What is Machine Learning? Answer: Machine Learning is a subset of artificial intelligence that enables computers to learn from data and improve their performance over time.
What is Supervised Learning? Answer: Supervised learning is where the algorithm learns from labeled data, making predictions based on input-output pairs.
What is Unsupervised Learning? Answer: Unsupervised learning is where the algorithm learns from unlabeled data to find patterns and relationships without specific output labels.
What is Reinforcement Learning? Answer: Reinforcement learning is where the algorithm learns by interacting with an environment and receiving feedback in the form of rewards or penalties.
Explain Decision Trees. Answer: Decision Trees are a tree-like model used for classification and regression tasks, where each internal node represents a decision based on features, leading to leaf nodes representing outcomes.
What is Overfitting? Answer: Overfitting occurs when a model performs well on training data but poorly on new, unseen data due to memorizing noise rather than learning patterns.
How do you prevent Overfitting? Answer: Techniques include using more data, cross-validation, regularization, and feature selection.
What is Cross-Validation? Answer: Cross-validation is a technique to assess a model’s performance by splitting data into multiple subsets for training and testing.
What is the Bias-Variance Tradeoff? Answer: The Bias-Variance Tradeoff refers to the tradeoff between a model’s ability to fit the training data (low bias) and its ability to generalize to new data (low variance).
What is Gradient Descent? Answer: Gradient Descent is an optimization algorithm used to minimize the loss function in a model by adjusting the model’s parameters in the direction of steepest descent.
Explain Support Vector Machines (SVM). Answer: SVM is a supervised learning algorithm used for classification tasks by finding the hyperplane that best separates classes.
What is K-Nearest Neighbors (KNN)? Answer: KNN is a simple supervised learning algorithm used for classification and regression by considering the majority class or average value of k-nearest data points.
What is Naive Bayes? Answer: Naive Bayes is a probabilistic classifier based on Bayes’ theorem with the assumption of independence between features.
Explain Random Forest. Answer: Random Forest is an ensemble learning method that constructs multiple decision trees and combines their predictions for improved accuracy and robustness.
What is Deep Learning? Answer: Deep Learning is a subset of Machine Learning that uses artificial neural networks to model complex patterns and representations.
What is a Neural Network? Answer: A Neural Network is a computational model inspired by the human brain’s structure, used for various Machine Learning tasks.
What is Backpropagation? Answer: Backpropagation is a training algorithm for neural networks that adjusts the model’s weights based on the error between predicted and actual output.
What is Dropout in Neural Networks? Answer: Dropout is a regularization technique used in training neural networks to randomly ignore or “drop out” certain neurons during training to reduce overfitting.
Explain Batch Normalization. Answer: Batch Normalization is a technique used to normalize the inputs to each layer in a neural network to stabilize and speed up training.
What is Transfer Learning? Answer: Transfer Learning is a technique where a pre-trained model is used as a starting point for a related task, saving time and resources.
What is a Loss Function? Answer: A Loss Function measures the error between predicted and actual values, guiding the model’s parameter updates during training.
What is a Cost Function? Answer: A Cost Function is the average of the Loss Function over the entire dataset and is minimized during training.
What is Mean Squared Error (MSE)? Answer: MSE is a Loss Function commonly used for regression tasks, calculating the average squared difference between predicted and actual values.
What is Cross-Entropy Loss? Answer: Cross-Entropy Loss is used in classification tasks, measuring the dissimilarity between predicted probabilities and true labels.
What is L1 Regularization? Answer: L1 Regularization adds the absolute values of the model’s weights to the Loss Function to prevent overfitting and encourage sparsity.
What is L2 Regularization? Answer: L2 Regularization adds the squared values of the model’s weights to the Loss Function to prevent overfitting.
What is Grid Search? Answer: Grid Search is a hyperparameter tuning technique that exhaustively searches a predefined set of hyperparameter combinations to find the best model performance.
What is Feature Engineering? Answer: Feature Engineering is the process of selecting and transforming input features to improve model performance.
What is One-Hot Encoding? Answer: One-Hot Encoding is a technique used to convert categorical variables into binary vectors for machine learning algorithms.
What is a Confusion Matrix? Answer: A Confusion Matrix is a table used to evaluate the performance of a classification model by comparing predicted and true labels.
What is Precision and Recall? Answer: Precision is the ratio of true positives to all positive predictions, while Recall is the ratio of true positives to all actual positive samples.
What is F1 Score? Answer: F1 Score is the harmonic mean of Precision and Recall, providing a balance between the two metrics.
What is ROC Curve? Answer: ROC Curve (Receiver Operating Characteristic Curve) is a graphical representation of a classifier’s performance at different classification thresholds.
What is AUC-ROC? Answer: AUC-ROC (Area Under the ROC Curve) is a metric used to assess the overall performance of a classifier.
What is Gradient Boosting? Answer: Gradient Boosting is an ensemble learning technique that combines weak learners (usually decision trees) to create a strong predictive model.
What is XGBoost? Answer: XGBoost is an optimized implementation of Gradient Boosting, known for its high performance and accuracy.
Explain Q-Learning. Answer: Q-Learning is a model-free, off-policy reinforcement learning algorithm used to learn optimal action-value functions for Markov Decision Processes.
What is GAN (Generative Adversarial Network)? Answer: GAN is a type of generative model that consists of a generator and a discriminator, trained together in a competitive process.
What is Word Embedding? Answer: Word Embedding is a technique used to represent words as dense vectors, capturing semantic relationships for natural language processing tasks.
What is Dropout in Neural Networks? Answer: Dropout is a regularization technique used in training neural networks to randomly ignore or “drop out” certain neurons during training to reduce overfitting.
What is Transfer Learning? Answer: Transfer Learning is a technique where a pre-trained model is used as a starting point for a related task, saving time and resources.
What is a Loss Function? Answer: A Loss Function measures the error between predicted and actual values, guiding the model’s parameter updates during training.
What is a Cost Function? Answer: A Cost Function is the average of the Loss Function over the entire dataset and is minimized during training.
What is Mean Squared Error (MSE)? Answer: MSE is a Loss Function commonly used for regression tasks, calculating the average squared difference between predicted and actual values.
What is Cross-Entropy Loss? Answer: Cross-Entropy Loss is used in classification tasks, measuring the dissimilarity between predicted probabilities and true labels.
What is L1 Regularization? Answer: L1 Regularization adds the absolute values of the model’s weights to the Loss Function to prevent overfitting and encourage sparsity.
What is L2 Regularization? Answer: L2 Regularization adds the squared values of the model’s weights to the Loss Function to prevent overfitting.
What is Grid Search? Answer: Grid Search is a hyperparameter tuning technique that exhaustively searches a predefined set of hyperparameter combinations to find the best model performance.
What is Feature Engineering? Answer: Feature Engineering is the process of selecting and transforming input features to improve model performance.
What is One-Hot Encoding? Answer: One-Hot Encoding is a technique used to convert categorical variables into binary vectors for machine learning algorithms.
What is a Confusion Matrix? Answer: A Confusion Matrix is a table used to evaluate the performance of a classification model by comparing predicted and true labels.
What is Precision and Recall? Answer: Precision is the ratio of true positives to all positive predictions, while Recall is the ratio of true positives to all actual positive samples.
What is F1 Score? Answer: F1 Score is the harmonic mean of Precision and Recall, providing a balance between the two metrics.
What is ROC Curve? Answer: ROC Curve (Receiver Operating Characteristic Curve) is a graphical representation of a classifier’s performance at different classification thresholds.
What is AUC-ROC? Answer: AUC-ROC (Area Under the ROC Curve) is a metric used to assess the overall performance of a classifier.
What is Gradient Boosting? Answer: Gradient Boosting is an ensemble learning technique that combines weak learners (usually decision trees) to create a strong predictive model.
What is XGBoost? Answer: XGBoost is an optimized implementation of Gradient Boosting, known for its high performance and accuracy.
Explain Q-Learning. Answer: Q-Learning is a model-free, off-policy reinforcement learning algorithm used to learn optimal action-value functions for Markov Decision Processes.
What is GAN (Generative Adversarial Network)? Answer: GAN is a type of generative model that consists of a generator and a discriminator, trained together in a competitive process.
What is Word Embedding? Answer: Word Embedding is a technique used to represent words as dense vectors, capturing semantic relationships for natural language processing tasks.
What is LSTM (Long Short-Term Memory)? Answer: LSTM is a type of recurrent neural network designed to handle long-term dependencies in sequence data.
What is Dropout in Neural Networks? Answer: Dropout is a regularization technique used in training neural networks to randomly ignore or “drop out” certain neurons during training to reduce overfitting.
What is Transfer Learning? Answer: Transfer Learning is a technique where a pre-trained model is used as a starting point for a related task, saving time and resources.
What is a Loss Function? Answer: A Loss Function measures the error between predicted and actual values, guiding the model’s parameter updates during training.
What is a Cost Function? Answer: A Cost Function is the average of the Loss Function over the entire dataset and is minimized during training.
What is Mean Squared Error (MSE)? Answer: MSE is a Loss Function commonly used for regression tasks, calculating the average squared difference between predicted and actual values.
What is Cross-Entropy Loss? Answer: Cross-Entropy Loss is used in classification tasks, measuring the dissimilarity between predicted probabilities and true labels.
What is L1 Regularization? Answer: L1 Regularization adds the absolute values of the model’s weights to the Loss Function to prevent overfitting and encourage sparsity.
What is L2 Regularization? Answer: L2 Regularization adds the squared values of the model’s weights to the Loss Function to prevent overfitting.
What is Grid Search? Answer: Grid Search is a hyperparameter tuning technique that exhaustively searches a predefined set of hyperparameter combinations to find the best model performance.
What is Feature Engineering? Answer: Feature Engineering is the process of selecting and transforming input features to improve model performance.
What is One-Hot Encoding? Answer: One-Hot Encoding is a technique used to convert categorical variables into binary vectors for machine learning algorithms.
What is a Confusion Matrix? Answer: A Confusion Matrix is a table used to evaluate the performance of a classification model by comparing predicted and true labels.
What is Precision and Recall? Answer: Precision is the ratio of true positives to all positive predictions, while Recall is the ratio of true positives to all actual positive samples.
What is F1 Score? Answer: F1 Score is the harmonic mean of Precision and Recall, providing a balance between the two metrics.
What is ROC Curve? Answer: ROC Curve (Receiver Operating Characteristic Curve) is a graphical representation of a classifier’s performance at different classification thresholds.
What is AUC-ROC? Answer: AUC-ROC (Area Under the ROC Curve) is a metric used to assess the overall performance of a classifier.
What is Gradient Boosting? Answer: Gradient Boosting is an ensemble learning technique that combines weak learners (usually decision trees) to create a strong predictive model.
What is XGBoost? Answer: XGBoost is an optimized implementation of Gradient Boosting, known for its high performance and accuracy.
Explain Q-Learning. Answer: Q-Learning is a model-free, off-policy reinforcement learning algorithm used to learn optimal action-value functions for Markov Decision Processes.
What is GAN (Generative Adversarial Network)? Answer: GAN is a type of generative model that consists of a generator and a discriminator, trained together in a competitive process.
What is Word Embedding? Answer: Word Embedding is a technique used to represent words as dense vectors, capturing semantic relationships for natural language processing tasks.
What is LSTM (Long Short-Term Memory)? Answer: LSTM is a type of recurrent neural network designed to handle long-term dependencies in sequence data.
What is Dropout in Neural Networks? Answer: Dropout is a regularization technique used in training neural networks to randomly ignore or “drop out” certain neurons during training to reduce overfitting.
What is Transfer Learning? Answer: Transfer Learning is a technique where a pre-trained model is used as a starting point for a related task, saving time and resources.
What is a Loss Function? Answer: A Loss Function measures the error between predicted and actual values, guiding the model’s parameter updates during training.
What is a Cost Function? Answer: A Cost Function is the average of the Loss Function over the entire dataset and is minimized during training.
What is Mean Squared Error (MSE)? Answer: MSE is a Loss Function commonly used for regression tasks, calculating the average squared difference between predicted and actual values.
What is Cross-Entropy Loss? Answer: Cross-Entropy Loss is used in classification tasks, measuring the dissimilarity between predicted probabilities and true labels.
What is L1 Regularization? Answer: L1 Regularization adds the absolute values of the model’s weights to the Loss Function to prevent overfitting and encourage sparsity.
What is L2 Regularization? Answer: L2 Regularization adds the squared values of the model’s weights to the Loss Function to prevent overfitting.
What is Grid Search? Answer: Grid Search is a hyperparameter tuning technique that exhaustively searches a predefined set of hyperparameter combinations to find the best model performance.
What is Feature Engineering? Answer: Feature Engineering is the process of selecting and transforming input features to improve model performance.
What is One-Hot Encoding? Answer: One-Hot Encoding is a technique used to convert categorical variables into binary vectors for machine learning algorithms.
What is a Confusion Matrix? Answer: A Confusion Matrix is a table used to evaluate the performance of a classification model by comparing predicted and true labels.