Data Science for Machine Learning

Course overview

Concepts of Machine Learning Introduction Introduction to the fundamentals of machine learning (ML) and its role in data science. Overview of the types of machine learning: supervised, unsupervised, and reinforcement learning. Key concepts such as training data, test data, models, and algorithms. Techniques of Supervised Learning Understanding classification and regression algorithms. Introduction to decision trees, logistic regression, and k-nearest neighbors. Practical applications and problem-solving in the real world using supervised learning. Data Preprocessing and Feature Engineering Techniques for handling missing values and outliers. The importance of feature selection and extraction in ML models. Data normalization and standardization for better model performance. Algorithms for Machine Learning and Model Evaluation Introduction to common machine learning algorithms used in data science. Evaluation metrics: accuracy, precision, recall, F1-score, and ROC curve. Best practices for selecting and tuning models based on the problem at hand. Real Application of Machine Learning Application of machine learning models to real datasets. Practical project work with Python libraries such as scikit-learn. Creation and deployment of machine learning models for business solutions. Machine learning requires thorough data preprocessing and feature engineering to optimize the performance of ML models in data science.

Upcoming dates

• 2026-10-18T22:00:00.000Z · 2026-11-16T23:00:00.000Z · Aachen · Combined Learning
• 2026-10-18T22:00:00.000Z · 2026-11-16T23:00:00.000Z · Bielefeld · Combined Learning
• 2026-10-18T22:00:00.000Z · 2026-11-16T23:00:00.000Z · Bonn · Combined Learning
• 2026-10-18T22:00:00.000Z · 2026-11-16T23:00:00.000Z · Dresden · Combined Learning
• 2026-10-18T22:00:00.000Z · 2026-11-16T23:00:00.000Z · Duisburg · Combined Learning