Senior Machine Learning Engineer Interview Questions and Answers

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

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

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.

Clustering is an unsupervised learning technique that groups similar data points based on their features.

• Clustering algorithms include K-Means, Hierarchical Clustering, and DBSCAN.

• K-Means requires specifying the number of clusters (k) beforehand.

• Hierarchical Clustering creates a tree of clusters, useful for visualizing data relationships.

• DBSCAN identifies clusters based on density, making it effective for irregularly shaped clusters.

Understanding prefix and suffix problems in strings is crucial for efficient algorithm design.

• A prefix is a substring that starts from the beginning of the string. Example: In 'hello', 'he' is a prefix.

• A suffix is a substring that ends at the end of the string. Example: In 'hello', 'lo' is a suffix.

• Common problems include finding the longest common prefix or suffix among an array of strings.

• For example, given ['flower', 'flow', 'flight'], the longest common prefix is 'fl'.

• Suf...read more

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

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

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