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Basic activities for app optimization include keyword research, A/B testing, performance monitoring, and user feedback analysis.
Conduct keyword research to identify relevant keywords for app store optimization
Implement A/B testing to compare different app store listings, screenshots, and descriptions
Monitor app performance metrics such as downloads, retention rates, and user engagement
Analyze user feedback and reviews ...
I applied via Naukri.com and was interviewed before Mar 2022. There were 2 interview rounds.
I am a highly motivated and results-driven Inside Sales Executive with a proven track record of exceeding sales targets.
Experienced in building and maintaining strong customer relationships
Skilled in identifying customer needs and providing tailored solutions
Proficient in using CRM software to track sales activities and manage leads
Consistently meet or exceed sales quotas
Excellent communication and negotiation skills
Ab...
I chose sales because I enjoy building relationships and helping customers find solutions to their problems.
I have always been a people person and enjoy interacting with others.
I find satisfaction in helping customers find the right product or service that meets their needs.
Sales allows me to constantly learn and adapt to new challenges and market trends.
I am motivated by the opportunity to earn commission and be rewar...
I was interviewed in Jun 2021.
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I applied via Naukri.com and was interviewed in Sep 2024. There were 4 interview rounds.
I will do my best in the job
I will do my work currently
I am interested in the job importens
To improve the job as a Telecaller, one can focus on enhancing communication skills, building rapport with customers, and consistently meeting targets.
Enhance communication skills through training and practice
Build rapport with customers by actively listening and showing empathy
Consistently meet targets by setting achievable goals and staying organized
Seek feedback from supervisors and implement suggestions for improve
I was interviewed in Dec 2023.
posted on 20 Feb 2024
I applied via LinkedIn and was interviewed in Jan 2024. There were 3 interview rounds.
Assignment was shared and was requested to be submitted in 3 days
I applied via Approached by Company and was interviewed in Sep 2023. There were 3 interview rounds.
I applied via Approached by Company and was interviewed before Mar 2023. There were 3 interview rounds.
I applied via LinkedIn and was interviewed in Feb 2022. There were 2 interview rounds.
A typical day of a technical writer involves creating and editing technical documentation using various tools and meeting technical requirements.
Creating and editing technical documentation such as user manuals, guides, and tutorials
Collaborating with subject matter experts to gather information
Using tools like Microsoft Word, Adobe FrameMaker, and MadCap Flare
Ensuring documentation meets technical requirements and ind...
I applied via Referral and was interviewed in Oct 2021. There were 5 interview rounds.
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...
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...
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...
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...
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...
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