Great Learning Interview Questions, Process, and Tips

Q1. Basic about your self
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Q2. Previous work experience
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Q1. Previous experience, achievements, target, communications skills, sales pitching, AGM will give some scenario
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Q1. I can't!! Sorry but it's confidential.
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Q1. Sorry but it's confidential.
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Q1. Interpretation of classification metrics like accuracy, precision, recall
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Classification metrics like accuracy, precision, and recall are used to evaluate the performance of a classification model.
• Accuracy measures the overall correctness of the model's predictions.

• Precision measures the proportion of true positive predictions out of all positive predictions.

• Recall measures the proportion of true positive predictions out of all actual positive instances.
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Q2. Difference between bagging and boosting
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Bagging and boosting are ensemble learning techniques used to improve the performance of machine learning models by combining multiple weak learners.
• Bagging (Bootstrap Aggregating) involves training multiple models independently on different subsets of the training data and then combining their predictions through averaging or voting.

• Boosting involves training multiple models sequentially, where each subsequent model c...read more
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Q3. Difference between R-squared and Adjusted R-squared
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R-squared measures the proportion of variance explained by the model, while Adjusted R-squared adjusts for the number of predictors in the model.
• R-squared is the proportion of variance in the dependent variable that is predictable from the independent variables. It ranges from 0 to 1, with 1 indicating a perfect fit.

• Adjusted R-squared penalizes the addition of unnecessary predictors to the model, providing a more accur...read more
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Q4. Feature Selection Techniques
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Feature selection techniques help in selecting the most relevant features for building predictive models.
• Filter methods: Select features based on statistical measures like correlation, chi-squared test, etc.

• Wrapper methods: Use a specific model to evaluate the importance of features by adding or removing them iteratively.

• Embedded methods: Feature selection is integrated into the model training process, like LASSO regre...read more
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Q1. Various types of joins in SQL
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Various types of joins in SQL include inner join, outer join, left join, right join, and full join.
• Inner join: Returns rows when there is a match in both tables.

• Outer join: Returns all rows when there is a match in one of the tables.

• Left join: Returns all rows from the left table and the matched rows from the right table.

• Right join: Returns all rows from the right table and the matched rows from the left table.

• Full joi...read more
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Q2. SQL query on self join
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Q3. Bias and Variance Tradeoff
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Q4. Model interpretability
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Python coding, lambda function, closures

A situation will be provided, with details about a learner's work & education profile, where final output is recommended resolution

Q1. Situation-based questions about critical cases
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Q1. General Knowledge question; test of communication skills
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Q1. HR basic round discussion on job specification
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Marketing topics/ ppt create and submit

Q1. Previous company experience and job details
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