Top 60 Machine Learning Viva Questions and Answers

Machine learning is transforming education, research, and industry by enabling systems to learn from data and make intelligent decisions. From automation to predictive analytics, its applications span multiple domains, driving innovation and efficiency. This blog is designed to help students and professionals strengthen their foundational and practical understanding of ML concepts. 

In this comprehensive guide, you’ll find machine learning viva questions, including viva questions for machine learning lab and machine learning lab viva questions, organized by difficulty level—beginner, intermediate, and advanced. Each section is crafted to enhance your theoretical knowledge, improve problem-solving skills, and prepare you for academic or professional viva exams with confidence. 

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Machine Learning Viva Questions for Beginners 

This section focuses on foundational topics essential for understanding the basics of machine learning. It covers core principles, key algorithms, and fundamental statistical concepts every learner should know before advancing to complex models. 

Question 1: What is Machine Learning? 

Answer Intent: 
To assess understanding of how machines can learn patterns from data and make predictions or decisions without explicit rule-based programming. 

How to Answer: 
Machine learning is a branch of artificial intelligence that enables systems to learn from historical data and improve automatically. Instead of being explicitly programmed, algorithms analyze data to recognize patterns and make decisions. For example, an ML model can classify images or predict sales trends by learning from past datasets. 

Question 2: What are the main types of Machine Learning? 

Answer Intent: 
To evaluate understanding of ML categorization and the differences between learning approaches. 

How to Answer: 
Machine learning is mainly categorized into supervised learning, unsupervised learning, and reinforcement learning. In supervised learning, models are trained on labeled data. Unsupervised learning uses unlabeled data to find hidden structures or clusters. Reinforcement learning allows an agent to learn through trial and error by receiving rewards or penalties. 

Also Read: Supervised vs Unsupervised Learning: Key Differences 

Question 3: What is the difference between supervised and unsupervised learning? 

Answer Intent: 
To check clarity on how labeling of data influences the learning process in machine learning models. 

How to Answer: 
In supervised learning, the algorithm is trained on labeled datasets with known outputs—for instance, predicting house prices. In unsupervised learning, the data lacks predefined labels, and the model tries to find hidden patterns or clusters, like customer segmentation. The key distinction lies in the presence or absence of labeled data. 

Question 4: What is overfitting in Machine Learning? 

Answer Intent: 
To test understanding of model generalization and its behavior on unseen data. 

How to Answer: 
Overfitting occurs when a model learns not only the data patterns but also the noise in the training dataset. As a result, it performs exceptionally well on training data but poorly on test data. Techniques like cross-validation, regularization, or pruning can be used to minimize overfitting and improve model generalization. 

Question 5: What is underfitting? 

Answer Intent: 
To check knowledge about insufficient learning in ML models and causes behind poor model performance. 

How to Answer: 
Underfitting happens when a model is too simple to capture the underlying patterns in data. It leads to high bias and poor accuracy on both training and test sets. Using more complex algorithms, feature engineering, or longer training can help mitigate underfitting. 

Question 6: What are features in a dataset? 

Answer Intent: 
To evaluate understanding of the core components of a dataset used for training ML models. 

How to Answer: 
Features are the measurable properties or independent variables of a dataset that are used to predict outcomes. For example, in predicting house prices, features could include square footage, location, and number of rooms. Good feature selection improves model accuracy and reduces computation time. 

Question 7: What is a label in Machine Learning? 

Answer Intent: 
To test comprehension of target variables in supervised learning. 

How to Answer: 
A label is the dependent variable or output the model is trying to predict. In a classification task like spam detection, “spam” or “not spam” are labels. In regression problems, labels are continuous values such as price or temperature. 

Question 8: What is the difference between classification and regression? 

Answer Intent: 
To confirm understanding of output types and algorithmic differences. 

How to Answer: 
Classification predicts discrete categorical outcomes such as “yes/no” or “spam/not spam.” Regression predicts continuous numerical values like stock prices or rainfall. Algorithms like Logistic Regression and Decision Trees handle classification, whereas Linear Regression or SVR handle regression. 

Also Read: Regression Vs Classification in Machine Learning: Difference Between Regression and Classification 

Question 9: What is the purpose of data preprocessing? 

Answer Intent: 
To check awareness of data preparation steps critical for accurate model performance. 

How to Answer: 
Data preprocessing involves cleaning, transforming, and organizing raw data to make it suitable for analysis. It includes handling missing values, normalizing data, encoding categorical variables, and removing outliers. Proper preprocessing ensures the ML model learns from quality data and performs efficiently. 

Question 10: What is a confusion matrix? 

Answer Intent: 
To evaluate knowledge of model evaluation metrics used in classification tasks. 

How to Answer: 
A confusion matrix is a table that visualizes the performance of a classification model by showing true positives, true negatives, false positives, and false negatives. It helps assess accuracy, precision, recall, and F1-score to understand model reliability. 

Question 11: What is bias and variance in Machine Learning? 

Answer Intent: 
To test conceptual clarity about the trade-off in model performance. 

How to Answer: 
Bias represents error due to overly simplistic models, while variance indicates sensitivity to small fluctuations in training data. High bias leads to underfitting, and high variance results in overfitting. The goal is to achieve an optimal balance known as the bias-variance trade-off for better model generalization. 

Question 12: What is cross-validation? 

Answer Intent: 
To verify understanding of model evaluation techniques. 

How to Answer: 
Cross-validation divides data into subsets (folds) to train and test the model multiple times. The most common method, k-fold cross-validation, helps ensure that model evaluation is not biased by specific data splits and improves reliability of performance metrics. 

Question 13: What are hyperparameters? 

Answer Intent: 
To assess familiarity with configuration parameters in machine learning models. 

How to Answer: 
Hyperparameters are external parameters set before training that influence how an algorithm learns. Examples include learning rate, number of trees in a Random Forest, or number of clusters in K-Means. Hyperparameter tuning helps improve model performance and stability. 

Question 14: What is feature scaling? 

Answer Intent: 
To test understanding of normalization and standardization in data preprocessing. 

How to Answer: 
Feature scaling ensures all features contribute equally to model training by bringing them to a similar scale. Techniques like Min-Max normalization and Z-score standardization are commonly used to prevent bias toward variables with larger numerical ranges. 

Question 15: What is the difference between batch learning and online learning? 

Answer Intent: 
To examine understanding of model training strategies. 

How to Answer: 
Batch learning trains models on the entire dataset at once, suitable for static data. Online learning updates models incrementally as new data arrives, ideal for streaming or real-time applications. The choice depends on data availability and system constraints. 

Question 16: What is gradient descent? 

Answer Intent: 
To test grasp of optimization algorithms used in training ML models. 

How to Answer: 
Gradient descent minimizes the cost function by iteratively adjusting model parameters in the direction of steepest descent. It helps find optimal weights that reduce prediction error. Variants like stochastic and mini-batch gradient descent improve convergence speed and computational efficiency. 

Question 17: What is the role of loss functions in Machine Learning? 

Answer Intent: 
To verify knowledge of how model performance is quantified. 

How to Answer: 
Loss functions measure the difference between predicted and actual values. Examples include Mean Squared Error for regression and Cross-Entropy Loss for classification. The goal of training is to minimize this loss, indicating better model performance. 

Question 18: What is one-hot encoding? 

Answer Intent: 
To check understanding of categorical data transformation. 

How to Answer: 
One-hot encoding converts categorical variables into binary vectors so that ML algorithms can process them. For example, colors {Red, Green, Blue} become [1,0,0], [0,1,0], [0,0,1]. This prevents models from assuming ordinal relationships between categories. 

Question 19: What is the difference between training and testing data? 

Answer Intent: 
To evaluate comprehension of dataset splitting for model validation. 

How to Answer: 
Training data is used to build and adjust the model, while testing data evaluates its performance on unseen samples. This separation helps ensure the model generalizes well and doesn’t memorize patterns from training data. 

Question 20: What are some common algorithms used in Machine Learning? 

Answer Intent: 
To check awareness of widely used algorithms and their applications. 

How to Answer: 
Common algorithms include Linear Regression, Logistic Regression, Decision Trees, K-Nearest Neighbors, Naive Bayes, Support Vector Machines, and K-Means Clustering. Each serves different purposes—regression, classification, or clustering—depending on the nature of the dataset.

Intermediate-Level Machine Learning Viva Questions 

This section focuses on concepts and techniques beyond the basics, targeting learners with some practical experience in machine learning. It covers algorithmic understanding, data preprocessing, model evaluation, and ensemble methods. These intermediate-level machine learning viva questions help students strengthen coding skills, interpret results accurately, and prepare for more complex lab exercises and real-world problem-solving scenarios. 

Question 1: What is the difference between training, validation, and testing datasets? 

Answer Intent: 
To evaluate understanding of dataset partitioning for model training and performance evaluation. 

How to Answer: 
The training dataset is used to teach the model, the validation dataset tunes hyperparameters and prevents overfitting, and the testing dataset measures final model performance. Separating these ensures unbiased evaluation and better generalization on unseen data. 

Question 2: What is regularization, and why is it important? 

Answer Intent: 
To assess knowledge of techniques that prevent overfitting in machine learning models. 

How to Answer: 
Regularization adds a penalty term to the loss function to discourage overly complex models. Common methods include L1 (Lasso) and L2 (Ridge) regularization. It helps improve generalization by limiting model weights, reducing variance without significantly increasing bias. 

Question 3: Explain the concept of feature selection. 

Answer Intent: 
To test understanding of optimizing dataset quality by selecting relevant features. 

How to Answer: 
Feature selection identifies the most impactful variables that contribute to model accuracy. It reduces dimensionality, eliminates redundancy, and improves computation efficiency. Techniques include correlation analysis, Recursive Feature Elimination (RFE), and mutual information-based selection. 

Question 4: What is the difference between Bagging and Boosting? 

Answer Intent: 
To verify conceptual clarity about ensemble learning methods. 

How to Answer: 
Bagging (Bootstrap Aggregating) trains multiple models in parallel on random data subsets to reduce variance, as seen in Random Forests. Boosting trains models sequentially, where each new model focuses on errors from the previous one (e.g., AdaBoost, XGBoost). Bagging improves stability, while Boosting enhances accuracy. 

Question 5: What is the K-Nearest Neighbors (KNN) algorithm? 

Answer Intent: 
To assess understanding of a non-parametric classification algorithm. 

How to Answer: 
KNN classifies a new data point based on the majority label among its k closest neighbors using distance metrics like Euclidean or Manhattan distance. It’s simple and effective for small datasets but computationally expensive for large-scale data. 

Question 6: Explain the working of a Decision Tree. 

Answer Intent: 
To test comprehension of tree-based learning models. 

How to Answer: 
A Decision Tree splits data recursively based on feature values that maximize information gain or minimize Gini impurity. Internal nodes represent features, branches represent conditions, and leaf nodes represent outcomes. It’s intuitive but prone to overfitting without pruning. 

Question 7: What is Random Forest, and how does it improve over a single Decision Tree? 

Answer Intent: 
To evaluate understanding of ensemble-based generalization improvement. 

How to Answer: 
Random Forest combines multiple Decision Trees trained on random feature subsets and data samples. The final output is determined by averaging (regression) or majority voting (classification). It reduces overfitting, increases robustness, and performs well on varied datasets. 

Question 8: What is Principal Component Analysis (PCA)? 

Answer Intent: 
To check understanding of dimensionality reduction techniques. 

How to Answer: 
PCA transforms high-dimensional data into a smaller set of uncorrelated components while retaining most variance. It projects data onto new axes called principal components, improving visualization and reducing computational complexity while minimizing information loss. 

Question 9: Explain the concept of Gradient Boosting. 

Answer Intent: 
To test understanding of advanced ensemble techniques. 

How to Answer: 
Gradient Boosting builds models sequentially, where each model corrects the residuals of the previous one by minimizing a differentiable loss function. It uses decision trees as weak learners and improves predictive accuracy significantly. Examples include XGBoost and LightGBM. 

Question 10: What is a Support Vector Machine (SVM)? 

Answer Intent: 
To assess knowledge of supervised learning algorithms used for classification. 

How to Answer: 
SVM finds an optimal hyperplane that separates data points of different classes with maximum margin. It can handle non-linear data using kernel functions like polynomial or RBF kernels. SVMs are powerful for high-dimensional spaces but computationally intensive. 

Question 11: What is the difference between parametric and non-parametric models? 

Answer Intent: 
To evaluate conceptual understanding of model assumptions and flexibility. 

How to Answer: 
Parametric models assume a fixed functional form (e.g., Linear Regression), whereas non-parametric models like KNN or Decision Trees make fewer assumptions about data distribution. Parametric models are faster but less flexible; non-parametric models adapt better to complex data patterns. 

Question 12: Explain the role of the ROC curve and AUC score. 

Answer Intent: 
To verify understanding of model performance evaluation metrics. 

How to Answer: 
The ROC curve plots the True Positive Rate against the False Positive Rate across thresholds, showing a model’s ability to distinguish between classes. The AUC score quantifies the area under the ROC curve — the higher the AUC, the better the model’s classification capability. 

Question 13: What is a confusion matrix imbalance problem? 

Answer Intent: 
To test awareness of imbalanced dataset challenges in classification. 

How to Answer: 
When one class dominates the dataset, accuracy may become misleading. A confusion matrix for imbalanced data shows poor recall for minority classes. Metrics like precision, recall, F1-score, or techniques like SMOTE (oversampling) help address imbalance. 

Question 14: What is feature engineering, and why is it important? 

Answer Intent: 
To assess understanding of transforming raw data into meaningful features. 

How to Answer: 
Feature engineering involves creating, modifying, or combining features to improve model accuracy. It leverages domain knowledge to derive new variables. For instance, creating interaction terms or encoding dates into cyclical features enhances model interpretability and prediction quality. 

Question 15: What is normalization, and how is it different from standardization? 

Answer Intent: 
To test grasp of scaling methods in preprocessing. 

How to Answer: 
Normalization scales data between 0 and 1, while standardization centers data around a mean of 0 and standard deviation of 1. Normalization is preferred for bounded data; standardization works better for algorithms assuming Gaussian distributions. 

Question 16: What are Naive Bayes classifiers? 

Answer Intent: 
To check understanding of probabilistic classification models. 

How to Answer: 
Naive Bayes is based on Bayes’ theorem, assuming feature independence. It calculates the probability of each class given the input features. It’s fast, works well with text classification and spam detection, and performs efficiently even with small datasets. 

Question 17: What is the difference between stochastic and batch gradient descent? 

Answer Intent: 
To assess knowledge of optimization variations. 

How to Answer: 
Batch gradient descent uses the entire dataset per iteration, ensuring stability but slower convergence. Stochastic gradient descent (SGD) updates weights for each sample, leading to faster but noisier updates. Mini-batch combines both for efficiency and stability. 

Question 18: What is an activation function in neural networks? 

Answer Intent: 
To check foundational understanding of deep learning components. 

How to Answer: 
Activation functions introduce non-linearity into neural networks, enabling them to learn complex patterns. Common types include ReLU, Sigmoid, and Tanh. Without activation functions, networks behave like simple linear models and can’t model intricate data relationships. 

Question 19: Explain the term “Model Evaluation Metrics.” 

Answer Intent: 
To test knowledge of various metrics used to assess model performance. 

How to Answer: 
Model evaluation metrics measure accuracy, error, or predictive power. For regression: RMSE, MAE, R² score; for classification: accuracy, precision, recall, F1-score. Selection depends on the problem type and business objective. 

Question 20: What are some common real-world applications of Machine Learning? 

Answer Intent: 
To assess practical understanding of ML implementations. 

How to Answer: 
Machine learning powers applications like fraud detection, recommendation systems, medical image diagnosis, predictive maintenance, and autonomous vehicles. These demonstrate how trained models automate decision-making and extract insights from large datasets efficiently.

Advanced-Level Machine Learning Viva Questions 

This section is designed for learners with strong foundational and intermediate knowledge, focusing on deep learning, optimization, deployment, and emerging ML trends. The advanced-level machine learning viva questions help students tackle complex algorithms, understand model scalability, and demonstrate expertise in real-world applications, research, and cutting-edge ML technologies. 

Question 1: What is Deep Learning, and how does it differ from traditional Machine Learning? 

Answer Intent: 
To assess understanding of hierarchical representation learning and differences between ML and DL approaches. 

How to Answer: 
Deep Learning is a subset of machine learning that uses multi-layered neural networks to automatically extract high-level features from raw data. Unlike traditional ML, which relies heavily on manual feature engineering, deep learning can handle unstructured data like images, text, and audio, enabling tasks such as image recognition and natural language processing. 

Question 2: Explain Convolutional Neural Networks (CNNs). 

Answer Intent: 
To evaluate knowledge of specialized architectures for visual data. 

How to Answer: 
CNNs are deep learning models designed for image and video analysis. They use convolutional layers to detect spatial hierarchies in data, pooling layers for dimensionality reduction, and fully connected layers for classification. Key applications include object detection, image segmentation, and facial recognition. 

Question 3: What are Recurrent Neural Networks (RNNs)? 

Answer Intent: 
To check understanding of sequential data modeling. 

How to Answer: 
RNNs are neural networks designed for sequential data, where current outputs depend on previous computations. They use internal memory to retain context, making them suitable for time-series prediction, language modeling, and speech recognition. Variants like LSTM and GRU address long-term dependency issues. 

Question 4: What is overfitting in deep learning, and how can it be prevented? 

Answer Intent: 
To assess understanding of model generalization at an advanced level. 

How to Answer: 
Overfitting occurs when a model learns training data patterns, including noise, reducing performance on unseen data. Techniques to prevent overfitting include dropout, early stopping, regularization, data augmentation, and increasing training dataset size. Proper evaluation on validation data ensures generalization. 

Question 5: Explain the role of activation functions in deep neural networks. 

Answer Intent: 
To test knowledge of non-linear transformations in complex architectures. 

How to Answer: 
Activation functions introduce non-linearity, allowing networks to learn complex patterns. Common types include ReLU, Sigmoid, and Tanh. Choosing the correct activation function impacts convergence speed, gradient propagation, and overall model accuracy. 

Question 6: What is a loss function in deep learning? 

Answer Intent: 
To evaluate understanding of performance measurement in neural networks. 

How to Answer: 
A loss function quantifies the difference between predicted and actual outputs. Examples include Cross-Entropy Loss for classification and Mean Squared Error for regression. Minimizing loss through optimization techniques like gradient descent improves model predictions. 

Question 7: What is gradient descent, and what are its variants? 

Answer Intent: 
To test understanding of optimization algorithms in deep learning. 

How to Answer: 
Gradient descent updates model parameters iteratively to minimize the loss function. Variants include Batch Gradient Descent (entire dataset), Stochastic Gradient Descent (single sample), and Mini-Batch Gradient Descent (subset of data). Optimizers like Adam and RMSProp further improve convergence efficiency. 

Question 8: Explain the concept of learning rate and its significance. 

Answer Intent: 
To assess knowledge of a critical hyperparameter in training. 

How to Answer: 
Learning rate determines the size of parameter updates during optimization. A high learning rate may overshoot minima, while a low learning rate slows convergence. Techniques like learning rate scheduling or adaptive optimizers help achieve optimal training performance. 

Question 9: What are vanishing and exploding gradients? 

Answer Intent: 
To test understanding of common deep learning training issues. 

How to Answer: 
Vanishing gradients occur when gradients shrink during backpropagation, slowing learning in early layers. Exploding gradients happen when gradients grow excessively, causing instability. Solutions include gradient clipping, careful weight initialization, and using architectures like LSTM for sequential data. 

Question 10: What is a dropout layer, and why is it used? 

Answer Intent: 
To evaluate knowledge of regularization techniques in deep networks. 

How to Answer: 
Dropout randomly deactivates neurons during training to prevent over-reliance on specific features, reducing overfitting and improving generalization. It’s simple, effective, and commonly used in fully connected layers of deep networks. 

Question 11: Explain the difference between training, validation, and test sets in deep learning. 

Answer Intent: 
To assess understanding of proper model evaluation practices. 

How to Answer: 
The training set is used for learning, the validation set tunes hyperparameters and monitors overfitting, and the test set evaluates final model performance. This separation ensures robust assessment and helps in selecting the best model architecture. 

Question 12: What is transfer learning? 

Answer Intent: 
To test knowledge of advanced model reuse techniques. 

How to Answer: 
Transfer learning leverages pre-trained models on large datasets to solve related tasks with limited data. For instance, using a pre-trained ResNet on ImageNet for a custom image classification problem reduces training time and improves performance, especially when labeled data is scarce. 

Question 13: What is reinforcement learning? 

Answer Intent: 
To assess understanding of reward-based learning systems. 

How to Answer: 
Reinforcement learning trains agents to make sequences of decisions by maximizing cumulative rewards. Applications include robotics, game AI, and autonomous vehicles. Core concepts include states, actions, rewards, and policies that guide optimal behavior. 

Question 14: Explain hyperparameter tuning in deep learning. 

Answer Intent: 
To evaluate knowledge of optimizing model performance through parameter selection. 

How to Answer: 
Hyperparameter tuning involves selecting optimal values for learning rate, batch size, number of layers, and activation functions. Techniques include grid search, random search, and Bayesian optimization. Proper tuning significantly enhances accuracy and generalization. 

Question 15: What is a generative model, and give an example? 

Answer Intent: 
To test awareness of advanced ML models that create data. 

How to Answer: 
Generative models learn data distribution to generate new samples similar to training data. Examples include Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs). They are used in image synthesis, data augmentation, and content creation. 

Question 16: Explain model deployment in machine learning. 

Answer Intent: 
To assess understanding of operationalizing ML models in real-world systems. 

How to Answer: 
Model deployment involves integrating trained models into production environments where they serve predictions. This includes converting models to APIs, ensuring scalability, monitoring performance, and updating models as new data arrives. Tools include Flask, FastAPI, Docker, and cloud platforms like AWS and Azure. 

Question 17: What is bias and fairness in Machine Learning? 

Answer Intent: 
To evaluate knowledge of ethical considerations in advanced ML systems. 

How to Answer: 
Bias occurs when a model systematically favors certain groups or outcomes. Fairness ensures predictions do not discriminate against protected attributes. Techniques include data balancing, fairness-aware algorithms, and auditing model decisions for ethical compliance. 

Question 18: Explain attention mechanisms in neural networks. 

Answer Intent: 
To assess understanding of advanced architectures in NLP and sequence modeling. 

How to Answer: 
Attention mechanisms allow models to focus on relevant parts of input sequences when making predictions. They improve performance in tasks like machine translation and text summarization by weighting important tokens more heavily. Transformers heavily rely on self-attention layers. 

Question 19: What is the difference between CNN and RNN? 

Answer Intent: 
To evaluate conceptual clarity between architectures for different data types. 

How to Answer: 
CNNs are optimized for spatial data like images, using convolution and pooling layers to extract features. RNNs are designed for sequential data, retaining temporal dependencies. Choosing the architecture depends on the input type—images for CNN, time-series or text for RNN. 

Question 20: What are some emerging trends in Machine Learning? 

Answer Intent: 
To assess awareness of cutting-edge research and industry applications. 

How to Answer: 
Emerging trends include transformers, generative AI, federated learning, self-supervised learning, and reinforcement learning in autonomous systems. These technologies enhance model efficiency, scalability, and application scope across domains like healthcare, finance, and AI-driven creativity.

Tips to Prepare for a Machine Learning Viva 

Preparing effectively for a machine learning viva requires balancing theoretical knowledge with practical skills. Focusing on both concepts and hands-on experience ensures you can confidently tackle questions in theory and demonstrate proficiency in lab exercises. Here are some essential tips to guide your preparation: 

1. Strengthen Theoretical Understanding – Revise core concepts, algorithms, and key differences. 
2. Practice Lab Exercises – Implement models, preprocess data, and evaluate results hands-on. 
3. Work on Mini Projects – Apply ML techniques to small projects to demonstrate practical skills. 
4. Revise Common Viva Questions – Practice answering typical questions to improve recall and confidence. 
5. Document Your Work – Keep clear notes and code explanations for easy reference during the viva. 

Common Mistakes to Avoid During a Machine Learning Viva 

Many students make avoidable errors during a machine learning viva. Being aware of these can help you perform confidently and accurately. 

• Over-reliance on memorization – Avoid rote learning; understand concepts instead of only recalling definitions. 
• Lack of coding practice – Failing to implement models can reduce confidence in lab-related questions. 
• Poor explanation of model outputs – Always interpret results clearly; vague answers confuse examiners. 
• Tips to articulate answers confidently: Speak slowly, structure answers logically, provide examples, and clarify assumptions when necessary. 

How to Excel in a Machine Learning Lab Viva 

Success in a lab-focused viva requires hands-on preparation and problem-solving skills. Focusing on coding proficiency and dataset handling ensures you can tackle practical questions efficiently. 

• Prepare for coding rounds – Practice implementing algorithms from scratch and using libraries like Scikit-learn or TensorFlow. 
• Handling dataset-related questions – Understand preprocessing, feature selection, and exploratory data analysis thoroughly. 
• Understand code outputs and errors – Be able to explain results, debug issues, and justify model performance. 
• Always review viva questions for machine learning lab to align practice with likely exam scenarios. 

Conclusion 

Structured preparation is key to performing well in a machine learning viva. Balancing theoretical understanding with hands-on practice ensures learners can answer questions confidently and accurately. Reviewing machine learning viva questions, including lab-focused ones, strengthens conceptual clarity and problem-solving skills. 

This comprehensive list of 60 questions, from beginner to advanced, helps learners excel in academic and professional evaluations. Regular practice on real-world datasets and mini projects enhances practical knowledge, improves coding skills, and builds confidence. Consistent preparation ensures success in viva exams and equips learners for real-life machine learning challenges.

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