Machine Learning Questions and Answers

Machine Learning Questions and Answers

Machine Learning is a hot technology that permits computers to study without delay from examples and revel in within the form of data. Traditional methods to programming rely on hardcoded guidelines, which set out a way to clear up a trouble, step-by-step. In evaluation, Machine learning structures are set a project, and given a massive quantity of data to apply as examples of the way this task may be finished or from which to stumble on styles. The system then learns how great to achieve the desired output. It may be concept of as narrow AI: machine learning supports shrewd systems, which are capable of analyze a particular function, given a selected set of data to examine from. Machine learning is a modern and fantastically sophisticated technological utility of a long set up belief look at the beyond to expect the destiny.

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What is Machine Learning?

What is Deep Learning?

Why machine learning?

What is difference between Supervised and unsupervised learning algorithms?

What is reinforcement learning?

What is Data Pre-processing technique for Machine Learning?

What are the important Data pre-processing techniques in Python Machine Learning?

Can you explain Rescaling Data technique in Data pre-processing?

What is Data Standardization in ML?

What is Data Normalization in ML?

What is Data Augmentation in ML?

How is Machine Learning (ML) different from Artificial Intelligence (AI)?

Explain the k-nearest algorithm different from the KNN clustering?

Can you explain how do you handle missing or corrupted data in a dataset?

What is dimensionality reduction?

What is PCA in ML?

What are the best python libraries used in Machine Learning?

What is Feature Engineering?

What is Feature Scaling?

What is Batch Normalization?

What is the F1 score?

Why is Naive Bayes so Naive?

What is difference between a parameter and a hyperparameter?

What are the smaller dataset techniques?

What is Pruning in Decision trees?

What is ROC?

Can you explain bias-variance trade-off?

How will you handle missing data?

What is Data structure? And what are the different types of Data structures supported in R programming?

What is Type I vs Type II error?

How would you handle an imbalanced data-set?

What is the difference between Probability and Likelihood?

What is Data set in ML?

What sentiment analysis?

hat is Natural language processing?

What are the best public data sets for Machine learning?

What are the differences between Machine learning and Artificial Intelligence?

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