Fast and Effective Analytics for Big Data: Multi-Dimensional Insights

Related lectures (32)

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Provides an overview of deep learning concepts, focusing on data, model architecture, and challenges in handling large datasets.

Covers the basics of machine learning, including supervised and unsupervised learning, linear regression, and classification.

Introduces the course structure and fundamental concepts of machine learning, including supervised learning and linear regression.

Explores challenges in distributed computing, data growth, and data types, emphasizing the battle against the three Vs in big data.

Covers dimensionality reduction, PCA, clustering techniques, and density estimation methods.

Covers unsupervised learning, focusing on dimensionality reduction and clustering, explaining how it helps find patterns in data without labels.

Covers data types, quantity, quality, and representativeness in the world of data.

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

Covers the overview of information systems, data modeling, managing data, and the distinction between data and information.

Covers clustering, PCA, LDA, K-means, GMM, KDE, and Mean Shift algorithms for density estimation and clustering.