Not only is it difficult to find a good machine learning candidate hiring one can sometimes be impossible. Companies are always looking for candidates that have the right skill set but having a great mindset on top of it, is something that is difficult to find. As a discipline it hasn't been around that long which sometime results in mistakes, which are fairly common. As a candidate who is applying for machine learning engineer, having a good knowledge of mathematics and well built programming skills can land you your dream job. As a ML engineer a company is looking for someone who is hardworking, can deal with a ton of data, all the algorithms and how it all moulds together.
Anything short of a perfect interview can sometime be the reason of you not getting selected. Companies are looking for that one minor mistake so they can say no to you. ML engineering is still a small space and filling the space with right thing is more important than just filling the space for the interviewer. You as an interviewee have to hit the home run in the interview to start you career in Machine Learning. To help you how you can hit the homerun here are some of the questions regularly asked in a ML engineer interview.
These interviews are basically a set of questions that are asked to test your fundamentals of the subjects of Machine learning and Programming and how you approach a problem. Before telling you the hot questions of an interview one advice that we can provide you is before going for an interview is that you go through the important topics of ML the various concepts and techniques other than that we will advise you not to be rust in your programming skills and knowledge.
What is the difference between AI and ML?
Artificial Intelligence is the broader concept of machines being able to carry out tasks in a way that we would consider "smart" and, Machine Learning is a current application of AI based around the idea that we should really just be able to give machines access to data and let them learn for themselves.
How would you differentiate statistics from ML?
Machine Learning is an algorithm that can learn from data without relying on rules-based programming. Statistical modelling is a formalization of relationships between variables in the data in the form of mathematical equations.
What are the applications of neural network in ML?
Neural networks are information processing models that derive their functions based on biological neurons found in the human brain. The reason they are the choice of technique in ML is because, they help discover patterns in data that are sometimes too complex to comprehend by humans.
How is hyperparameter different from parameters?
A model hyperparameter is a configuration that is external to the model and whose value cannot be estimated from data. A model parameter is a configuration variable that is internal to the model and whose value can be estimated from data.
What do you mean by tuning in ML?
Tuning is the process that provides accurate output from the vast amounts of input data without human influence it involves optimising hyperparameters for an algorithm or a ML model to make them perform correctly.
What is the use of gradient descent?
The use of gradient descent plainly lies with the fact that it is easy to implement and is compatible with most of the ML algorithms when it comes to optimisation. This technique works on the principle of cost function.
Can you explain any one of the data processing techniques for ML?
Standardisation:It is mainly used for algorithms following a Gaussian distribution. It can be done through scikit.
Can you explain dimensional reduction?
The process of reducing variables in a ML classification scenario is called Dimensionality reduction. The process is segregated into sub-processes called feature extraction and feature selection. Dimensionality reduction is done to enhance visualisation of training data. It finds the appropriate set of variables known as principal variables.
What is PCA?
PCA stands for Principal Component Analysis is a dimensionality-reduction technique which mathematically transforms a set of correlated variables into a smaller set of uncorrelated variables called principal components.
What value do you optimise when using a support vector machine (SVM)?
For a linear function, SVM optimises the product of input vectors as well as the coefficients. In other words, the algorithm with the linear function can be restructured into a dot-product.
What are the bases on which you choose a classifier?
Classifiers must be chosen based on the accuracy it provides on the trained data. Also, the size of the dataset sometimes affects accuracy.
What would you choose for image classification and why?
In a supervised classification, the images are interpreted manually by the ML expert to create feature classes whereas this is not the case in unsupervised classification wherein the ML software creates feature classes based on image pixel values. Therefore, it is better to opt for supervised classification for image classification in terms of accuracy.
These are some questions that an interviewer asks you, just to check your basic knowledge/ general knowledge about the subject. These are not the answers you have to give; you can re-structure you answer according to your knowledge and expertise. You should go through all topics because of so many concepts and techniques it has. These are some questions they might ask you to know your knowledge about ML; they will ask you questions about programming as it is an important part of ML. Here are some of the programming questions you might be tested on
Is Python an object-oriented programming language? If yes, give an example.
Mention the Python modules for numerical and scientific computations.
List the file processing modes supported in Python?
Read-only mode ("r")
Write-only mode ("w")
Read-write mode ("rw")
Append mode ("a")
Which do you prefer, an integrated development environment (IDE) for Python or notebook software such as Jupyter. Explain.
I prefer Jupyter Notebook over IDEs since its gives us the power to execute code step by step and to debugs the errors easily.
Name the commonly used libraries in Python for ML.
Differentiate tuples and lists in Python.
Tuple is an immutable type which cannot be changed (it is static) whereas Lists are mutable (dynamic). Tuples do not support 'append' or 'remove' whereas Lists can.
List down the data structures supported in R.
How do you create linear models in R?
Using the lm() function
How do you test code written in R?
Using a package called 'testthat'.
How will you read a .csv file in R?
Using read.csv() function
What is the use of functions in R?
Functions provide twofold advantage. Variable input can be used for different data. Output is returned as an object which enables the manipulation of the function.
What is 'workspace' in R?
Workspace represents the interface that contains user-defined objects vectors, lists, tables etc.
Programming has an ocean of questions and languages; this is just a small pond of questions, not even the tip of the iceberg. So go through all the relevant languages related to ML. and that you are able to write codes.
Other than these questions they can ask you subjective questions to test your ability and experience when working on ML.