Senior Machine Learning Engineer

Senior Machine Learning Engineer Interview Questions and Answers

Updated 14 Nov 2024

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Q1. Data formats in big Data, why each format.

Ans.

Different data formats in big data are used for various purposes like storage efficiency, data processing speed, and compatibility with different systems.

JSON: Lightweight, human-readable, and widely supported for web applications.
Parquet: Columnar storage format for efficient querying and processing of large datasets.
Avro: Schema-based serialization format with support for complex data types.
ORC: Optimized Row Columnar format for high compression and fast processing.
CSV: Sim...read more

Q2. Machine learning coding question: using Associate rule mining

Ans.

Using associate rule mining to find patterns in data

Associate rule mining is a technique used to discover interesting relationships or patterns in large datasets
It is commonly used in market basket analysis to find associations between items purchased together
The output of associate rule mining is a set of rules in the form of IF-THEN statements
Support and confidence are two important measures used in associate rule mining
Support measures the frequency of occurrence of an ite...read more

Q3. What is k value in k means clustering

Ans.

k value in k means clustering represents the number of clusters to be formed in the data set.

The k value is a hyperparameter that needs to be specified by the user before running the algorithm.
It determines the number of centroids to be initialized in the data set.
Choosing the right k value is crucial for the performance of the clustering algorithm.
For example, if we set k=3 in k means clustering, the algorithm will try to partition the data into 3 clusters.

Q4. Leet code - Most freq elem in a list

Ans.

Find the most frequent element in a list of strings.

Iterate through the list and count the frequency of each element using a dictionary.
Track the element with the highest frequency as you iterate.
Return the element with the highest frequency.

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Q5. Explain about tree boosted algorithms

Ans.

Tree boosted algorithms are ensemble learning methods that combine multiple decision trees to improve predictive accuracy.

Tree boosted algorithms, such as Gradient Boosting and XGBoost, work by sequentially adding decision trees to correct the errors made by the previous trees.
They are popular in machine learning competitions due to their high predictive accuracy and ability to handle complex relationships in data.
Boosting algorithms like AdaBoost and LightGBM are also exampl...read more

Q6. What is pip freeze?

Ans.

pip freeze is a command in Python that lists all installed packages and their versions in a requirements.txt format.

Used to freeze the current state of installed packages for reproducibility
Helps in sharing the list of dependencies with others
Commonly used in virtual environments

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Q7. Explain bagging and boosting

Ans.

Bagging and boosting are ensemble learning techniques used to improve the performance of machine learning models by combining multiple weak learners.

Bagging (Bootstrap Aggregating) involves training multiple instances of the same model on different subsets of the training data and then averaging the predictions to reduce variance.
Boosting involves training multiple weak learners sequentially, with each learner focusing on the mistakes made by the previous ones. The final pred...read more

Q8. Knn algorithm using python

Ans.

KNN algorithm is a simple and effective machine learning algorithm for classification and regression tasks.

KNN stands for K-Nearest Neighbors.
It is a non-parametric, lazy learning algorithm.
Works by finding the K closest training examples in feature space to a given input data point.
Classification: Assign the most common class among the K nearest neighbors.
Regression: Take the average of the K nearest neighbors' target values.
Python libraries like scikit-learn provide impleme...read more