Machine Learning with Python Practice

Course overview

This training provides you with practical knowledge in the field of Machine Learning with Python. You will learn to confidently categorize typical tasks, apply suitable methods and tools, and present results in a comprehensible manner. The focus is on practical applications from the areas of Python and Data Science, which will support you in your role as a research assistant or in comparable professional tasks. Python ecosystem for Machine Learning * Overview of libraries: NumPy, Pandas, Scikit-learn, and Matplotlib * Data cleaning, feature engineering, and handling missing values * Exploratory Data Analysis (EDA) as a basis for model selection Supervised Learning algorithms in practice * Linear and logistic regression: interpretation and use cases * Decision trees, Random Forests, and Gradient Boosting with Scikit-learn * Hyperparameter tuning with GridSearchCV and Cross-Validation Unsupervised Learning and dimensionality reduction * Clustering methods: K-Means, DBSCAN, and hierarchical clustering * Principal Component Analysis (PCA) for dimensionality reduction in high-dimensional datasets * Anomaly detection and outlier identification in research data Model validation and scientific reproducibility * Metrics for model evaluation: Accuracy, F1-Score, ROC-AUC, and RMSE * Avoiding overfitting: Regularization, cross-validation, and test set strategy * Experiment tracking and documentation with MLflow and Jupyter Notebooks This training helps you to handle typical tasks in the field of Machine Learning with Python more safely and efficiently. You will combine subject knowledge with practical methods and tools from the areas of Python and Data Science. This strengthens your confidence in your role as a research assistant and allows you to apply the content directly in your professional life.

Upcoming dates

• 2026-08-23T22:00:00.000Z · 2026-11-15T23:00:00.000Z · Recklinghausen, Westfalen · Blended Learning
• 2026-08-23T22:00:00.000Z · 2026-11-15T23:00:00.000Z · Eberswalde · Blended Learning
• 2026-08-23T22:00:00.000Z · 2026-11-15T23:00:00.000Z · Heilbronn, Neckar · Blended Learning
• 2026-08-23T22:00:00.000Z · 2026-11-15T23:00:00.000Z · Berlin · Blended Learning
• 2026-08-23T22:00:00.000Z · 2026-11-15T23:00:00.000Z · Erfurt · Blended Learning