Answer / Monika

There are several approaches for machine learning. Here are some of them: 1) Supervised Learning (e.g., Linear Regression, Logistic Regression, Support Vector Machines), 2) Unsupervised Learning (e.g., K-means Clustering, Hierarchical Clustering, Principal Component Analysis), 3) Semi-Supervised Learning (combining supervised and unsupervised learning), 4) Reinforcement Learning (e.g., Q-Learning, Deep Q Networks), and 5) Transfer Learning (using pre-trained models for new tasks).

Why is naïve bayes machine learning algorithm naïve?

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What is bias-variance trade-off in machine learning?

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What is the baseline in machine learning?

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Which operating system is best for machine learning?

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Can you name some feature extraction techniques used for dimensionality reduction?

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What is regularization in machine learning?

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What is regularization? What kind of problems does regularization solve?

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What is classification and regression in machine learning?

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Can naive bayes be used for multiclass classification?

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What are collinearity and multicollinearity?

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Tell us what's a fourier transform?

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How do deductive and inductive machine learning differ?

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