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posted on 7 Mar 2024
Types of performance testing in machine learning include cross-validation, hyperparameter tuning, and model evaluation metrics.
Cross-validation: Splitting the data into multiple subsets to train and test the model on different combinations.
Hyperparameter tuning: Adjusting the parameters of the model to optimize performance.
Model evaluation metrics: Using metrics like accuracy, precision, recall, and F1 score to evaluat
Deep learning is a subset of machine learning that uses neural networks to learn complex patterns from data.
Deep learning involves training neural networks with multiple layers to learn representations of data.
It is used in various applications such as image and speech recognition, natural language processing, and autonomous driving.
Examples of deep learning frameworks include TensorFlow, PyTorch, and Keras.
posted on 6 Jul 2022
I applied via Company Website and was interviewed in Jan 2022. There were 2 interview rounds.
My skills python and Java
posted on 10 May 2024
My favorite algorithm is Random Forest, which I have implemented for predicting customer churn in a telecom company.
Random Forest is an ensemble learning method that builds multiple decision trees and merges them together to get a more accurate and stable prediction.
I have implemented Random Forest in Python using scikit-learn library for a telecom company to predict customer churn based on various features like call d...
posted on 25 Jul 2024
Python arrays loops data structures
posted on 29 Mar 2023
I applied via Company Website and was interviewed in Mar 2023. There were 4 interview rounds.
Precision is the ratio of correctly predicted positive observations to the total predicted positive observations, while Recall is the ratio of correctly predicted positive observations to the all observations in actual class.
Precision focuses on the accuracy of positive predictions, while Recall focuses on the proportion of actual positives that were correctly identified.
Precision = TP / (TP + FP)
Recall = TP / (TP + FN...
Hyperparameters of Random Forest include number of trees, max depth of trees, minimum samples per leaf, and maximum features.
Number of trees: Determines the number of decision trees in the forest.
Max depth of trees: Controls the maximum depth of each decision tree.
Minimum samples per leaf: Specifies the minimum number of samples required to be at a leaf node.
Maximum features: Determines the maximum number of features t
I applied via Job Portal and was interviewed in Aug 2021. There were 2 interview rounds.
I applied via Campus Placement and was interviewed before Oct 2023. There was 1 interview round.
Two questions asked which have difficulty level is high
SQL joins are used to combine rows from two or more tables based on a related column between them.
Types of joins include INNER JOIN, LEFT JOIN, RIGHT JOIN, and FULL JOIN.
INNER JOIN returns rows when there is at least one match in both 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.
F...
Delete removes a record entirely, while update modifies an existing record.
Delete removes the entire record from the database
Update modifies specific fields of an existing record
Delete is irreversible, while update can be undone by another update
Example: Deleting a user account vs updating the user's email address
LLAMA models are used for large language model adaptation.
LLAMA models are used for adapting large language models to specific tasks or domains.
They are commonly used in natural language processing tasks such as text generation, translation, and sentiment analysis.
Examples of LLAMA models include GPT-3, BERT, and RoBERTa.
based on 1 interview
Interview experience
Software Engineer
3
salaries
| ₹5 L/yr - ₹7.2 L/yr |
Machine Learning Engineer
3
salaries
| ₹17 L/yr - ₹32 L/yr |
TCS
Accenture
Wipro
Cognizant