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Covers feature extraction, clustering, and classification methods for high-dimensional datasets and behavioral analysis using PCA, t-SNE, k-means, GMM, and various classification algorithms.
Delves into the spectral bias of polynomial neural networks, analyzing the impact on learning different frequencies and discussing experimental results.
Offers a comprehensive introduction to Data Science, covering Python, Numpy, Pandas, Matplotlib, and Scikit-learn, with a focus on practical exercises and collaborative work.
Covers data science tools, Hadoop, Spark, data lake ecosystems, CAP theorem, batch vs. stream processing, HDFS, Hive, Parquet, ORC, and MapReduce architecture.
