Al Hejaz Harmony Interview Questions and Answers

Different types of learning include supervised learning, unsupervised learning, semi-supervised learning, reinforcement learning, and transfer learning.

• Supervised learning: Training a model using labeled data to make predictions or classifications.

• Unsupervised learning: Training a model on unlabeled data to discover patterns or relationships.

• Semi-supervised learning: Combining labeled and unlabeled data for training.

• Reinforcement learning: Training a model to make decisions b...read more

ML algorithms are techniques used to train models to make predictions or decisions based on data.

• Supervised learning algorithms: Linear regression, logistic regression, decision trees, random forests, support vector machines, k-nearest neighbors

• Unsupervised learning algorithms: K-means clustering, hierarchical clustering, principal component analysis

• Reinforcement learning algorithms: Q-learning, SARSA

• Deep learning algorithms: Convolutional neural networks, recurrent neural ne...read more

Naive Bayes is a probabilistic algorithm that uses Bayes' theorem to classify data based on prior knowledge.

• Naive Bayes assumes that all features are independent of each other.

• It is commonly used for text classification and spam filtering.

• There are three types of Naive Bayes classifiers: Gaussian, Multinomial, and Bernoulli.

• It is a fast and simple algorithm that works well with high-dimensional datasets.

• Naive Bayes can handle missing data and is not affected by irrelevant fea...read more

Regression is a statistical method used to analyze the relationship between a dependent variable and one or more independent variables.

• Regression is used to predict continuous numerical values.

• It helps in identifying the strength and direction of the relationship between variables.

• Linear regression is a common type of regression used to model the relationship between two variables.

• Examples of regression include predicting housing prices based on square footage and predicting ...read more

Overfitting occurs when a model learns the training data too well, leading to poor generalization. Underfitting happens when a model is too simple to capture the underlying patterns.

• Overfitting: Model performs well on training data but poorly on unseen data. Can be caused by a model being too complex or training for too long.

• Underfitting: Model is too simple to capture the underlying patterns in the data. Results in poor performance on both training and unseen data.

• Examples: ...read more