Machine learning is a combination of artificial intelligence and data science. Both of these skillsets are vastly growing in the landscape of the best tech jobs for the future. AI careers are projected to grow up to 2.3 million jobs by 2020 and will continue to grow in demand.
If you’re scheduled for a machine learning interview soon—congratulations! Being able to qualify in this field is a great achievement. It also means you need to prepare for your machine learning interview adequately. To help equip you with confidence, here’s a guide to several general machine learning interview questions you’re likely to encounter.
Top Machine Learning Interview Questions to Prepare For
1. What is Machine Learning?
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Another way of phrasing this question could be, “What does machine learning mean to you?” Answer by providing a general explanation about what machine learning is, particularly in relation to yourself.
Machine learning is an attempt to apply human experience-based learning to computers. It may sound like artificial intelligence (AI), but really it focuses more on manipulating algorithms to allow computers to learn on their own. It’s very similar to the field of data science, except the goal is to teach computers to simplify the work.
2. Why Do You Want to Work in Machine Learning?
Machine learning enthusiasts can enter a wide variety of fields. There is no one cookie-cutter job related to machine learning. Some career paths include machine learning engineering, a business intelligence developer, a natural language processing scientist, or even a data scientist.
3. What Are the Daily Responsibilities Involved in Machine Learning?
The general responsibilities involved in a machine learning job may depend on the role. If you’re applying for a machine learning engineer, you’ll focus on designing the actual algorithms needed for machine learning. You’ll be running a lot of tests to make sure your algorithms work properly.
Data science will appear a lot in your work, so as to utilize the best data science models to improve machine learning processes There will also be many statistical tests, organizing data sets, and training computer systems to apply your algorithms. All of these tasks will also be tailored to what industry you’re working for.
4. What Machine Learning Related Skills Do You Have?
- General math knowledge
- Knowledge of software structures and data science
- Statistics & Probability
- Data modeling
- Knowledge of machine learning algorithms
- Knowledge of some programming languages
- Working well with a team
5. What Machine Learning Algorithms Are You Familiar With?
There are also tools that machine learning departments in any company might use, like Hadoop or Hive. If you’ve already done research on the company, try to find out what software and algorithms they prefer, in order to become acquainted with them. Otherwise, it’s fine if you have no experience with a specific algorithm. Be honest and express a willingness to learn new software.
Some of the many algorithms include Linear Regression, K-Means Clustering, Naïve Bayes, Logistic Regression, SVM (Support Vector Machine), etc.
6. What Programming Languages Do You Know?
Although machine learning isn’t too development-based, you might have learned a few languages during your studies. To utilize many of the algorithms, a basic understanding of languages like SQL, Scala, R Programming, and Python is necessary.
7. What is Supervised vs Unsupervised Machine Learning?
Supervised learning utilizes both input and output data for predictive analysis. In other words, a computer can use previously known data results to predict what occurs when coming across potential data. It is best to use supervised learning to make a future prediction; like a company’s stock price.
Supervised learning includes methods like classification and regression models. Classification consists of categorizing data, usually using models likes like the k-nearest neighbor, neural networks, or discriminant analysis.
Regression is used to predict data over time, usually in regards to numerical values like weather forecasting. You may have heard of the linear and nonlinear models and stepwise regression, which are just some of the many types of regression techniques.
Unsupervised learning, on the other hand, is when computers try to analyze and draw predictions based only on the input data. There aren’t any past outputs to analyze, which makes it much like trial and error. Unsupervised learning is recommended for representing data in an organized matter that might show some correlation.
Clustering in unsupervised learning is used to find hidden trends in data. These trends are usually datapoints grouped together based on how similar they are. In market research, clustering can help a company group their customers based on similarities and tailor their marketing strategy.
8. What is the Difference Between Probability and Likelihood?
Any AI position requires knowledge of this distinction, so expect some machine learning interview questions on this topic. Probability and likelihood are not the same thing, so make sure you don’t use the terms interchangeably. Probability entails estimating how likely an event might be to happen in the future based on past statistical analyses. Likelihood is used less substantially, to note a possibility of an event occurring without definitive evidence or a standard form of measurement.
When compiling reports based on data in machine learning, you need to be able to use these two terms appropriately.
9. Are You Familiar With Deep Learning?
To understand machine learning, it isn’t necessary to know what deep learning is. However, it may prove useful to brush up on its basics.
Deep learning algorithms are based on the human brain’s neural pathways. As an unsupervised learning process, deep learning creates networks in computer systems that allow for the capability to make decisions. Deep learning is made to organize, process, and analyze patterns in big data.
These machine learning interview questions are not by any means complete—your job might ask more specific or personal questions. When in doubt, brush up on your machine learning knowledge and be honest about what you still need to learn.
Good luck with your machine learning interview questions and preparation!
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