10+ 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

Explanation of evaluation metrics, precision, recall, entropy, Gini impurity, bagging, boosting, validation vs test data, LSTM, GRU, K-means clustering, and importing CSV datasets.

• Evaluation metrics: used to measure the performance of machine learning models (e.g., accuracy, precision, recall, F1 score)

• Precision: ratio of true positive predictions to the total predicted positives (TP / (TP + FP))

• Recall: ratio of true positive predictions to the total actual positives (TP / (T...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

Transformer architecture is a neural network architecture used for natural language processing tasks. Positional encoders are used to encode the position of words in a sentence.

• Transformer architecture is based on the self-attention mechanism.

• It consists of an encoder and a decoder.

• Positional encoders are added to the input embeddings to encode the position of words in a sentence.

• They are computed using sine and cosine functions of different frequencies.

• Positional encoders he...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

PCA is a dimensionality reduction technique used to reduce the number of features in a dataset while preserving the most important information.

• PCA stands for Principal Component Analysis

• It works by finding the directions (principal components) in which the data varies the most

• These principal components are orthogonal to each other and capture the maximum variance in the data

• Feature selection can be done by selecting the top principal components that explain most of the varian...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

To overcome overfitting, use techniques like cross-validation, regularization, early stopping, and increasing training data.

• Use cross-validation to evaluate model performance on different subsets of data.

• Apply regularization techniques like L1 or L2 regularization to penalize large coefficients.

• Implement early stopping to stop training when validation error starts to increase.

• Increase training data to provide more diverse examples for the model to learn from.