Ace your interview for any machine learning position Given the increased demand for experts in the field of Machine Learning, more and more developers start looking for common questions on such interviews. In this story, I have listed the typical questions I and other developers got while applying for jobs in the field.

Given the increased demand for experts in the field of Machine Learning, more and more developers start looking for common questions on such interviews. In this story, I have listed the typical questions I and other developers got while applying for jobs in the field.

**Machine Learning** is a practice of using algorithms to organize data, learn from it, and make predictions about real-world problems. In comparison to finding a solution to each specific problem, the machine is trained using large amounts of data to find solutions on its own.

**Deep Learning** is a form of Machine Learning that is based on the principles of the human brain. In short, Deep Learning is a technique for implementing Machine Learning. Neural networks are an essential part of Deep learning, which is based on the principle of interconnected neurons.

To put it in simple terms, **precision** is the number of relevant entries over the total number of entries. Precision signifies the percentage of the results which are relevant. So if you search for “brown dogs” in Google and only seven out of ten images are brown dogs, then the precision is 7/10=0.7.

The **recall** is the number of retrieved instances among all relevant instances. In this case, we are looking for correct entries that were displayed to us from the total number of available correct entries. Say there are fifteen correct pages about brown dogs. Since we only got seven, our recall is 7/15=0.47.

In statistical analysis, F1 is a measure of a test’s accuracy. In the case of Machine Learning, it refers to the model’s performance.

It is calculated as a weighted average of the precision and recall of a model. The results closer to 1 are the best and closer to 0 are the worst. F1 score is best when we need to seek a balance between precision and recall, and there is an uneven class distribution.

**Machine Learning** is a practice of using algorithms to organize data, learn from it, and make predictions about real-world problems. In comparison to finding a solution to each specific problem, the machine is trained using large amounts of data to find solutions on its own.

**Deep Learning** is a form of Machine Learning that is based on the principles of the human brain. In short, Deep Learning is a technique for implementing Machine Learning. Neural networks are an essential part of Deep learning, which is based on the principle of interconnected neurons.

To put it in simple terms, **precision** is the number of relevant entries over the total number of entries. Precision signifies the percentage of the results which are relevant. So if you search for “brown dogs” in Google and only seven out of ten images are brown dogs, then the precision is 7/10=0.7.

The **recall** is the number of retrieved instances among all relevant instances. In this case, we are looking for correct entries that were displayed to us from the total number of available correct entries. Say there are fifteen correct pages about brown dogs. Since we only got seven, our recall is 7/15=0.47.

In statistical analysis, F1 is a measure of a test’s accuracy. In the case of Machine Learning, it refers to the model’s performance.

It is calculated as a weighted average of the precision and recall of a model. The results closer to 1 are the best and closer to 0 are the worst. F1 score is best when we need to seek a balance between precision and recall, and there is an uneven class distribution.

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