is also possible via Europäischen Sozialfonds (ESF), Deutsche Rentenversicherung (DRV) or regional funding programs. As a regular soldier, you have the option of attending further training courses via Berufsförderungsdienst (BFD). Companies can also have their employees qualified via funding from Agentur für Arbeit (Qualifizierungschancengesetz).