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Top 29 Interview Questions For Machine Learning Engineer

Going for a Machine learning interview? First, read these top 29 interview questions for Machine Learning Engineering. We have list 29 questions that will help you to clear the machine learning interview. For more information, visit https://vasitum.com/

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Top 29 Interview Questions For Machine Learning Engineer

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  1. TOP 29 Interview Questions For Machine Learning Read the full article- Link in the bio

  2. What is the meaning of Variance Error in ML algorithms? Variance error is found in machine learning algorithms that are highly complex and pose difficulties in understanding them. As a result, you can find a greater extent of variation in the training data. Subsequently, the machine learning model would over fit the data. Also, you can also find excessive noise for training data, which is entirely inappropriate for the test data. Read the full article- Link in the bio

  3. What is precision, and what is a recall? The recall is the number of true positive rates identified for a specific total number of data sets. Precision involves predictions for positive values claimed by a model as compared to the number of actually claimed positives. You can assume this as a special case for probability with respect to mathematics. Read the full article- Link in the bio

  4. What is an F1 Score and how is it used? The F1 score is a measure of a model’s performance. It is a weighted average of the precision and recall of a model, with results tending to 1 being the best, and those tending to 0 being the worst. You would use it in classification tests where true negatives don’t matter much. Read the full article- Link in the bio

  5. What are the different languages used for machine learning? The most popular language for machine learning is python. Other languages for machine learning are: C++ JavaScript Java C# Julia Shell R TypeScript Scala Read the full article- Link in the bio

  6. What is Naive Bayes? Naive Bayes is ideal for practical application in text mining. However, it also involves an assumption that it is not possible to visualize in real-time data. Naive Bayes consists of the calculation of conditional probability from the pure product of individual probabilities of different components. The condition in such cases would imply complete independence for the features that are practically not possible or very difficult. Read the full article- Link in the bio

  7. Curious to learn more? Go to the link in the bio for the full article Follow us for more! @vasitum

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