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Related lectures (32)

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Covers data manipulation and exploration using Python with a focus on visualization techniques.

Explores the theory of quantum jumps in open quantum systems and their simulation.

Covers the basics of object-oriented programming in Python, including objects, classes, inheritance, and input/output handling.

Covers Python list manipulation and comprehension, emphasizing memory representation and mutability.

Covers statistical analysis and visualization of morphometric data, including discrimination between tree types and numpy calculations.

Explores the analysis of structures and mechanisms through a sample problem of opening a box with a string-held lid.

Covers Kernel Methods in Machine Learning, focusing on overfitting, model selection, cross-validation, regularization, kernel functions, and SVM.

Explains the merge sort algorithm, its correctness, and time complexity compared to other sorting algorithms.

Covers conditions, loops, functions, and graphics in R programming with practical examples.

Introduces functions in Python, covering predefined and user-defined functions, formal and effective parameters, and the importance of docstrings.