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Related lectures (32)

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

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

Introduces the basics of data science, covering decision trees, machine learning advancements, and deep reinforcement learning.

Explores file handling and exceptions in Python programming, covering reading, writing, and error handling strategies.

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

Covers decision tree classification using KNIME Analytics Platform for data preprocessing and model creation.

Delves into the Lausanne Time Machine project, focusing on digital urban history and interactive historical map creation.

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

Covers exception handling in Python, including try, raise, except, else, and finally blocks, with examples.

Covers scaling, mathematical modeling, tensor notation, and Python exercises in Jupyter notebooks.