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Concept

Scikit-learn

Related lectures (28)

General Introduction to Data Science

Offers a comprehensive introduction to Data Science, covering Python, Numpy, Pandas, Matplotlib, and Scikit-learn, with a focus on practical exercises and collaborative work.

Data Science Essentials: Python, Numpy, Pandas, and Scikit-learn

Covers the essentials of Data Science using Python, Numpy, Pandas, and Scikit-learn, including DNA sequence analysis and classification.

Unsupervised Behavior Clustering

Explores unsupervised behavior clustering and dimensionality reduction techniques, covering algorithms like K-Means, DBSCAN, and Gaussian Mixture Model.

Unsupervised Machine Learning: Clustering Basics

Introduces unsupervised machine learning clustering techniques like K-means, Gaussian Mixture Models, and DBSCAN, explaining their algorithms and applications.

Soft K-means Clustering & DBSCAN

Covers Soft K-means Clustering and DBSCAN principles, algorithms, and comparison.

Advanced Spark Optimizations and Partitioning

Delves into advanced Spark optimizations, partitioning, data skew, persistency, MLlib, and best practices.

Clustering Methods: K-means and DBSCAN

Explores K-means and DBSCAN clustering methods, discussing properties, drawbacks, initialization, and optimal cluster selection.

Vision BeLearn: Learning the Future in the Centre

Explores the vision of BeLearn in digital education transformation and challenges, emphasizing the importance of adapting to digital changes in education.

Unsupervised Learning: Clustering Methods

Explores unsupervised learning through clustering methods like K-means and DBSCAN, addressing challenges and applications.

Unsupervised Learning: Clustering Methods

Covers unsupervised learning focusing on clustering methods and the challenges faced in clustering algorithms like K-means and DBSCAN.

Making at School: Challenges and Projects

Explores challenges in education, maker-based teaching, and hands-on interdisciplinary projects in schools.

Document Segmentation: dhSegment

Explores the development and improvements of dhSegment, an open-source document segmentation package using PyTorch.

Data Science Visualization with Pandas Log in to Mediaspace to watch this video

Covers data manipulation and exploration using Python with a focus on visualization techniques.

Logistic Regression: Fundamentals and Applications Log in to Mediaspace to watch this video

Explores logistic regression fundamentals, including cost functions, regularization, and classification boundaries, with practical examples using scikit-learn.

Principal Component Analysis: Eigenfaces Log in to Mediaspace to watch this video

Covers the application of Principal Component Analysis in facial recognition using a famous faces dataset.

Classification pipeline: building and evaluating Log in to Mediaspace to watch this video

Explains building and evaluating a classification pipeline using tweet data sets.

Decision Trees: Classification Log in to Mediaspace to watch this video

Explores decision trees for classification, entropy, information gain, one-hot encoding, hyperparameter optimization, and random forests.

PCA and BBP Phase Transition Log in to Mediaspace to watch this video

Covers PCA application and BBP phase transition in a card game dataset.

Clustering Methods Log in to Mediaspace to watch this video

Covers K-means, hierarchical, and DBSCAN clustering methods with practical examples.

Regression: High Dimensions Log in to Mediaspace to watch this video

Explores linear regression in high dimensions and practical house price prediction from a dataset.