Machine Learning

Course overview

Machine Learning: Machine Learning Explore the core concepts of machine learning (ML) and develop the skills to create predictive models. This course covers supervised and unsupervised learning techniques, model evaluation, and real-world ML applications. Introduction to Machine Learning Overview of machine learning and its types Difference between supervised, unsupervised, and reinforcement learning Real-world applications of ML in various industries Supervised Learning Techniques Classification algorithms: Decision Trees, SVM, and Naive Bayes Regression models: Linear Regression, Logistic Regression Metrics for model evaluation: Accuracy, Precision, Recall Unsupervised Learning Methods Clustering techniques: K-Means, DBSCAN, Hierarchical Clustering Dimensionality reduction: PCA and t-SNE Anomaly detection and recommendation systems Deep Learning and Neural Networks Basics of Artificial Neural Networks (ANNs) Convolutional Neural Networks (CNNs) for image processing Recurrent Neural Networks (RNNs) for time series and NLP Optimizing Machine Learning Models Feature selection and engineering techniques Hyperparameter tuning and improving model performance Cross-validation and avoiding overfitting Applications of Machine Learning in Practice AI-driven automation and predictive analytics Deployment of ML models in cloud environments Ethical considerations and bias mitigation in ML The course "Machine Learning: Machine Learning" delves into the optimization of machine learning models and illustrates practical applications across various industries.

Upcoming dates

• 2026-10-18T22:00:00.000Z · 2027-01-15T23:00:00.000Z · Aachen · Combined Learning
• 2026-10-18T22:00:00.000Z · 2027-01-15T23:00:00.000Z · Bielefeld · Combined Learning
• 2026-10-18T22:00:00.000Z · 2027-01-15T23:00:00.000Z · Bonn · Combined Learning
• 2026-10-18T22:00:00.000Z · 2027-01-15T23:00:00.000Z · Dresden · Combined Learning
• 2026-10-18T22:00:00.000Z · 2027-01-15T23:00:00.000Z · Duisburg · Combined Learning