At KritiKal Solutions, we believe Machine Learning (ML) is a must-have skill for candidates who want to stay updated and equip themselves with new-age technologies. The extent to which ML has to be mastered depends on the application and domain of the developer.
Being the newest technology, machine learning is still largely in the development phase and is being tested out in many areas to discover its full potential. If you are interested in this cutting-edge technology and want to make it your day-to-day activity, then perhaps a ‘Machine Learning Engineer’ is the best role for you.
In this Machine learning interview guide 2021 blog, we have collated the most important points that you must consider before appearing for an ML interview. Not just this, we have also provided a list of common questions that are asked in ML interviews.
#1 Understand Logic behind the Code
Pick one programming language, understand the logic, master it and be ready to answer questions by writing code in front of the interviewer.
No matter what language you choose, it all about how you utilize that knowledge to come up with innovative solutions to solve real-world problems.
Usually, python is the most recommended language for Machine learning engineers as it’s a de-facto in the ML Community. However, Kotlin, Java, C#, C++, Scala could be the possible alternatives. Try not to choose SAS/SPSS or MATLAB as they are niche-specific.
The modern developer community prefers open-source tools: TensorFlow, Keras, Pandas, NumPy, and Scipy are some of the best state-of-the-art machine learning systems that are well documented and have excellent stability.
Here are some of the most important things you must know about any programming language you choose:
- Working with data sets, limit, and maps
- Handling exceptions
- Building specialized data structures such as prefix tree or linked lists
- Using optimized vectorized operations
#2 Knowledge about Machine Learning Workflow
The most common question asked in an ML interview is: Describe the workflow of a Machine learning project.
Here, you have to describe the different stages required to build a proper ML project from scratch.
Here is a reference answer.
Stage 1: Data gathering (identifying the needed data and the source to get it from)
Stage 2: Data pre-processing (removing noisy and inconsistent data)
Stage 3: Researching the model (understanding the requirement and choosing the best learning model: supervised, unsupervised, and reinforcement learning)
Stage 4: Training & testing (training the system on tested and untested data)
Stage 5: Model Evaluation & Deployment (algorithm selection, hyperparameter tuning, and deployment in production)
#3 Understanding of Algorithms
Another important aspect that the interviewer may check is your knowledge of algorithms and how they work.
You have to be able to explain how algorithms work in each ML domain.
For supervised learning, You should have a good understanding of at least one of these algorithms.
- Linear and Logistic Regression
- Decision Trees
- Support Vector Machines (SVM)
- Multilayer Perceptron (Feedforward Neural Network)
For unsupervised learning, you have to be able to explain K-means, Latent Dirichlet Allocation, etc., and how to find the right number of cluster data in the collection of documents.
For reinforced learning, you can learn any one of the modern deep-learning algorithms.
#4 Understanding of Feature Extraction
Feature extraction is the most important aspect of any machine learning model.
You should have a good in-depth understanding of; how sound and text can be converted into features, working of one-hot encoding, how to transform continuous attributes into categorical, and vice versa.
#5 Knowledge of Data Visualization Algorithms
As an ML engineer, you must be good with data visualization. But, sometimes data is often so high-dimensional that it becomes practically impossible for humans to visualize data points.
For that, you have to have a good understanding of dimensionality reduction algorithms and be able to explain their working and different forms. Here are 3 algorithms that you should at least master before appearing for an ML interview.
- Principal Component Analysis
#6 Know how to Deploy an ML Model
The Machine learning models can be deployed in multiple ways. Some of the most common ways are:
- RESTful web services
- Docker containers
While answering this question, you have to explain what exactly the model consists of. For example: In the case of the python language, usually, it’s a pickle file that contains model objects, data normalizers, feature extractors, and dimensional reducers.
Commonly asked questions in ML Interviews
Below, we have provided a list of most common questions asked in any Machine learning interview.
Now that you have an overall idea of the questions that an interviewer may ask, we are sure that you are well-equipped to pass an ML interview. Apart from technical knowledge, presentation skills and confidence plays an important role in cracking the interview. So, stay calm and all the best!
Are you looking for ML Jobs in Noida?
If you are looking for the best ML jobs in India to build your career as a successful machine learning engineer while learning new skills and having fun at work. You are at the right place!
KritiKal Solutions is a technology design house with a keen focus on cutting-edge technologies like AI/ML, Computer Vision, IoT, cloud, mobile and moreto design and develop innovative solutions for ever-evolving business needs. With 18+ years of experience in Product designing and R&D, we have developed solutions for many industries including automobile, defense, edutech, health & wellness.
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