Great Learning Interview Questions, Process, and Tips

Looking for new challenges and growth opportunities. Great Learning's vision and culture align with my career goals.

• Seeking new challenges and growth opportunities

• Great Learning's vision and culture align with my career goals

• Excited about the potential to contribute to a fast-growing company

• Current company lacks opportunities for career advancement

Starting and scaling an After School Activities Centre

• Conduct market research to identify demand and competition

• Develop a business plan including budget, staffing, and marketing strategy

• Secure a location and necessary permits

• Hire qualified staff and purchase necessary equipment and supplies

• Offer a variety of activities to appeal to a wide range of interests

• Establish partnerships with local schools and community organiz...read more

To design a sales funnel, I would start by identifying the target audience and their needs, then create a clear path to conversion.

• Identify target audience and their needs

• Create a clear path to conversion

• Use data to optimize the funnel

• Test and iterate to improve results

Using machine learning to prioritize leads and create a Next Gen Experience for V2

• Implement machine learning algorithms to analyze lead data and prioritize them based on their likelihood to convert

• Use customer feedback and data analysis to identify pain points and areas for improvement in the current product

• Develop a roadmap for V2 that addresses these pain points and incorporates new features and technologies

• Measure s...read more

Discussed Speech to Text for Sales Call Efficacy and Salary Expectations

• Discussed using Speech to Text technology to evaluate sales call effectiveness

• Also briefly discussed salary expectations

• Potential benefits of using Speech to Text include improved sales training and performance analysis

• Examples of Speech to Text software include Dragon NaturallySpeaking and Google Cloud Speech-to-Text

• Salary expectations will depend

Answering questions on weight loss, cultural fit, career aspirations, and future plans.

• Discussed my personal experience with weight loss and how it relates to the product

• Highlighted my ability to adapt to different cultures and work with diverse teams

• Shared my long-term career goals and how this role fits into them

• Outlined my vision for where I see myself in 5-7 years and how I plan to get there

Ensemble techniques combine multiple models to improve prediction accuracy.

• Ensemble techniques can be used with various types of models, such as decision trees, neural networks, and support vector machines.

• Common ensemble techniques include bagging, boosting, and stacking.

• Bagging involves training multiple models on different subsets of the data and combining their predictions through averaging or voting.

• Boosting invol...read more

Ensemble techniques combine multiple models to improve prediction accuracy.

• Bagging: Bootstrap Aggregating

• Boosting: AdaBoost, Gradient Boosting

• Stacking: Meta-model combines predictions of base models

• Voting: Combining predictions of multiple models by majority voting

Bagging is a technique used in machine learning to improve the stability and accuracy of a model by combining multiple models.

• Bagging stands for Bootstrap Aggregating.

• It involves creating multiple subsets of the original dataset by randomly sampling with replacement.

• Each subset is used to train a separate model, and the final prediction is the average of all the predictions made by each model.

• Bagging reduces overfittin...read more

Boosting is an ensemble learning technique that combines multiple weak models to create a strong model.

• Boosting iteratively trains weak models on different subsets of data

• Each subsequent model focuses on the misclassified data points of the previous model

• Final prediction is made by weighted combination of all models

• Examples include AdaBoost, Gradient Boosting, XGBoost

Bias is error due to erroneous assumptions in the learning algorithm. Variance is error due to sensitivity to small fluctuations in the training set.

• Bias is the difference between the expected prediction of the model and the correct value that we are trying to predict.

• Variance is the variability of model prediction for a given data point or a value which tells us spread of our data.

• High bias can cause an algorithm to m...read more

Classification techniques are used to categorize data into different classes or groups based on certain features or attributes.

• Common classification techniques include decision trees, logistic regression, k-nearest neighbors, and support vector machines.

• Classification can be binary (two classes) or multi-class (more than two classes).

• Evaluation metrics for classification include accuracy, precision, recall, and F1 scor...read more

Random forest is an ensemble learning method for classification, regression and other tasks.

• Random forest builds multiple decision trees and combines their predictions to improve accuracy.

• It uses bagging technique to create multiple subsets of data and features for each tree.

• Random forest reduces overfitting and is robust to outliers and missing values.

• It can handle high-dimensional data and is easy to interpret featur...read more