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Usarawgi911 Machine Learning Interview Questions
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Machine Learning - Questions and Answers
Q&A Topics:
Supervised vs Unsupervised
Bias vs Variance
Underfitting vs overfitting
Tackle overfitting
L1 regulariziation
L2 regularization
L1 (Lasso) vs L2 (Ridge)
Dropout
Why feature reduction / dimensionality reduction
How feature reduction / dimensionality reduction
AUC ROC
No Free Lunch Theorum
Empirical Risk
Class imbalance tackle
Selection bias
What is random forest? Why "random"?
Decision trees vs Logisitc regression
SVM vs Logistic Regression
Kernel trick
Primal vs Dual version of classifier
Parametric vs Non-parametric
KNN
KMeans
KNN vs Kmeans
Smoothing Time series
Gradient descent
Backpropagation
Regularization
Normalization
Batch Normalization
Vanishing Gradients
Exploding gradients
Other Topics:
Dimensionality reduction
PCA
Kernel PCA
Ridge regression
L1 vs L2 loss
Activation Functions
Advantages of RelU
Thresholds for a classifier
Interpretation of an ROC area under the curve as an integral
Confusion matrix
More elaborate and extensive Questions:
General Machine Learning
Deep Learning
Mathematics for Machine Learning
Open Source Agenda is not affiliated with "Usarawgi911 Machine Learning Interview Questions" Project. README Source:
usarawgi911/machine-learning-interview-questions
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