Freshers Interview Q&A-AIML
1. What is Artificial Intelligence (AI) and Machine Learning (ML)?
AI is the broader field of creating intelligent agents capable of mimicking human-like cognitive functions.
ML is a subset of AI that focuses on developing algorithms and models that enable computers to learn from data and make predictions or decisions.
2.Explain the difference between supervised, unsupervised, and reinforcement learning.
Supervised Learning: Involves training a model on labeled data, where the model learns to make predictions based on input-output pairs.
Unsupervised Learning: Involves discovering patterns or relationships in unlabeled data, often used for clustering and dimensionality reduction.
Reinforcement Learning: Involves training agents to make a sequence of decisions to maximize a reward signal in an environment.
3. What is overfitting in machine learning, and how can it be prevented?
Overfitting occurs when a model learns the training data too well but fails to generalize to unseen data. To prevent it, techniques such as cross-validation, regularization, and having more diverse data can be used.
4. What is bias-variance trade-off in machine learning?
The bias-variance trade-off is a fundamental concept in ML. It refers to the balance between underfitting (high bias, low variance) and overfitting (low bias, high variance). Finding the right trade-off is crucial for model performance.
5. What is a decision tree, and how does it work?
A decision tree is a supervised learning algorithm used for classification and regression tasks. It works by recursively splitting the data into subsets based on the most significant feature to make decisions.
6. Explain the concept of feature engineering.
Feature engineering is the process of selecting, transforming, or creating new features from the raw data to improve the performance of machine learning models. It involves domain knowledge and creativity.
7. What is the curse of dimensionality, and how does it affect machine learning algorithms?
The curse of dimensionality refers to the challenges and problems that arise when dealing with high-dimensional data. It can lead to increased computational complexity, overfitting, and difficulties in visualization and interpretation.
8.What is cross-validation, and why is it important in machine learning?
Cross-validation is a technique for assessing a model’s performance by splitting the data into multiple subsets and repeatedly training and testing the model on different partitions. It helps evaluate a model’s generalization ability.
9. What is deep learning, and how does it differ from traditional machine learning?
Deep learning is a subfield of machine learning that focuses on neural networks with multiple layers (deep neural networks). It excels at tasks involving unstructured data, such as images, audio, and text, and often requires large amounts of labeled data.
10.Explain the concept of gradient descent in the context of optimization in machine learning.
Gradient descent is an optimization algorithm used to find the minimum of a cost function by iteratively adjusting model parameters in the direction of the steepest decrease in the cost function’s gradient.
11.What is a neural network activation function, and why is it important?
An activation function introduces non-linearity to a neural network by determining the output of a neuron. It is essential because it allows neural networks to learn complex, non-linear relationships in data.
12. What is the difference between precision and recall in binary classification?
Precision is the ratio of true positive predictions to the total positive predictions made by a model. It measures the accuracy of positive predictions.
Recall is the ratio of true positive predictions to the total actual positive instances. It measures a model’s ability to find all positive instances.
13. What are hyperparameters in machine learning, and how are they different from model parameters?
Hyperparameters are settings or configurations that are set before training a model. They control aspects like model complexity and training behavior. Model parameters, on the other hand, are learned from data during training.
14. What is transfer learning in deep learning?
Transfer learning is a technique where a pre-trained neural network, trained on a large dataset for a specific task, is adapted or fine-tuned for a different but related task. It leverages the knowledge gained from the original task to improve performance on the new task.
15.How do you evaluate the performance of a classification model?
Classification model performance can be evaluated using metrics such as accuracy, precision, recall, F1-score, and the ROC curve. The choice of metrics depends on the problem and the importance of false positives and false negatives.