What you'll learn:
- Machine Learning interview questions with answers
- Crack Machine Learning job interviews
- Enhance your knowledge of Machine Learning
- Become a Machine Learning Engineer
- Get aware about the most trending topics on Machine Learning
A warm welcome to the Machine Learning Interview Questions & Answers course by Uplatz.
Uplatz provides this course on the most frequently asked questions in Machine Learning Engineer / Data Scientist job interviews. In this Machine Learning interview questions course, you will learn and get familiarized with the correct and comprehensive answers to the trending questions related to Machine Learning.
According to Indeed, the average salary for a machine learning engineer is $149,750 per year in the United States and similar high salaries in other countries too. With more and more organizations making machine learning as a key pillar for innovation and driving growth, there is huge scope for smart and enthusiastic Machine Learning engineers. It is one of the fields having great career prospects, both in terms of the compensation offered as well as considering the variety of challenges available.
What is Machine Learning?
Machine learning is the fastest growing subfield of artificial intelligence, where systems have the ability to “learn” through data, statistics and trial and error in order to optimize processes and innovate at quicker rates. Machine learning is giving computers the ability to develop human-like learning capabilities that are allowing them to solve some of the world’s toughest problems, ranging from cancer research to climate change.
Machine Learning facilitates a system to learn from examples and experience without being explicitly programmed. Hence instead of writing code, what you do is you feed data to the generic algorithm, and the algorithm/ machine itself builds the logic based on the given data. Thus, Machine learning is the science of enabling computers to function without being programmed to do so.
By combining software engineering and data analysis, machine learning engineers enable machines to learn without the need for further programming. As a machine learning engineer, working in this branch of artificial intelligence, you'll be responsible for creating programs and algorithms that enable machines to take actions without being directed. An example of a system you may produce is a self-driving car or a customized newsfeed.
This branch of artificial intelligence can enable systems to identify patterns in data, make decisions, and predict future outcomes. Machine Learning can help companies determine the products you're most likely to buy and even the online content you're most likely to consume and enjoy. Machine Learning makes it easier to analyze and interpret massive amounts of data, which would otherwise take decades or even an eternity for humans to decode.
Roles of a Machine Learning engineer
Design and Build distributed, scalable, and reliable data pipelines that ingest and process data at scale and in real-time
Apply computer science fundamentals, including data structures, algorithms, computability and complexity and computer architecture
Use exceptional mathematical skills, in order to perform computations and work with the algorithms involved in this type of programming
Produce project outcomes and isolate the issues that need to be resolved, in order to make programs more effective
Collaborate with data engineers to build data and model pipelines
Select appropriate platforms for the execution and productization of ML pipelines
Develop and productionize machine learning solutions aligned to business needs and push the boundaries by suggesting and driving new technologies
Support the implementation of industry-scale high-quality production systems
Manage the infrastructure and data pipelines needed to bring code to production
Understand how to combine data architectures, distributed systems, machine learning, and next-generation user interfaces
Build algorithms based on statistical modelling procedures and build and maintain scalable machine learning solutions in production
Use data modelling and evaluation strategy to find patterns and predict unseen instances
Apply machine learning algorithms and libraries
Create advanced analytics and machine learning driven solutions for varying data volumes, data types and formats
Design machine learning systems, and oversee the platform on which the solutions would be deployed
Analyze large, complex datasets to extract insights and decide on the appropriate technique
Research and implement best practices to improve the existing machine learning infrastructure
Provide support to engineers and product managers in implementing machine learning in the product
