Unsupervised Learning Training in Madrid, Spain

This training on Unsupervised Learning is designed to introduce participants to the key concepts, algorithms, and techniques used in unsupervised machine learning. Unlike supervised learning, unsupervised learning works with unlabeled data and is used to uncover hidden patterns, structures, or relationships in the data. The course will cover important algorithms such as Clustering (e.g., K-Means, DBSCAN), Dimensionality Reduction (e.g., PCA, t-SNE), and Association Rule Learning. Participants will gain hands-on experience using Python and popular libraries like scikit-learn to solve real-world problems using unsupervised learning.

Introduction to Unsupervised Learning and Clustering

• Session 1: Introduction to Unsupervised Learning

  • What is unsupervised learning?
  • Key differences between supervised and unsupervised learning.
  • Applications of unsupervised learning: Clustering, Dimensionality Reduction, Anomaly Detection, etc.
  • Overview of scikit-learn and Python libraries for unsupervised learning.

• Session 2: Data Preprocessing and Feature Engineering

  • Understanding the need for data preprocessing in unsupervised learning.
  • Handling missing data, feature scaling, and encoding categorical data.
  • Feature extraction and feature selection.

• Session 3: Clustering Algorithms

  • Introduction to Clustering: Types of clustering (partitional vs hierarchical).
  • K-Means Clustering: Algorithm, distance metric, and number of clusters.
  • DBSCAN: Density-based clustering and its advantages.

• Session 4: Hierarchical Clustering

  • Introduction to Hierarchical Clustering: Agglomerative vs Divisive clustering.
  • Dendrograms and determining the optimal number of clusters.

Dimensionality Reduction, Association Rule Learning, and Applications

• Session 5: Dimensionality Reduction Techniques

  • Introduction to Dimensionality Reduction and its significance.
  • Principal Component Analysis (PCA): Understanding eigenvalues, eigenvectors, and the covariance matrix.
  • t-SNE: Techniques for visualizing high-dimensional data in 2D or 3D.

• Session 6: Association Rule Learning

  • Introduction to Association Rule Mining and its applications (e.g., market basket analysis).
  • The Apriori Algorithm and Eclat Algorithm for mining frequent itemsets.
  • Evaluation metrics: Support, Confidence, Lift.

• Session 7: Unsupervised Learning for Anomaly Detection

  • Overview of Anomaly Detection: Identifying outliers or anomalies in data.
  • Using K-Means and DBSCAN for anomaly detection.

• Session 8: Putting It All Together: Final Evaluation

  • Participants work on a final project to solve a real-world unsupervised learning problem.
  • Final evaluation based on Clustering, Dimensionality Reduction, and Association Rule Mining techniques.
  • Q&A and feedback.

This training on Unsupervised Learning provides participants with the tools and techniques necessary to perform tasks like Clustering, Dimensionality Reduction, and Association Rule Mining. By combining theory with hands-on practice, participants will leave with the skills needed to apply unsupervised learning algorithms to solve complex problems in data analysis.