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Explores the graphical analysis of full factorial experiments, emphasizing efficient data analysis and interpretation of main effects and interactions.
Covers nonparametric regression, scatterplot smoothing, kernel methods, and bias-variance tradeoff.
Covers a wide range of topics in Matplotlib, including simple plots, subplots, and various types of charts.
Explores different types of graphs achievable with Matplotlib, showcasing a variety of visualization techniques.
Introduces numerical optimization with Scipy, covering zero finding, problem dimensionality, derivatives, and advanced visualization techniques.
