Machine Learning Data

Course overview

Machine Learning: Concepts of Machine Learning Develop a solid understanding of the concepts of machine learning (ML), from data preprocessing to advanced model optimization. This course covers the key ML techniques, algorithms, and practical applications. Introduction to Machine Learning Fundamental principles of machine learning and AI Differences between supervised, unsupervised, and reinforcement learning Industry applications of ML in various fields Data Preparation and Feature Engineering Data cleaning and preprocessing for ML models Methods for feature extraction and selection Handling missing values and data imbalances Supervised Learning Algorithms Classification techniques: Decision Trees, Random Forest, SVM Regression models: Linear, Logistic, and Polynomial Regression Performance evaluation: Confusion Matrix, AUC-ROC, F1-Score Unsupervised Learning and Clustering K-Means and hierarchical clustering methods Dimensionality reduction: PCA and t-SNE Anomaly detection and pattern recognition Deep Learning & Neural Networks Basics of Artificial Neural Networks (ANNs) Convolutional Neural Networks (CNNs) for image processing Recurrent Neural Networks (RNNs) and time series analysis Deployment and Optimization of Machine Learning Hyperparameter tuning and improving model performance Deploying ML models in cloud environments Ethical AI, fairness, and bias reduction in ML The course "Machine Learning: Machine Learning Concepts" provides comprehensive insights into common algorithms and their practical applications in the field of machine learning.

Upcoming dates

• 2026-09-20T22:00:00.000Z · 2026-12-18T23:00:00.000Z · Aachen · Combined Learning
• 2026-09-20T22:00:00.000Z · 2026-12-18T23:00:00.000Z · Bielefeld · Combined Learning
• 2026-09-20T22:00:00.000Z · 2026-12-18T23:00:00.000Z · Bonn · Combined Learning
• 2026-09-20T22:00:00.000Z · 2026-12-18T23:00:00.000Z · Dresden · Combined Learning
• 2026-09-20T22:00:00.000Z · 2026-12-18T23:00:00.000Z · Duisburg · Combined Learning