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HTML
CSS
Python
PHP
MySQL
Linux
Shobha
The best way to predict the future is to invent it
Explain how principal component analysis (PCA) works for dimensionality reduction.
RELATED INTERVIEW QUESTIONS
How do you evaluate the effectiveness of an outlier detection model?
Explain the difference between supervised and unsupervised learning with examples.
What is the role of Natural Language Processing (NLP) in data analytics?
Explain the concept of ensemble learning and its applications in classification and regression problems.
Explain the concept of reinforcement learning and its applications in analytics.
How does feature scaling improve the performance of gradient-based learning algorithms?
What are the best practices for implementing cloud-based analytics for large-scale datasets?
How would you visualize high-dimensional data using dimensionality reduction techniques?
What are the different types of feature engineering methods and how do they impact model performance?
What is gradient descent, and how does it work in the training of machine learning models? How do you decide the learning rate?
What are the types of biases in data analysis and how can they be minimized?
What are LSTM networks, and how do they handle sequential data?
What is the role of Bayesian methods in data analytics, and how do they compare to frequentist approaches?
How do you perform feature importance analysis for model interpretability?
How do you address skewed data distributions in machine learning models?
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