ENH iSecure Interview Questions and Answers

Questions related to programming and machine learning concepts.

• pass and continue are control statements in Python. pass does nothing and continue skips the current iteration of a loop.

• try-except is a block of code used to handle errors and exceptions in Python.

• Semi-supervised ML is a type of machine learning where some data is labeled and some is not. Supervised ML uses labeled data while unsupervised ML uses unlabeled data.

• Regression is used to predict continuous values whil...read more

I am very confident with the syllabus.

• I have thoroughly studied and prepared the syllabus.

• I have experience teaching the topics covered in the syllabus.

• I am up-to-date with the latest developments in the field.

• I am confident in my ability to explain complex concepts in a simple manner.

Python and SQL coding are essential for data science. ML algorithms are used for predictive modeling.

• Python is used for data cleaning, manipulation, and visualization.

• SQL is used for data querying and manipulation in databases.

• ML algorithms are used for predictive modeling and classification tasks.

• Examples of ML algorithms include linear regression, decision trees, and neural networks.

Exceptions in Python can be handled using try-except blocks to gracefully handle errors and prevent program crashes.

• Use try-except blocks to catch and handle exceptions in Python code

• Specify the type of exception to catch or use a generic except block to catch all exceptions

• Use the else block to execute code only if no exceptions are raised

• Use the finally block to execute cleanup code regardless of whether an exception was raised

• Example: try: a = 10 / 0 except ZeroDivisionErr...read more

Supervised learning is a type of machine learning where the model is trained on labeled data.

• In supervised learning, the algorithm learns from labeled training data, where each example is a pair consisting of an input object and a desired output value.

• The goal is to learn a mapping from inputs to outputs in order to make predictions on unseen data.

• Common examples of supervised learning algorithms include linear regression, logistic regression, decision trees, and support vect...read more