Applied Materials Data Scientist Interview Questions and Answers

Q1. What is Adaboost?
Ans. 
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
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Q2. Print the binary tree in different order
Ans. 
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
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Binary tree question was asked

Q1. Several tech question related to past projects and random forest etc were asked.
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Q1. Tell me about yourself and several other questions
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Few standard dsa questions were asked.

Q1. Explain projects
Ans. 
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
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Q2. Introduce urself
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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.
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Case study about some machine learning project which i did in thr past.

Q1. What is correlation(in plain english)?
Ans. 
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...read more
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Q2. What is multi-collinearity?
Ans. 
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...read more
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Q3. What are p-values? explain it in plain english without bringing up machine learning?
Ans. 
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.
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Q4. How are LSTMs better than RNNs? what makes them better? how does LSTMs do better what they do better than vanilla RNNs?
Ans. 
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 ...read more
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Q5. Does pooling in CNNs have any learning?
Ans. 
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.
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Q6. Why does optimisers matter? what's their purpose? what do they do in addition to weights-updation that the vanilla gradient and back-prop does?
Ans. 
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...read more
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Q7. What does KNN do during training?
Ans. 
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.
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Q8. You have two different vectors with only small change in one of the dimensions. but, the predictions/output from the model is drastically different for each vector. can you explain why this can be the case...read more
Ans. 
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
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Q9. Slope vs gradient (again not in relation to machine learning, and in plain english)
Ans. 
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...read more
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Q10. How are boosting and bagging algorithms different?
Ans. 
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...read more
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Q11. What is a logarithm? (in linear algebra) what is it's significance and what purpose does it serve?
Ans. 
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
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Q12. What are gradients? (not in relation to machine learning)
Ans. 
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
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Q1. Time Series forecasting questions
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