Emerging technologies have taken the world by storm. The innovations, opportunities, and threats they have unleashed are like no other. Along with their growth, the demand for specialists in these areas has grown.
As per the findings of the latest industry report, jobs in emerging technologies like machine learning, artificial intelligence, and data science rank among the top emerging jobs. A career in emerging technologies such as machine learning, AI, or data science can be highly lucrative as well as intellectually stimulating.
In this article, I have compiled some of the most frequently asked machine learning interview questions with their corresponding answers. Machine learning aspirants, as well as experienced ML professionals, can use this to revise their fundamentals before the interview.
Machine Learning Interview Questions 2019
Differentiate Machine Learning and Deep Learning
What do you understand by the terms Recall and Precision?
Differentiate between Supervised Machine Learning and Unsupervised Machine learning?
What is K-means and KNN
What makes Classification different from Regression
How will you deal with missing data in a dataset?
What do you understand by Inductive Logic Programming (ILP)?
What are the steps you need to ensure you don’t overfit with a specific model?
What is Ensemble Learning?
Name the steps that are required in a machine learning project?
Machine learning, a subset of artificial intelligence, provides the machines with the capability to learn and improve automatically without any explicit programming. Whereas Deep learning, a subset of machine learning, artificial neural networks that are capable of making intuitive decisions.
The recall is alternatively called a true positive rate. It refers to the number of positives that have been claimed by your model compared to the number of positives that are available throughout the data.
Precision, which is alternatively called a positive predicted value, is based on prediction. It is a measurement of the number of accurate positives that the model has claimed as compared to the number of positives that the model has actually claimed.
In Supervised learning, the machine is trained with the help of labeled data, i.e., data that is tagged with the right answers. Whereas in unsupervised machine learning, the model learns by discovering information by itself. As compared to supervised learning models, unsupervised models are more preferred for performing difficult processing tasks.
K-means is an unsupervised algorithm that is used for the process of clustering problems and KNN or K nearest neighbors is a supervised algorithm that is used for the process of regression and classification.
Both these concepts are an important aspect of supervised machine learning techniques. With Classification, the output is classified into different categories for making predictions. Whereas Regression models are usually used to find out the relationship between forecasting and variables. A key difference between classification and regression is that in the former the output variable is discrete and it is continuous in the latter.
One of the greatest challenges faced by a data scientist pertains to the problem of missing data. You can attribute the missing values in many ways including assigning a unique category, row deletion, substituting with mean/median/mode, employing algorithms that support the support missing values, and forecasting the missing value to name a few.
A subfield of machine learning, Inductive Logic Programming searches patterns in data by using logic programming to develop predictive models. This process assumes that logic programs are a hypothesis or background knowledge.
When the model is provided a large amount of data during training, it starts to learn from the noise and other wrong data from the data set. This makes it difficult for the model to learn to generalize new instances apart from the training set. There are three ways by which you can avoid overfitting in machine learning. The first way is by keeping the model simple, the second way is by using cross-validation techniques and thirdly, by using regularization techniques, for example, LASSO.
Ensemble methods are alternatively called learning multiple classifier systems or committee-based learning. Ensemble method refers to the learning algorithms that build classifier sets and then categorize new data points to make a choice of their forecasting. This method trains a number of hypotheses to address the same problem. The best example of ensemble modeling is the random forest trees where many decision trees are used for predicting the results.
Some of the critical steps that you should take for achieving a good working model are collecting data, preparing data, selecting a machine learning model, model training, evaluating the model, tuning the parameter, and lastly, prediction.