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Data Science Essentials
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Related lectures (57)
Deep Learning: Principles and Applications
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Covers the fundamentals of deep learning, including data, architecture, and ethical considerations in model deployment.
Vertical Metronome: Equilibrium and Stability
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Explores equilibrium and stability of a vertical metronome system with springs.
Storage Management in SmartDataLake
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Explores storage management challenges in transitioning to data lakes, addressing software and hardware heterogeneity, unified storage design, and performance optimization.
NumPy Arrays and Graphical Representations: Introduction
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Covers NumPy arrays and their graphical representations using Matplotlib, focusing on array creation, manipulation, and visualization techniques.
Image Processing with Fiji
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Covers the basics of image processing using Fiji and various plugins.
Regression Trees and Ensemble Methods in Machine Learning
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Discusses regression trees, ensemble methods, and their applications in predicting used car prices and stock returns.
Boltzmann Machine
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Covers the Boltzmann Machine, a type of stochastic recurrent neural network.
Critical Data Studies: Introduction and Models
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Introduces the Critical Data Studies course, covering data models, trust, reproducibility, biases, and ethics.
Clustering & Density Estimation
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Covers clustering, PCA, LDA, K-means, GMM, KDE, and Mean Shift algorithms for density estimation and clustering.
Introduction to Matplotlib
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Covers the basics of Matplotlib for 2D graphic representation in Python, including plotting data and visualizing functions.
Apache Spark Ecosystem: Basics and Operations
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Provides an overview of the Apache Spark ecosystem, covering basics, operations, and key components.
Data Mining: Introduction
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Covers the challenges and opportunities of data mining, practical questions, algorithm components, and applications like shopping basket analysis.
CloudCompare: Basic Features and Orthophoto Extraction
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Covers the basics of CloudCompare, including orthophoto extraction from point clouds.
Machine Learning: Supervised and Unsupervised Learning Techniques
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Covers supervised and unsupervised learning techniques in machine learning, highlighting their applications in finance and environmental analysis.
Visualization: Techniques and Applications
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Explores scientific visualization techniques and applications for understanding physical phenomena and communicating results effectively.
Thermo-Calc: Exercise and Property Models
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Explores Thermo-Calc software for defining functions and property models in materials science.
Project Management: Portfolio Management Training
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Covers CRISTAL status, Archibus overview, funding allocation, and project evolution phases.
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