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PCA is a dimensionality reduction technique, decision tree is a classification algorithm, and computer vision is a field of study focused on enabling computers to interpret and understand visual information.
PCA is used to reduce the number of variables in a dataset while retaining the most important information.
Decision trees are used to classify data based on a set of rules and conditions.
Computer vision involves usin...
I applied via Campus Placement and was interviewed before Sep 2020. There were 3 interview rounds.
I applied via Referral and was interviewed in Mar 2021. There were 4 interview rounds.
Data science is the field of extracting insights and knowledge from data using various techniques and tools.
Data science involves collecting, cleaning, and analyzing data to extract insights.
It uses various techniques such as machine learning, statistical modeling, and data visualization.
Data science is used in various fields such as finance, healthcare, and marketing.
Examples of data science applications include fraud...
Python and R are programming languages commonly used in data science and statistical analysis.
Python is a general-purpose language with a large community and many libraries for data manipulation and machine learning.
R is a language specifically designed for statistical computing and graphics, with a wide range of packages for data analysis and visualization.
Both languages are popular choices for data scientists and hav...
I applied via LinkedIn and was interviewed before Apr 2023. There was 1 interview round.
fbprophet is a forecasting model developed by Facebook that uses time series data to make predictions.
fbprophet is an open-source forecasting tool developed by Facebook's Core Data Science team.
It is based on an additive model where non-linear trends are fit with yearly, weekly, and daily seasonality, plus holiday effects.
fbprophet can be used to forecast traffic by providing historical data on traffic patterns and usi...
I applied via Approached by Company and was interviewed in Aug 2023. There was 1 interview round.
Logistic regression can be applied for multiclasstext classification by using one-vs-rest or softmax approach.
One-vs-rest approach: Train a binary logistic regression model for each class, treating it as the positive class and the rest as the negative class.
Softmax approach: Use the softmax function to transform the output of the logistic regression into probabilities for each class.
Evaluate the model using appropriate...
2 coding questions - one on binary tree and list operations.
Xgboost is a popular machine learning algorithm known for its speed and performance in handling large datasets.
Xgboost stands for eXtreme Gradient Boosting, which is an implementation of gradient boosted decision trees.
It is widely used in Kaggle competitions and other machine learning tasks due to its efficiency and accuracy.
Xgboost is known for its ability to handle missing data, regularization techniques, and parall...
I applied via Company Website and was interviewed before Mar 2023. There was 1 interview round.
L1 & L2 regularization are techniques used in machine learning to prevent overfitting by adding a penalty term to the cost function.
L1 regularization adds the absolute values of the coefficients as penalty term (Lasso regression)
L2 regularization adds the squared values of the coefficients as penalty term (Ridge regression)
L1 regularization encourages sparsity in the model, while L2 regularization tends to shrink the c...
Error metric is a measure used to evaluate the performance of a model by comparing predicted values to actual values.
Error metric quantifies the difference between predicted values and actual values.
Common error metrics include Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and R-squared.
Lower values of error metric indicate better performance of the model.
Error metric helps in und...
I applied via Company Website and was interviewed before Mar 2023. There were 3 interview rounds.
Yes, I have the right tech skills for the Data Scientist role.
Proficient in programming languages like Python, R, and SQL
Experience with data visualization tools like Tableau or Power BI
Knowledge of machine learning algorithms and statistical analysis techniques
Familiarity with big data technologies like Hadoop and Spark
Simple leetcode type sql, python questions
I applied via Naukri.com and was interviewed in Apr 2022. There were 4 interview rounds.
To work towards a random forest, you need to gather and preprocess data, select features, train individual decision trees, and combine them into an ensemble.
Gather and preprocess data from various sources
Select relevant features for the model
Train individual decision trees using the data
Combine the decision trees into an ensemble
Evaluate the performance of the random forest model
Bias-variance trade-off is the balance between overfitting and underfitting in a model.
Bias is the error due to assumptions made in the learning algorithm. Variance is the error due to sensitivity to small fluctuations in the training set.
High bias leads to underfitting, while high variance leads to overfitting.
The goal is to find the sweet spot where the model has low bias and low variance, which results in good gener...
I am a data scientist with expertise in machine learning and data analysis.
I have a strong background in statistics and mathematics.
I am proficient in programming languages such as Python and R.
I have experience working with large datasets and extracting insights from them.
I have developed predictive models for various industries, including finance and e-commerce.
I am skilled in data visualization and communicating com
I have a strong background in data analysis and machine learning, with a proven track record of delivering actionable insights.
I have a Master's degree in Data Science and have completed several projects involving data analysis and predictive modeling.
I am proficient in programming languages such as Python and R, as well as in using tools like TensorFlow and Tableau.
I have experience working with large datasets and hav...
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