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I applied via LinkedIn and was interviewed in Jul 2024. There were 2 interview rounds.
Developed a machine learning model to predict customer churn for a telecom company
Used historical customer data to train the model
Implemented various classification algorithms such as logistic regression, random forest, and XGBoost
Evaluated model performance using metrics like accuracy, precision, recall, and F1 score
I applied via Referral and was interviewed before Aug 2023. There were 2 interview rounds.
AMAT is a leading provider of semiconductor manufacturing equipment and services.
AMAT is known for its cutting-edge technology and innovation in the semiconductor industry.
I appreciate AMAT's commitment to research and development, constantly pushing the boundaries of what is possible.
The company has a strong global presence and a track record of delivering high-quality products and services.
AMAT's focus on sustainabil...
I enjoy working in a collaborative environment where I can use my analytical skills to solve complex problems.
I thrive in environments where I can work with a team to brainstorm ideas and solutions.
I appreciate opportunities to use data analysis techniques to uncover insights and drive decision-making.
I value a work culture that encourages continuous learning and professional growth.
I find satisfaction in overcoming ch...
I applied via Referral and was interviewed before May 2023. There were 3 interview rounds.
Decision tree is a predictive modeling tool that uses a tree-like graph of decisions and their possible consequences.
Decision tree splits data into subsets based on the value of a certain attribute
It recursively divides data into smaller subsets until a stopping criterion is met
Each internal node represents a decision based on an attribute, and each leaf node represents the outcome
Pressure generally increases with temperature due to the kinetic energy of gas molecules.
Pressure is directly proportional to temperature in a closed system (Boyle's Law).
As temperature increases, gas molecules move faster and collide with the container walls more frequently, increasing pressure.
For example, a balloon inflated indoors may burst when taken outside on a hot day due to increased pressure from higher tempe
What people are saying about Applied Materials
I applied via Referral and was interviewed before Oct 2023. There was 1 interview round.
Adaboost is a machine learning algorithm that combines multiple weak learners to create a strong learner.
Adaboost stands for Adaptive Boosting.
It works by adjusting the weights of incorrectly classified instances so that subsequent weak learners focus more on them.
The final prediction is made by combining the predictions of all the weak learners, weighted by their accuracy.
Example: Adaboost is commonly used in face det
Printing a binary tree in different orders
Use inorder traversal to print the binary tree in ascending order
Use preorder traversal to print the binary tree in root-left-right order
Use postorder traversal to print the binary tree in left-right-root order
I applied via Company Website and was interviewed before Jul 2023. There were 3 interview rounds.
Binary tree question was asked
I applied via Campus Placement and was interviewed in Jul 2024. There were 2 interview rounds.
Few standard dsa questions were asked.
Projects are specific tasks or assignments that require a set of skills and resources to achieve a particular goal or outcome.
Projects involve defining objectives and deliverables
They require planning, execution, and monitoring
Projects often have timelines and budgets
Examples: Data analysis project to identify customer trends, Project to implement a new software system
I am a data analyst with a strong background in statistics and data visualization.
I have a Bachelor's degree in Statistics from XYZ University.
I have 3 years of experience working as a data analyst at ABC Company.
Proficient in using tools like Excel, SQL, and Tableau for data analysis.
I have experience in creating reports and dashboards to present data insights to stakeholders.
I applied via LinkedIn and was interviewed before Dec 2023. There was 1 interview round.
Case study about some machine learning project which i did in thr past.
I applied via Naukri.com and was interviewed in Jun 2022. There were 2 interview rounds.
Correlation is a statistical measure that shows how two variables are related to each other.
Correlation measures the strength and direction of the relationship between two variables.
It ranges from -1 to 1, where -1 indicates a perfect negative correlation, 0 indicates no correlation, and 1 indicates a perfect positive correlation.
Correlation does not imply causation, meaning that just because two variables are correlat...
Multicollinearity is a phenomenon where two or more independent variables in a regression model are highly correlated.
It can lead to unstable and unreliable estimates of regression coefficients.
It can also make it difficult to determine the individual effect of each independent variable on the dependent variable.
It can be detected using correlation matrices or variance inflation factors (VIF).
Solutions include removing...
P-values are a statistical measure that helps determine the likelihood of obtaining a result by chance.
P-values range from 0 to 1, with a smaller value indicating stronger evidence against the null hypothesis.
A p-value of 0.05 or less is typically considered statistically significant.
P-values are commonly used in hypothesis testing to determine if a result is statistically significant or not.
LSTMs are better than RNNs due to their ability to handle long-term dependencies.
LSTMs have a memory cell that can store information for long periods of time.
They have gates that control the flow of information into and out of the cell.
This allows them to selectively remember or forget information.
Vanilla RNNs suffer from the vanishing gradient problem, which limits their ability to handle long-term dependencies.
LSTMs ...
Pooling in CNNs has learning but reduces spatial resolution.
Pooling helps in reducing overfitting by summarizing the features learned in a region.
Max pooling retains the strongest feature in a region while average pooling takes the average.
Pooling reduces the spatial resolution of the feature maps.
Pooling can also help in translation invariance.
However, too much pooling can lead to loss of important information.
Optimizers are used to improve the efficiency and accuracy of the training process in machine learning models.
Optimizers help in finding the optimal set of weights for a given model by minimizing the loss function.
They use various techniques like momentum, learning rate decay, and adaptive learning rates to speed up the training process.
Optimizers also prevent the model from getting stuck in local minima and help in ge...
KNN during training stores all the data points and their corresponding labels to use for prediction.
KNN algorithm stores all the training data points and their corresponding labels.
It calculates the distance between the new data point and all the stored data points.
It selects the k-nearest neighbors based on the calculated distance.
It assigns the label of the majority of the k-nearest neighbors to the new data point.
Small change in one dimension causing drastic difference in model output. Explanation and solution.
This is known as sensitivity to input
It can be caused by non-linearities in the model or overfitting
Regularization techniques can be used to reduce sensitivity
Cross-validation can help identify overfitting
Ensemble methods can help reduce sensitivity
It is generally a bad thing as it indicates instability in the model
Slope and gradient are both measures of the steepness of a line, but slope is a ratio while gradient is a vector.
Slope is the ratio of the change in y to the change in x on a line.
Gradient is the rate of change of a function with respect to its variables.
Slope is a scalar value, while gradient is a vector.
Slope is used to describe the steepness of a line, while gradient is used to describe the direction and magnitude o...
Boosting and bagging are ensemble learning techniques used to improve model performance.
Bagging involves training multiple models on different subsets of the data and averaging their predictions.
Boosting involves training multiple models sequentially, with each model focusing on the errors of the previous model.
Bagging reduces variance and overfitting, while boosting reduces bias and underfitting.
Examples of bagging al...
A logarithm is a mathematical function that measures the relationship between two quantities.
Logarithms are used to simplify complex calculations involving large numbers.
They are used in linear algebra to transform multiplicative relationships into additive ones.
Logarithms are also used in data analysis to transform skewed data into a more normal distribution.
Common logarithms use base 10, while natural logarithms use
Gradients are the changes in values of a function with respect to its variables.
Gradients are used in calculus to measure the rate of change of a function.
They are represented as vectors and indicate the direction of steepest ascent.
Gradients are used in optimization problems to find the minimum or maximum value of a function.
They are also used in physics to calculate the force acting on a particle.
Gradients can be cal
I applied via Referral and was interviewed in Apr 2024. There was 1 interview round.
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