Machine Learning Engineer

Course overview

Goals of the Training The qualification measure "Machine Learning Engineer" at IfaDW imparts practical skills for the development, training, and production-ready deployment of machine learning models in companies and organizations. The program lasts for 20 weeks, takes place entirely remotely with digital learning support, and covers the areas of data analysis with Python, machine learning, deep learning, as well as MLOps and deployment. The aim is to comprehensively enable participants to independently manage the entire ML lifecycle, from data preparation to training classical and deep models, to containerized deployment with CI/CD and monitoring. Upon successful completion, participants receive a certificate according to AZAV and module-specific participation certificates with a competency profile. Contents of the Training Data Analysis with Python: Basics of Python, Numpy, Pandas, Polars, Matplotlib, Seaborn, Plotly, Regular Expressions, Networking Programs and Web Services, Object-Oriented Programming, Web Scraping, Time Series, Statistics, EDA Applied Machine Learning: Supervised Learning, Unsupervised Learning, Reinforcement Learning, Ensemble Learning, Decision Trees, Random Forests, Clustering, Feature Engineering, Missing Value Handling, Imputation, Outlier Detection, Model Evaluation, Explainable AI Deep Learning: TensorFlow, Keras, PyTorch, (Multilayer) Perceptron, DNNs, CNNs, RNNs, LSTMs, GRU, Autoencoder, GANs, Transformer, Graph Neural Networks, Interpretability, Fairness, and Robustness MLOps and Deployment: Linux Essentials, Docker, Version Control, MLFlow, CI/CD, GitHub Actions, Unit Tests, FastAPI, Monitoring, Model Deployment, Portfolio Preparation Individual Project Work

Upcoming dates

• 2026-11-29T23:00:00.000Z · 2027-04-29T22:00:00.000Z · Virtuelles Klassenzimmer
• 2027-01-17T23:00:00.000Z · 2027-06-03T22:00:00.000Z · Virtuelles Klassenzimmer