CYBER MONDAY : The Biggest Online Learning Holiday Sale, 30% Instant Discount With Coupon : CYBER30
Machine Learning and Artificial Intelligence are the most popular technologies in the developing era today. This comprehensive blog includes some of the most frequently asked Machine Learning interview questions that aim to help you go through all the important concepts and skills to achieve your dream job.
Moreover, when you are desiring to appear for the data science interview or machine learning, it is considered to be the necessary part that could help you in becoming successful machine learning engineers or data engineers.
Therefore, JanBask Training has created a free guide to data scientist machine learning interview questions so that you may analyze exactly where you stand currently. Here, this page will guide you to brush up on the Machine learning skills to crack the interview successfully.
Being professionals, our main focus will be on real-world scenario Machine Learning interview questions for freshers as well as experienced candidates. Machine learning interview questions will be related to the questions that may be asked in some renowned firms like Microsoft, Amazon, etc., And will help you improve the way to answer them.
Let’s get started!
It is very common to get confused between the three in-demand technologies: Machine Learning, Artificial Intelligence, and Deep Learning. It is because these three technologies, though they are a little different from one another and are interrelated to each other.
While Deep Learning is a subset of Machine Learning and Machine Learning is a subset of Artificial Intelligence which you can clearly understand in the below-mentioned image. Since some terms and techniques may overlap with each other while dealing with these technologies, it is easy to get confused between them.
Therefore, let’s go through about these technologies in detail so that you become capable of differentiating between them:
Bias is the common error in the machine learning algorithm due to simplistic assumptions. It may undermine your data and does not allow you to achieve maximum accuracy. Further generalizing the knowledge from the training set to the test sets would be highly difficult for you.
Variance error is common in machine learning when the algorithm is highly complex and difficult to understand as well. It may lead to a high degree of variation to your training data that can lead the model to overfit the data. Also, there could be so much noise for the training data that is not necessary in case of the test data.
The bias-variance trade-off is able to handle the learning errors effectively and manages noise too that happens due to underlying data. Essentially, this trade-off will make the model more complex than usual but errors are reduced optimally.
Here is the difference between supervised and unsupervised machine learning that you can consider before going on a Machine Learning Interview:
K-nearest algorithm is the supervised learning while the k-means algorithm is assigned under the unsupervised learning. While these two techniques look similar initially, still there is a lot of difference between the two Supervised learning requirements data in the labeled form.
For example, if you wanted to classify the data then you should first label the data then further classify it into different groups. On the other hand, unsupervised does not require any data labeling explicitly. The application of both the techniques also depends on project requirements.
Receiver Operating Characteristic curve (or ROC curve) is a fundamental tool used for diagnostic test evaluation and pictorial representation of the contrast between true positive rates and the false positive rates calculated at multiple thresholds. It is used as the proxy to measure the trade-offs and sensitivity of the model. Based on the observation, it will trigger false alarms.
The Recall is the measure of true positive rates claimed against the total number of datasets. Precision is the prediction of positive values that your model claims compared to the number of positives it actually claims. It can be taken as a special case of probability as well in the case of mathematics.
With the Bayes’ Theorem, you could measure the posterior probability of an event based on your prior knowledge. In mathematical terms, it will tell you the exact positive rate of a condition i.e. divided by the sum of total false rates of the entire population.
Bayes Theorem is also known as the Bayes Rule in mathematics, and it is popular for calculating the conditional probability. The name of the theorem was given after a popular mathematician Thomas Bayes. The two of the most significant applications of the Bayes’ theorem in Machine Learning are Bayesian optimization and Bayesian belief networks. This theorem is also considered as the foundation behind the Machine Learning brand that includes the Naive Bayes classifier.
Naïve is the word used to define the things that are virtually impossible in the real-life. Here, also you require to calculate the conditional probability as the product of individual probabilities of different components.
The Naive Bayes method is a supervised learning algorithm, it is naive since it makes assumptions by applying Bayes’ theorem that all attributes are independent of each other. Bayes’ theorem states the following relationship, given class variable y and dependent vector x1 through xn:
P(yi | x1,..., xn) =P(yi)P(x1,..., xn | yi)(P(x1,..., xn)
Using the naive conditional independence assumption that each xiis independent: for all I this relationship is simplified to:
P(xi | yi, x1, ..., xi-1, xi+1, ...., xn) = P(xi | yi)
P(x1,..., xn) is a constant given the input, we can use the following classification rule:
P(yi | x1, ..., xn) = P(y) ni=1P(xi | yi)P(x1,...,xn) and we can also use Maximum A Posteriori (MAP) estimation to estimate P(yi)and P(yi | xi) the former is then the relative frequency of class y in the training set.
P(yi | x1,..., xn) P(yi) ni=1P(xi | yi)
y = arg max P(yi)ni=1P(xi | yi)
The different naive Bayes classifiers mainly differ by the assumptions they make regarding the distribution of P(yi | xi): can be Bernoulli, binomial, Gaussian, and so on.
L2 regularization tends to spread error among multiple terms while L! is more specific to binary variables where either 0 or 1 is assigned based on requirements. L1 tends to set a Laplacian prior on terms, but L2 tends to set a Gaussian prior on terms.
The answer to this question will vary based on the projects you worked on earlier. Also, which algorithm assured better outcomes as compared to others?
This is a tricky question usually asked by experienced candidates only. If you would be able to answer this question then make sure that you are at the top of the game. Type 1 error is the false positive and Type 2 error is a false negative. Type 1 error signifies something has happened even if it does not exist in real life while Type 2 error means you claim something is happening in real life.
Here is a small difference between Type 1 and Type 2 error:
A Fourier Transformation is the generic method that helps in decomposing functions into a series of symmetric functions. It helps you in finding the set of cycle speeds, phases, and amplitude to match the particular time signal. It has the capability to convert the signal into frequency domain like sensor data or more.
Deep learning is a part of machine learning that is usually connected with the neural networks. This is a popular technique from neuroscience to model a set of labeled and structured data more precisely. In brief, deep learning is an unsupervised learning algorithm that represents data with the help of neural nets.
A generic model will explain the multiple categories of data while the discriminative model simply tells the difference between data categories. They are used in classification tasks and need to be studied deeply before you actually implement them.
The cross-validation method in Machine Learning allows a system to enhance the performance of the given Machine Learning algorithm to which you feed various sample data from the dataset. This sampling process is done to break the dataset into smaller parts that have the same number of rows, out of which a random part is selected as a test set, and the rest of the parts are kept as train sets. Cross-validation includes the following techniques:
Well, model accuracy is just a subset of the model performance parameter. For a model who is performing excellent, there are chances of more accuracy than others.
Let’s go through the below-mentioned model before directly jumping onto F1 score:
True Positive (TP)
False Negative (FN)
False Positive (FP)
True Negative (TN)
In binary classification we consider the F1 score to be a measure of the model’s accuracy. The F1 score is a weighted average of precision and recall scores.
F1 = 2TP/2 TP + FP + FN
Now, let’s learn about the F1 score, which is used to check the performance of a model or this is the average of precision and recall of a model where 1 means the best and 0 means the worst.
Collect more data, manage the imbalanced data, try a different algorithm to work on imbalanced datasets in machine learning.
Classification gives you discrete results while regression works on continuous results more. To become more specific with data points, you are always recommended using classification over regression in machine learning.
For this purpose, you can always check the F1 score to make sure either machine learning model is working effectively or needs improvement. All the best and Happy job hunting!
Precision and recall are the two different ways of monitoring the power of machine learning implementation. They are mostly used at the same time. Precision answers the question, “Out of the items that the classifier predicted to be relevant, how many are truly relevant?”
Whereas, recall answers the question, “Out of all the items that are truly relevant, how many are discovered by the classifier?
The basic meaning of precision is the fact of being exact and accurate. So the same will be followed in the machine learning model as well. If you have a set of items that your model requires to predict to be relevant.
The below figure shows the Venn diagram with precision and recall.
Mathematically, precision and recall can be defined as the following:
It fully depends on the dataset you have and if the data is discrete then you may use SVM. In case the dataset is continuous then you can use linear regression.
So there is no particular way that lets us know which Machine Learning algorithm to use, it all depends on the exploratory data analysis (EDA).
EDA is like “interviewing” the dataset; As part of our interview you may do the following:
Based on the above observations, choose the best-fit algorithm for a particular dataset.
Collaborative filtering is considered to be a proven technique that is used for personalized content recommendations. It is a type of filtering system that predicts new content by matching an individual's interest with other user preferences.
However, the content-based filtering is focused only on the user preferences. Also, new recommendations are made to the user from similar content based on the user’s previous choice.
Correlation is used for measuring and also for evaluating the quantitative relationship between two variables. Correlation measures the relationship of two variables such as Income and expenditure etc.
Moreover, Covariance is a simple way to measure the correlation between two variables but there is a problem with covariance is that they are hard to compare without normalization.
Parametric models have limited parameters and to predict new data, you only require to know the parameters of the model.
However, Non-parametric models have no limits in taking a huge number of parameters that allow more flexibility to predict new data. You can efficiently know the state of the data and model parameters via Parametric and Non-parametric models.
Reinforcement learning varies from the other types of learning such as supervised and unsupervised learning. However, in reinforcement learning, we are given nothing neither the data nor the labels. Our learning is basically, based on the rewards given to the agent by the environment.
The sigmoid function is used for binary classification and the probabilities sum required to be 1. Whereas, Softmax function is used for multi-classification and its probability sum will be 1.
So these are the most frequent Machine Learning Interview Questions. However, if you wish to brush up more on your knowledge, you can go through more such blogs:
With this, we come to the end of this blog. I hope the above mentioned Machine Learning Interview Questions will help you ace your Machine Learning Interview and grab a suitable seat for yourself.
Moreover, if you want to become a successful Machine Learning Engineer, you can take up Machine Learning Certification Training using Python from JanBask Training. This program exposes you to concepts of Statistics, Time Series and multiple classes of machine learning algorithms including various concepts like supervised, unsupervised and reinforcement algorithms.
All these will help you be proficient in multiple Machine Learning algorithms like Regression, Clustering, Decision Trees, Random Forest, Naïve Baye, and much more.
Do share your comments below to let us know whether this Machine learning interview question booklet helped you crack your interview or not!FaceBook Twitter Google+ LinkedIn Pinterest Email
Through market research and a deep understanding of products and services, Jyotika has been translating complex product information into simple, polished, and engaging content for Janbask Training.
MS SQL Server
Receive Latest Materials and Offers on Artificial Intelligence Course