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Machine Learning Interview Questions
Q.
What are the Advantages/Disadvantages of a n-gram model
Q.
What happens to new words that appear in Test dataset but are not present in Training Data?
Q.
What is Lemmatization?
Q.
How to identify Stop Words?
Q.
What is the problem with using a generic list of stop words?
Q.
What is Vector Normalization? How is that useful?
Q.
In what cases (and why) does using Binary Occurrence instead of TF-IDF makes more sense?
Q.
When to use Ridge Regression vs Lasso?
Q.
How would you perform feature selection using Lasso?
Q.
What is Elastic-net? Why is it better in comparison to Ridge and Lasso?
Q.
How does Machine Learning differ from Classical Statistics and Deep Learning?
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Explore Questions by Topics
Computer Vision
(15)
Generative AI
(4)
Reinforcement Learning
(13)
Machine Learning Basics
(18)
–
Deep Learning
(78)
DL Basics
(16)
–
DL Architectures
(21)
Feedforward Network / MLP
(3)
Sequence models
(6)
Transformers
(11)
DL Training and Optimization
(39)
–
Natural Language Processing
(34)
NLP Data Preparation
(18)
–
Supervised Learning
(115)
–
Regression
(41)
Linear Regression
(26)
Generalized Linear Models
(9)
Regularization
(6)
–
Classification
(70)
Logistic Regression
(10)
Support Vector Machine
(9)
Ensemble Learning
(24)
Other Classification Models
(9)
Classification Evaluations
(9)
–
Unsupervised Learning
(64)
–
Clustering
(40)
Distance Measures
(9)
K-Means Clustering
(10)
Hierarchical Clustering
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Gaussian Mixture Models
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Clustering Evaluations
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Dimensionality Reduction
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Statistics
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Data Preparation
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Feature Engineering
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Sampling Techniques
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Other Questions in Machine Learning Interview Questions
What is the cost function used in Poisson Regression?
What is Multi-Task Learning?
What does Centering and Scaling mean? What is the individual effect of each of those?
What is Random Projection? Discuss its advantages and disadvantages?
What are the advantages and disadvantages of Decision Tree model?
Why does multicollinearity result in poor estimates of coefficients in linear regression?