15 Questions to Ask at Machine Learning InterviewSeptember 23, 2018
There has been a lot of debate about what are the most common machine learning interview questions during an interview. Some say a proper knowledge is required for answering the questions of machine learning while some say knowledge of python programming is enough to crack machine learning interview questions.
Here are some commonly asked questions about machine learning which is very important to know if the user is interested in machine learning online training.
Clearly explain dimensionality reduction stating its usability and benefits?
The process where the number of featured variables is minimized, taking into account a set of principal variables, can be termed as dimensionality reduction. Now it can be said that dimensionality reduction technique can be used in order to know how much a variable can contribute to representing the information. The technique that is mostly preferred and also used to know the contribution of a variable are nonlinear and trial and error technique. Some of the benefits of this process are known to be speeding computation, minimizing storage space and reduction in data dimension.
How can a user handle missing or corrupted data in a data set?
The best possible way to find a corrupted data in a data set is by replacing the variable with another value or by introducing new column and rows. Now it has noted that are few other techniques to find the missing data are known as the fillna() method and other is known as the dropna() and insull() method.
What is clustering algorithm?
Clustering algorithm can be defined as the unsupervised learning technique which used for finding out the structure of an unlabelled data. This clustering could be defined as the data which is similar in their orientation but dissimilar when compared to other clusters.
How can exploratory data analysis or EDA can be performed?
The main goal of this algorithm is to find out the information about the data before it is being applied to any model. Basically when EDA is performed the IT professionals look for some global insights which is to check out the mean variable of each specific class. After this action is performed then the IT professionals run a panda known df.info () to check for any of the variables are categorical or continuous like int, float or string.
How to decide on which machine learning model to use?
In deciding which machine learning model to use one should always keep the no freelunchtheorem at the back of their mind. Now if the user wants to estimate a direct relationship between the output variable and single variable then choosing a single regression model or multiple regression model is the best choice. Now if the user wants to determine complex nonlinear relationships then choosing neutral network model is the ideal choice.
How to use convolutions of pictures instead of FC layers?
This can be explained in two parts, firstly the users need to derive the information from the image since FC will have no actual information. The second part is using convolution neural networks which is useful since the FC acts as its own detector.
What makes CNN translation invariant?
Now it has to be noted that each convolution acts on its own way or acts as its own feature detector meaning if the user wants to perform image detection then convolution acts as a own feature detector. Now it is irrelevant where the image is since convolution will be acted in the entire image.
Why is there max polling classification in CNN’s?
CNN’s contains max pooling classification because it has the ability to minimize the computation process since the feature maps tend to be smaller in size than that of pooling. In addition, with the help of max poling classification of more translation can be found invariance.
Why does CNN’s have encoder-decoder style or structure?
CNN’s have the encoder-decoder structure for two reasons, firstly the encoder is helpful in extracting the feature network and the decoder is used to decode the image in segments and thenupscale it back to its original size
What is the importance of residual networks?
One of the major importance of residual networks is that it allows access from the past or previous layers of data. This access allows the flow of information to be smooth throughout the network.
What is batch normalization and how does it work
The technique where each input layer gets modified as the previous layers tend to change is known as the batch modification. The batch normalization mainly works by making a standard deviation to be 1 and the output to be zero.
How to handle imbalance data sheet?
Datasheet could be handled with the few basic steps. Some of these include:
- Using class weights.
- Using the training examples again and gain.
- Avoid any under sample if the data is too large.
- Use data augmentation.
Why machine learning is using small kernels instead of large kernels?
The use of small kernels is due to the fact that, with smaller kernels proper receptive field can be known. Since smaller kernels use small computations and fewer parameters it is possible to get more mapping functions and even more filters.
Can there be any other projects which can be related?
In order to draw relations with some other projects, the user doesn’t need to think a lot. The user just have to think over the facts which connect the research to business.
Explain the current master’s research? What worked? What did not? Future directions?
Current master’s research basically means which algorithms can be used to determine the value of coefficients and which model is best suitable for use. The use of machine learning algorithms worked a great deal but the single regression technique did not give the values correctly. Future directions would taking the time and doing research first before jumping to anyconclusion.
Thus from the FAQ, it can very well be said that these are some of the most common machine learning online training questions that the user can encounter during the course of online study. Furthermore, these questions also provide a glimpse of python programming which serves as an asset to the machine learning.