What are the advantages of neural networks?

Require less formal statistical training Have the ability to detect nonlinear relationships between variables Detect all possible interactions between predictor variables Availability of multiple training algorithms When asked about the advantages of neural networks in an artificial intelligence interview, you could mention several key points: Non-linearity: Neural networks are capable of learning complex non-linear relationships … Read more

List different methods for sequential supervised learning

Sliding window methods Recurrent sliding windows methods Hidden Markov models Maximum entropy Markov models Conditional random fields Graph transformer networks In a supervised learning setting where data is sequential (i.e., ordered or time-dependent), several methods can be employed for modeling and prediction. Here are some commonly used methods for sequential supervised learning: Autoregressive Models: These … Read more

What methods are used for reducing dimensionality?

Dimensionality reduction is the process of reducing the number of random variables. We can reduce dimensionality using techniques such as missing values ratio, low variance filter, high correlation filter, random forest, principal component analysis, etc. Reducing dimensionality is a crucial aspect of data preprocessing in various machine learning and artificial intelligence tasks. Several methods can … Read more

What is a recommendation system?

A recommendation system is an information filtering system that is used to predict user preference based on choice patterns followed by the user while browsing/using the system. A recommendation system is a type of artificial intelligence algorithm or system designed to predict and suggest items or actions that a user might be interested in, based … Read more

Can you name three feature selection techniques in Machine Learning?

Univariate Selection Feature Importance Correlation Matrix with Heatmap Certainly! Three common feature selection techniques in machine learning are: Filter Methods: These methods select features based on their statistical properties, such as correlation, chi-square tests, or information gain. Examples include Pearson correlation coefficient, chi-square test, and mutual information. Wrapper Methods: These methods involve selecting subsets of … Read more