Top 100 Python Machine Learning Interview Questions in ML and Data Science 2026

Python ML is the use of the Python programming language in the implementation of machine learning algorithms. It is a broadly applied area in the field of artificial intelligence, originating from the ability of machines to learn from data and make predictions or decisions without being explicitly programmed. In tech interviews, Python ML-related questions not only assess a candidate’s knowledge of machine learning concepts and algorithms, but also evaluate their hands-on experience in implementing these algorithms using Python’s rich library ecosystem, including popular libraries like scikit-learn, pandas, and TensorFlow.

Content updated: January 1, 2024

Python Basics for Machine Learning


  • 1.

    Explain the difference between Python 2 and Python 3.

    Answer:

    Python 2.7 and Python 3.x are distinct versions of the Python programming language. They have some differences in syntax, features, and library support.

    Key Distinctions

    Python 2.7 is the last release in the 2.x series. It’s still widely used but no longer actively developed.

    Python 3 is the most recent version, with continuous updates and improvements. It’s considered the present and future of the language.

    Major Changes

    • Print Statement: Python 2 uses print as a statement, while Python 3 requires it to be used as a function: print().
    • String Type: In Python 2, there are two main string types: byte and Unicode strings. In Python 3, all strings are Unicode by default.
    • Division: In Python 2, integer division results in an integer. Python 3 has a distinct operator // for this, while / gives a float.
    • Error Handling: Error handling is more uniform in Python 3; exceptions should be enclosed in parentheses in except statements.

    Future-Proofing

    Given that Python 2.x has reached its official end of life, businesses and communities are transitioning to Python 3 to ensure ongoing support, performance, and security updates. It’s vital for developers to keep these differences in mind when migrating projects or coding in Python, especially for modern libraries and frameworks that might only be compatible with Python 3.

  • 2.

    How does Python manage memory?

    Answer:
  • 3.

    What is PEP 8 and why is it important?

    Answer:
  • 4.

    Discuss the difference between a list, a tuple, and a set in Python.

    Answer:
  • 5.

    Describe how a dictionary works in Python. What are keys and values?

    Answer:
  • 6.

    What is list comprehension and give an example of its use?

    Answer:
  • 7.

    Explain the concept of generators in Python. How do they differ from list comprehensions?

    Answer:
  • 8.

    Discuss the usage of *args and **kwargs in function definitions.

    Answer:
  • 9.

    How does Python’s garbage collection work?

    Answer:
  • 10.

    What are decorators, and can you provide an example of when you’d use one?

    Answer:

Python Libraries for Machine Learning


  • 11.

    List the Python libraries that are most commonly used in machine learning and their primary purposes.

    Answer:
  • 12.

    What is NumPy and how is it useful in machine learning?

    Answer:
  • 13.

    Give an overview of Pandas and its significance in data manipulation.

    Answer:
  • 14.

    How does Scikit-learn fit into the machine learning workflow?

    Answer:
  • 15.

    Explain Matplotlib and Seaborn libraries for data visualization.

    Answer:
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