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Modeling & Machine Learning

Modeling & Machine Learning

18 of 63 Completed

Introduction to Machine Learning
Modeling Case Study
Data Pre-Processing
Feature Selection
Model Selection
Machine Learning Algorithms
Recommendation and Search Engines
Model Evaluation
Applied Modeling
Machine Learning System Design
Generalized Linear Models and Regression

How Much Machine Learning?

How much machine learning do I need to know?

This is the most repetitive question that I have gotten ever since starting Interview Query. Why do you think that is? Because there is an infinite amount of knowledge you can consume in machine learning. Literally infinite. The very definition of machine learning and AI conceptualizes this fact.

Machine learning is a technology that is breaking ground at new speeds every day. Technically, it should be improving faster and faster, given that ML is essentially supposed to be learning itself.

However, machine learning tested in an interview is completely different from how it is generally framed in real practice. It is also different depending on the type of role that you’re interviewing for. A data scientist is not expected to know the same level of knowledge necessary for machine learning compared to a machine learning engineer or research scientist. This varying expectation, however, can be confounded by what the employer thinks a data scientist does versus a machine learning engineer, such as a case where the role is titled data scientist, but the position is instead designed for building machine learning infrastructure the whole time. .

Data Scientist

The data scientist role is primarily responsible for solving business problems using data to pull, munge, and generate insights from data. Data scientists will explore all aspects of the business and work cross-functionally with different teams to do everything from developing dashboards for reporting and exploring analytics for insights, to building models.

The last part of building models is tricky in determining how much machine learning a data scientist should know. Many data science roles that are focused on analytics don’t require any machine learning at all, while some roles are essentially machine learning engineers with a data scientist title. Generally, the main way to understand the difference is to ask everyone at the company about the day-to-day responsibilities of the role that you’re interviewing for.

Machine Learning Engineers

Engineers build models and deploy them, develop infrastructure to scale, and work with data scientists to understand the best-use cases. They leverage data tools, programming frameworks, and data pipelines to ensure that models scale appropriately for any technical specifications.

Machine learning engineers should also have a strong knowledge of machine learning and theory, given their responsibility for building tooling and automation over the model creation, training, and evaluation lifecycle.

Research Scientist

Research scientists are typically roles meant for teams to break new ground with machine learning in the research domain. The level of machine learning and statistics knowledge needed is usually very high.

Given these three roles, the best way to estimate how much machine learning knowledge is needed for the interview would be to first understand how embedded in machine learning your job will be. This is done with individual research on the company, position, team, and background information of your interview panel.

Good job, keep it up!



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