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Related lectures (31)
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Big Data Best Practices and Guidelines
Covers best practices and guidelines for big data, including data lakes, architecture, challenges, and technologies like Hadoop and Hive.
Neural Networks: Training and Activation
Explores neural networks, activation functions, backpropagation, and PyTorch implementation.
Super-vision: Celebrating EPFL's Doctoral Theses History
Explores EPFL's doctoral theses history, data visualization, and the innovative D3 approach.
Statistics: Exploratory Data Analysis
Introduces statistics basics, including data analysis and probability theory, emphasizing central tendency, dispersion, and distribution shapes.
Maximum Likelihood: Inference and Model Comparison
Explores maximum likelihood inference, model selection, and comparing models using likelihood ratios.
General Introduction to Big Data
Covers data science tools, Hadoop, Spark, data lake ecosystems, CAP theorem, batch vs. stream processing, HDFS, Hive, Parquet, ORC, and MapReduce architecture.
Data Analysis Techniques: Amplitude Shift Keying and Graphical Methods
Covers amplitude shift keying and various data analysis techniques using Jupyter Notebooks.
Data Science for Engineers: Part 2
Explores data manipulation, exploration, and visualization in data science projects using Python.
Unsupervised Learning: Clustering & Dimensionality Reduction
Introduces unsupervised learning through clustering with K-means and dimensionality reduction using PCA, along with practical examples.
Principal Component Analysis: Dimension Reduction
Covers Principal Component Analysis for dimension reduction in biological data, focusing on visualization and pattern identification.
