Technical Interview Questions
Independent component analysis Principal component analysis Kernel-based principal component analysis In the context of dimensionality reduction, several extraction techniques are commonly employed to reduce the number of features or variables while preserving relevant information in the dataset. Here’s a list of some widely used techniques: Principal Component Analysis (PCA): PCA is a linear technique that … Read more
Naive Bayes Machine Learning algorithm is a powerful algorithm for predictive modeling. It is a set of algorithms with a common principle based on Bayes Theorem. The fundamental Naive Bayes assumption is that each feature makes an independent and equal contribution to the outcome. In the context of an Artificial Intelligence interview question, a concise … Read more
Logistic regression Linear regression Decision trees Support vector machines Naive Bayes, and so on When answering this question in an interview, it’s essential to demonstrate your understanding of various machine learning algorithms. Here’s a list of some common machine learning algorithms you could mention: Linear Regression Logistic Regression Decision Trees Random Forest Support Vector Machines … Read more
Deep Learning is a subset of Machine Learning which is used to create an artificial multi-layer neural network. It has self-learning capabilities based on previous instances, and it provides high accuracy. A concise and accurate answer to the question “What is Deep Learning?” for an artificial intelligence interview could be: “Deep learning is a subset … Read more
Supervised Learning Unsupervised Learning Semi-supervised Learning Reinforcement Learning Transduction Learning to Learn In machine learning, there are several algorithmic techniques used for various tasks. Here’s a comprehensive list of some common algorithm techniques: Supervised Learning Algorithms: Regression (Linear Regression, Polynomial Regression, Ridge Regression, Lasso Regression, etc.) Classification (Logistic Regression, Decision Trees, Random Forest, Support Vector … Read more