Machine Learning is an integral aspect of Data Science. Therefore, being a Machine learning engineer is an amazing way to kick-start your Data Science career. If you are a Machine Learning expert it is time to crack the interview that will land you your dream job!
Read on to know what the most asked interview questions are!
Explain Machine Learning in the simplest form.
Machine Learning is an aspect of Artificial Intelligence that makes it possible for the computers to learn without being explicitly programmed. Its main function is the development of computer programs that have the ability to change when exposed to data. Moreover, to create good Machine Learning systems the following are a necessity:
- Data preparation capabilities.
- Algorithms – basic and advanced
- Automation and iterative processes.
- Ensemble modeling.
What is the difference between KNN and K means clustering?
To know the difference between the two go through the table below:
What is Bayes’ Theorem? How is it useful in the Machine Learning context?
Bayes theorem is a mathematical formula for determining conditional probability. Moreover, it allows us to encode our prior beliefs about what those models should look like, independent of what the data tells us. This is especially useful when we don’t have a ton of data to confidently learn our model.
Why is “naïve” Bayes naïve?
Naïve bayes is “Naïve” as it makes an assumption that is virtually impossible to see in real-life data. Consequently, the conditional probability is calculated as the pure product of the individual probabilities of components. This implies the absolute independence of features — a condition probably never met in real life.
List out the difference between generative and discriminative model.
A generative algorithm asks how the generation of data in order to categorize a signal. It asks the question: based on my generation assumptions, which category is most likely to generate this signal?
However, a discriminative algorithm does not care about how the data was generated; it simply categorizes a given signal.
What is an imbalanced dataset? How is it handled?
Imbalanced data refers to a classification problem where classes are not equally represented. Correspondingly, there are two classes, namely, Majority class and Minority class. Ways to handle imbalanced data can include the following:
- Collecting more data
- Changing performance metric
- Resampling the dataset
- Generating synthetic samples
- Using Different algorithm
What is ensemble learning?
Ensemble learning combines diverse set of learners together to improvise on the stability and predictive power of the model.
To know more about ensemble learning read the blog ‘Basics of Ensemble Learning Explained in Simple English‘
How is missing or corrupted data handled?
It is possible to find missing or corrupted data in datasets. Best way to handle this is by either dropping those rows or columns or by changing them with a different value.
Which Big Data tools best suite Machine Learning?
The best Big Data analytics tools that can be used in Machine learning are:
What are the best Data Visualisation tools?
The most used data visualization tools are:
- Driven documents
- High Charts
These questions are definitely going to help you crack that job interview that you have been eyeing. However, if you are a newbie to Machine Learning, start off by learning more about it. Read our article on ‘Machine Learning: A Beginner’s Guide’ to know more.