Explain the difference between mutable and immutable datatypes in Python. Also State example of each.
| Mutable Datatypes | Immutable Datatypes |
|---|---|
| Can have their elements changed after creation | Elements cannot be modified |
| List and Dictionaries | Strings and Tuples |
What is the Global Interpreter Lock (GIL) in Python, and how does it affect performance in multithreaded applications?
Explain the concept of Duck Typing in Python
Explain the differences between function arguments, keyword arguments, and variable-length arguments in Python.
What are decorators in Python, and how can they be used to modify the behavior of functions?
Explain the concept of metaclasses in Python and how they allow creating custom class behavior.
Describe the differences between shallow and deep copies in Python, and how you can achieve each type.
Differentiate between generators and iterators in Python, and explain their use cases.
What are list comprehensions and generator expressions in Python, and how are they used for concise data manipulation?
Explain the purpose of the yield keyword in Python and how it relates to generators and coroutines.
Describe the event loop and asynchronous programming in Python, and how they are used for non-blocking I/O operations.
What are context managers in Python, and how are they used for resource management and exception handling?
Explain the different ways to handle exceptions in Python using try-except blocks and other mechanisms.
Describe the purpose of modules and packages in Python, and how they help organize and reuse code.
What are some best practices for writing clean, maintainable, and idiomatic Python code?
Explain the difference between supervised and unsupervised learning in the context of machine learning.
Compare and contrast linear regression and logistic regression. When would you choose one over the other?
What are the key differences between decision trees and random forests? How do they handle overfitting?
Describe the concept of K-Nearest Neighbors (KNN) and its limitations for high-dimensional data.
Explain the basic principles behind Support Vector Machines (SVMs) and their use for classification.
What are the key features of popular deep learning libraries like TensorFlow and PyTorch?
How do neural networks learn using backpropagation? Briefly explain the process.
What are common evaluation metrics used to assess the performance of machine learning models?
How can you address issues like overfitting and underfitting in machine learning models?
Explain the importance of data preprocessing and feature engineering for machine learning tasks.