{"id":25488600,"date":"2021-10-09T15:00:30","date_gmt":"2021-10-09T09:30:30","guid":{"rendered":"https:\/\/entri.app\/blog\/?p=25488600"},"modified":"2025-09-10T20:24:46","modified_gmt":"2025-09-10T14:54:46","slug":"machine-learning-interview-questions-and-answers","status":"publish","type":"post","link":"https:\/\/entri.app\/blog\/machine-learning-interview-questions-and-answers\/","title":{"rendered":"100 Machine Learning Interview Questions and Answers"},"content":{"rendered":"<div id=\"ez-toc-container\" class=\"ez-toc-v2_0_79_2 counter-hierarchy ez-toc-counter ez-toc-custom ez-toc-container-direction\">\n<p class=\"ez-toc-title\" style=\"cursor:inherit\">Table of Contents<\/p>\n<label for=\"ez-toc-cssicon-toggle-item-69e974df88b5b\" class=\"ez-toc-cssicon-toggle-label\"><span class=\"\"><span class=\"eztoc-hide\" style=\"display:none;\">Toggle<\/span><span class=\"ez-toc-icon-toggle-span\"><svg style=\"fill: #999;color:#999\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" class=\"list-377408\" width=\"20px\" height=\"20px\" viewBox=\"0 0 24 24\" fill=\"none\"><path d=\"M6 6H4v2h2V6zm14 0H8v2h12V6zM4 11h2v2H4v-2zm16 0H8v2h12v-2zM4 16h2v2H4v-2zm16 0H8v2h12v-2z\" fill=\"currentColor\"><\/path><\/svg><svg style=\"fill: #999;color:#999\" class=\"arrow-unsorted-368013\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"10px\" height=\"10px\" viewBox=\"0 0 24 24\" version=\"1.2\" baseProfile=\"tiny\"><path d=\"M18.2 9.3l-6.2-6.3-6.2 6.3c-.2.2-.3.4-.3.7s.1.5.3.7c.2.2.4.3.7.3h11c.3 0 .5-.1.7-.3.2-.2.3-.5.3-.7s-.1-.5-.3-.7zM5.8 14.7l6.2 6.3 6.2-6.3c.2-.2.3-.5.3-.7s-.1-.5-.3-.7c-.2-.2-.4-.3-.7-.3h-11c-.3 0-.5.1-.7.3-.2.2-.3.5-.3.7s.1.5.3.7z\"\/><\/svg><\/span><\/span><\/label><input type=\"checkbox\"  id=\"ez-toc-cssicon-toggle-item-69e974df88b5b\"  aria-label=\"Toggle\" \/><nav><ul class='ez-toc-list ez-toc-list-level-1 ' ><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-1\" href=\"https:\/\/entri.app\/blog\/machine-learning-interview-questions-and-answers\/#Introduction\" >Introduction<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-2\" href=\"https:\/\/entri.app\/blog\/machine-learning-interview-questions-and-answers\/#Interview_Preparation_Tips_for_Machine_Learning_Roles\" >Interview Preparation Tips for Machine Learning Roles<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-3\" href=\"https:\/\/entri.app\/blog\/machine-learning-interview-questions-and-answers\/#100_Machine_Learning_Interview_Questions\" >100 Machine Learning Interview Questions<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-4\" href=\"https:\/\/entri.app\/blog\/machine-learning-interview-questions-and-answers\/#Conclusion\" >Conclusion<\/a><\/li><\/ul><\/nav><\/div>\n<h2><span class=\"ez-toc-section\" id=\"Introduction\"><\/span><strong>Introduction<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p class=\"my-2 [&amp;+p]:mt-4 [&amp;_strong:has(+br)]:inline-block [&amp;_strong:has(+br)]:pb-2\">Preparing for a machine learning interview can feel overwhelming with the vast amount of knowledge involved. To help you navigate this journey confidently, we have compiled 100 of the most commonly asked interview questions along with concise, clear answers. Whether you\u2019re a fresher or a seasoned professional, this guide covers theory, algorithms, practical concepts, and tips that interviewers frequently probe.<\/p>\n<p><strong><div class=\"lead-gen-block\"><a href=\"https:\/\/entri.app\/blog\/wp-content\/uploads\/2021\/10\/100-Machine-Learning-Interview-Questions-and-Answers-1.pdf\" data-url=\"https:\/\/entri.app\/blog\/wp-content\/uploads\/2021\/10\/100-Machine-Learning-Interview-Questions-and-Answers-1.pdf\" class=\"lead-pdf-download\" data-id=\"25556853\"><\/strong><\/p>\n<p style=\"text-align: center;\"><button class=\"btn btn-default\">Free machine learning interview questions and answers<\/button><\/p>\n<p><strong><\/a><\/div><\/strong><\/p>\n<h2 class=\"mb-2 mt-4 font-display font-semimedium text-base first:mt-0\"><span class=\"ez-toc-section\" id=\"Interview_Preparation_Tips_for_Machine_Learning_Roles\"><\/span><strong>Interview Preparation Tips for Machine Learning Roles<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p class=\"my-2 [&amp;+p]:mt-4 [&amp;_strong:has(+br)]:inline-block [&amp;_strong:has(+br)]:pb-2\">Preparing for a machine learning interview goes beyond just knowing the algorithms or coding skills. It\u2019s about demonstrating your problem-solving mindset, practical experience, communication abilities, and passion for ongoing learning. Here are some essential tips to help you prepare effectively and confidently:<\/p>\n<h3 class=\"mb-2 mt-4 font-display font-semimedium text-base first:mt-0\">1.\u00a0<strong>Master the Fundamentals Thoroughly<\/strong><\/h3>\n<p class=\"my-2 [&amp;+p]:mt-4 [&amp;_strong:has(+br)]:inline-block [&amp;_strong:has(+br)]:pb-2\">Ensure that you have a strong grasp of core concepts like statistics, linear algebra, probability, and machine learning algorithms. Interviewers often test your understanding of how and why algorithms work, not just their syntax. Build a solid foundation with both theory and practical examples.<\/p>\n<h3 class=\"mb-2 mt-4 font-display font-semimedium text-base first:mt-0\">2.\u00a0<strong>Build Real-World Projects<\/strong><\/h3>\n<p class=\"my-2 [&amp;+p]:mt-4 [&amp;_strong:has(+br)]:inline-block [&amp;_strong:has(+br)]:pb-2\">Nothing speaks louder than experience. Create machine learning projects that solve realistic problems end to end\u2014from data collection and cleaning to model building, tuning, and evaluation. Publishing these projects on GitHub or personal blogs shows recruiters your hands-on skills and initiative.<\/p>\n<h3 class=\"mb-2 mt-4 font-display font-semimedium text-base first:mt-0\">3.\u00a0<strong>Practice Coding Regularly<\/strong><\/h3>\n<p class=\"my-2 [&amp;+p]:mt-4 [&amp;_strong:has(+br)]:inline-block [&amp;_strong:has(+br)]:pb-2\">Most interviews include coding rounds where you\u2019ll implement algorithms or solve data-related problems. Use platforms like LeetCode, HackerRank, or Kaggle to practice. Focus on writing clean, efficient, and well-documented code. Remember, communication while coding during interviews matters as much as getting the right answer.<\/p>\n<h3 class=\"mb-2 mt-4 font-display font-semimedium text-base first:mt-0\">4.\u00a0<strong>Understand Model Evaluation and Metrics<\/strong><\/h3>\n<p class=\"my-2 [&amp;+p]:mt-4 [&amp;_strong:has(+br)]:inline-block [&amp;_strong:has(+br)]:pb-2\">Be ready to explain how to assess model quality. Know metrics like accuracy, precision, recall, F1 score, ROC-AUC, and confusion matrix inside out. Interviewers may ask scenario-based questions on choosing appropriate metrics for problems.<\/p>\n<h3 class=\"mb-2 mt-4 font-display font-semimedium text-base first:mt-0\">5.\u00a0<strong>Study System Design and Deployment Basics<\/strong><\/h3>\n<p class=\"my-2 [&amp;+p]:mt-4 [&amp;_strong:has(+br)]:inline-block [&amp;_strong:has(+br)]:pb-2\">For roles involving deployment, understanding how ML models fit into production pipelines is key. Practice explaining concepts like data pipelines, model versioning, cloud infrastructure (AWS, GCP), containerization (Docker), and scaling.<\/p>\n<h3 class=\"mb-2 mt-4 font-display font-semimedium text-base first:mt-0\">6.\u00a0<strong>Improve Your Communication Skills<\/strong><\/h3>\n<p class=\"my-2 [&amp;+p]:mt-4 [&amp;_strong:has(+br)]:inline-block [&amp;_strong:has(+br)]:pb-2\">Being able to clearly explain complex technical ideas in simple terms is a prized skill. Practice telling stories about your projects, challenges faced, and insights discovered. Use visualization tools or simple analogies to make your points clear and memorable.<\/p>\n<h3 class=\"mb-2 mt-4 font-display font-semimedium text-base first:mt-0\">7.\u00a0<strong>Mock Interviews and Peer Reviews<\/strong><\/h3>\n<p class=\"my-2 [&amp;+p]:mt-4 [&amp;_strong:has(+br)]:inline-block [&amp;_strong:has(+br)]:pb-2\">Simulate the interview environment by practicing with friends, mentors, or through platforms offering mock interviews. This helps reduce anxiety and improves your ability to think and communicate under pressure. Feedback from peers can uncover blind spots and refine your answers.<\/p>\n<h3 class=\"mb-2 mt-4 font-display font-semimedium text-base first:mt-0\">8.\u00a0<strong>Stay Updated and Curious<\/strong><\/h3>\n<p class=\"my-2 [&amp;+p]:mt-4 [&amp;_strong:has(+br)]:inline-block [&amp;_strong:has(+br)]:pb-2\">Machine learning is a fast-moving field where new techniques and tools emerge regularly. Follow blogs, research papers, podcasts, and online courses to keep up. Showing awareness of the latest trends during interviews reflects your enthusiasm and commitment.<\/p>\n<h3 class=\"mb-2 mt-4 font-display font-semimedium text-base first:mt-0\">9.\u00a0<strong>Prepare for Behavioral Questions<\/strong><\/h3>\n<p class=\"my-2 [&amp;+p]:mt-4 [&amp;_strong:has(+br)]:inline-block [&amp;_strong:has(+br)]:pb-2\">Technical prowess is crucial, but companies also want to gauge your teamwork, adaptability, and problem-solving approach. Be ready with examples demonstrating how you handled tough situations, collaborated in teams, or learned from failures.<\/p>\n<h3 class=\"mb-2 mt-4 font-display font-semimedium text-base first:mt-0\">10.\u00a0<strong>Know the Company and Role<\/strong><\/h3>\n<p class=\"my-2 [&amp;+p]:mt-4 [&amp;_strong:has(+br)]:inline-block [&amp;_strong:has(+br)]:pb-2\">Research the organization\u2019s industry, products, and ML applications. Tailor your answers to highlight how your skills align with their challenges and goals. This shows genuine interest and helps you ask insightful questions.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"100_Machine_Learning_Interview_Questions\"><\/span><b>100 Machine Learning Interview Questions<\/b><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<div class=\"prose text-pretty dark:prose-invert inline leading-relaxed break-words min-w-0 [word-break:break-word] prose-strong:font-medium\">\n<p class=\"my-2 [&amp;+p]:mt-4 [&amp;_strong:has(+br)]:inline-block [&amp;_strong:has(+br)]:pb-2\">\n<p class=\"mb-2 mt-4 font-display font-semimedium text-base first:mt-0\">1. What is Machine Learning?<\/p>\n<p class=\"my-2 [&amp;+p]:mt-4 [&amp;_strong:has(+br)]:inline-block [&amp;_strong:has(+br)]:pb-2\">Machine Learning (ML) is a subset of artificial intelligence where machines learn patterns from data without being explicitly programmed.<\/p>\n<p class=\"mb-2 mt-4 font-display font-semimedium text-base first:mt-0\">2. Differentiate between Supervised, Unsupervised, and Semi-Supervised Learning.<\/p>\n<ul class=\"marker:text-quiet list-disc\">\n<li class=\"py-0 my-0 prose-p:pt-0 prose-p:mb-2 prose-p:my-0 [&amp;&gt;p]:pt-0 [&amp;&gt;p]:mb-2 [&amp;&gt;p]:my-0\">\n<p class=\"my-2 [&amp;+p]:mt-4 [&amp;_strong:has(+br)]:inline-block [&amp;_strong:has(+br)]:pb-2\">Supervised Learning uses labeled data for training.<\/p>\n<\/li>\n<li class=\"py-0 my-0 prose-p:pt-0 prose-p:mb-2 prose-p:my-0 [&amp;&gt;p]:pt-0 [&amp;&gt;p]:mb-2 [&amp;&gt;p]:my-0\">\n<p class=\"my-2 [&amp;+p]:mt-4 [&amp;_strong:has(+br)]:inline-block [&amp;_strong:has(+br)]:pb-2\">Unsupervised Learning uses unlabeled data to find patterns.<\/p>\n<\/li>\n<li class=\"py-0 my-0 prose-p:pt-0 prose-p:mb-2 prose-p:my-0 [&amp;&gt;p]:pt-0 [&amp;&gt;p]:mb-2 [&amp;&gt;p]:my-0\">\n<p class=\"my-2 [&amp;+p]:mt-4 [&amp;_strong:has(+br)]:inline-block [&amp;_strong:has(+br)]:pb-2\">Semi-Supervised Learning uses both labeled and unlabeled data.<\/p>\n<\/li>\n<\/ul>\n<p class=\"mb-2 mt-4 font-display font-semimedium text-base first:mt-0\">3. What is Reinforcement Learning?<\/p>\n<p class=\"my-2 [&amp;+p]:mt-4 [&amp;_strong:has(+br)]:inline-block [&amp;_strong:has(+br)]:pb-2\">Learning through trial and error, where an agent receives rewards or penalties to learn optimal behavior.<\/p>\n<p class=\"mb-2 mt-4 font-display font-semimedium text-base first:mt-0\">4. What are the different types of data used in Machine Learning?<\/p>\n<p class=\"my-2 [&amp;+p]:mt-4 [&amp;_strong:has(+br)]:inline-block [&amp;_strong:has(+br)]:pb-2\">Structured, unstructured, and semi-structured data.<\/p>\n<p class=\"mb-2 mt-4 font-display font-semimedium text-base first:mt-0\">5. Difference between Regression and Classification?<\/p>\n<p class=\"my-2 [&amp;+p]:mt-4 [&amp;_strong:has(+br)]:inline-block [&amp;_strong:has(+br)]:pb-2\">Regression predicts continuous outcomes, classification predicts discrete classes.<\/p>\n<p class=\"mb-2 mt-4 font-display font-semimedium text-base first:mt-0\">6. Define Features and Labels.<\/p>\n<p class=\"my-2 [&amp;+p]:mt-4 [&amp;_strong:has(+br)]:inline-block [&amp;_strong:has(+br)]:pb-2\">Features are input variables; labels are the output or target.<\/p>\n<p class=\"mb-2 mt-4 font-display font-semimedium text-base first:mt-0\">7. What is Scikit-learn?<\/p>\n<p class=\"my-2 [&amp;+p]:mt-4 [&amp;_strong:has(+br)]:inline-block [&amp;_strong:has(+br)]:pb-2\">A Python library for ML algorithms and data processing.<\/p>\n<p class=\"mb-2 mt-4 font-display font-semimedium text-base first:mt-0\">8. What are Training Set and Test Set?<\/p>\n<p class=\"my-2 [&amp;+p]:mt-4 [&amp;_strong:has(+br)]:inline-block [&amp;_strong:has(+br)]:pb-2\">Training set is for fitting the model; test set evaluates its performance.<\/p>\n<p class=\"mb-2 mt-4 font-display font-semimedium text-base first:mt-0\">9. List the stages of building a Machine Learning model.<\/p>\n<p class=\"my-2 [&amp;+p]:mt-4 [&amp;_strong:has(+br)]:inline-block [&amp;_strong:has(+br)]:pb-2\">Data collection \u2192 Preprocessing \u2192 Model selection \u2192 Training \u2192 Evaluation \u2192 Deployment.<\/p>\n<p class=\"mb-2 mt-4 font-display font-semimedium text-base first:mt-0\">10. What is a Confusion Matrix?<\/p>\n<p class=\"my-2 [&amp;+p]:mt-4 [&amp;_strong:has(+br)]:inline-block [&amp;_strong:has(+br)]:pb-2\">A table used to evaluate classification models showing TP, TN, FP, FN.<\/p>\n<p class=\"mb-2 mt-4 font-display font-semimedium text-base first:mt-0\">11. What are Type I and Type II errors?<\/p>\n<p class=\"my-2 [&amp;+p]:mt-4 [&amp;_strong:has(+br)]:inline-block [&amp;_strong:has(+br)]:pb-2\">Type I is false positive; Type II is false negative.<\/p>\n<p class=\"mb-2 mt-4 font-display font-semimedium text-base first:mt-0\">12. Define Precision, Recall, Accuracy, and F1 Score.<\/p>\n<p class=\"my-2 [&amp;+p]:mt-4 [&amp;_strong:has(+br)]:inline-block [&amp;_strong:has(+br)]:pb-2\">Precision is TP\/(TP+FP), recall is TP\/(TP+FN), accuracy is correct predictions\/total predictions, F1 score is harmonic mean of precision and recall.<\/p>\n<p class=\"mb-2 mt-4 font-display font-semimedium text-base first:mt-0\">13. What is the P-value?<\/p>\n<p class=\"my-2 [&amp;+p]:mt-4 [&amp;_strong:has(+br)]:inline-block [&amp;_strong:has(+br)]:pb-2\">Probability that observed results are due to chance under null hypothesis.<\/p>\n<p class=\"mb-2 mt-4 font-display font-semimedium text-base first:mt-0\">14. Explain ROC Curve.<\/p>\n<p class=\"my-2 [&amp;+p]:mt-4 [&amp;_strong:has(+br)]:inline-block [&amp;_strong:has(+br)]:pb-2\">Plot of true positive rate vs false positive rate at various thresholds.<\/p>\n<p class=\"mb-2 mt-4 font-display font-semimedium text-base first:mt-0\">15. How is KNN different from k-means clustering?<\/p>\n<p class=\"my-2 [&amp;+p]:mt-4 [&amp;_strong:has(+br)]:inline-block [&amp;_strong:has(+br)]:pb-2\">KNN is supervised classification; k-means is unsupervised clustering.<\/p>\n<p class=\"mb-2 mt-4 font-display font-semimedium text-base first:mt-0\">16. What does \u2018Naive\u2019 in Naive Bayes mean?<\/p>\n<p class=\"my-2 [&amp;+p]:mt-4 [&amp;_strong:has(+br)]:inline-block [&amp;_strong:has(+br)]:pb-2\">Assumes independent features.<\/p>\n<p class=\"mb-2 mt-4 font-display font-semimedium text-base first:mt-0\">17. What is Overfitting?<\/p>\n<p class=\"my-2 [&amp;+p]:mt-4 [&amp;_strong:has(+br)]:inline-block [&amp;_strong:has(+br)]:pb-2\">When the model performs well on training data but poorly on unseen data.<\/p>\n<p class=\"mb-2 mt-4 font-display font-semimedium text-base first:mt-0\">18. What is Underfitting?<\/p>\n<p class=\"my-2 [&amp;+p]:mt-4 [&amp;_strong:has(+br)]:inline-block [&amp;_strong:has(+br)]:pb-2\">When a model is too simple to capture the data pattern.<\/p>\n<p class=\"mb-2 mt-4 font-display font-semimedium text-base first:mt-0\">19. How to prevent overfitting?<\/p>\n<p class=\"my-2 [&amp;+p]:mt-4 [&amp;_strong:has(+br)]:inline-block [&amp;_strong:has(+br)]:pb-2\">Use techniques like cross-validation, regularization, pruning, early stopping.<\/p>\n<p class=\"mb-2 mt-4 font-display font-semimedium text-base first:mt-0\">20. What is Cross-Validation?<\/p>\n<p class=\"my-2 [&amp;+p]:mt-4 [&amp;_strong:has(+br)]:inline-block [&amp;_strong:has(+br)]:pb-2\">A technique to evaluate model\u2019s generalization by partitioning data into folds.<\/p>\n<p class=\"mb-2 mt-4 font-display font-semimedium text-base first:mt-0\">21. Define Bias and Variance.<\/p>\n<p class=\"my-2 [&amp;+p]:mt-4 [&amp;_strong:has(+br)]:inline-block [&amp;_strong:has(+br)]:pb-2\">Bias is error from erroneous assumptions; variance is error from sensitivity to data fluctuations.<\/p>\n<p class=\"mb-2 mt-4 font-display font-semimedium text-base first:mt-0\">22. What is Regularization?<\/p>\n<p class=\"my-2 [&amp;+p]:mt-4 [&amp;_strong:has(+br)]:inline-block [&amp;_strong:has(+br)]:pb-2\">Technique to reduce overfitting by adding penalty terms to loss functions.<\/p>\n<p class=\"mb-2 mt-4 font-display font-semimedium text-base first:mt-0\">23. Difference between L1 and L2 regularization?<\/p>\n<p class=\"my-2 [&amp;+p]:mt-4 [&amp;_strong:has(+br)]:inline-block [&amp;_strong:has(+br)]:pb-2\">L1 adds absolute weights penalty (sparsity), L2 adds squared weights penalty.<\/p>\n<p class=\"mb-2 mt-4 font-display font-semimedium text-base first:mt-0\">24. What is Gradient Descent?<\/p>\n<p class=\"my-2 [&amp;+p]:mt-4 [&amp;_strong:has(+br)]:inline-block [&amp;_strong:has(+br)]:pb-2\">Optimization algorithm to minimize the loss by iteratively adjusting parameters.<\/p>\n<p class=\"mb-2 mt-4 font-display font-semimedium text-base first:mt-0\">25. Different types of Gradient Descent?<\/p>\n<p class=\"my-2 [&amp;+p]:mt-4 [&amp;_strong:has(+br)]:inline-block [&amp;_strong:has(+br)]:pb-2\">Batch, Stochastic, Mini-batch.<\/p>\n<p class=\"mb-2 mt-4 font-display font-semimedium text-base first:mt-0\">26. What is a Decision Tree?<\/p>\n<p class=\"my-2 [&amp;+p]:mt-4 [&amp;_strong:has(+br)]:inline-block [&amp;_strong:has(+br)]:pb-2\">A tree-like model used for classification and regression.<\/p>\n<p class=\"mb-2 mt-4 font-display font-semimedium text-base first:mt-0\">27. Explain Random Forest.<\/p>\n<p class=\"my-2 [&amp;+p]:mt-4 [&amp;_strong:has(+br)]:inline-block [&amp;_strong:has(+br)]:pb-2\">An ensemble of decision trees to improve accuracy and reduce overfitting.<\/p>\n<p class=\"mb-2 mt-4 font-display font-semimedium text-base first:mt-0\">28. What is Ensemble Learning?<\/p>\n<p class=\"my-2 [&amp;+p]:mt-4 [&amp;_strong:has(+br)]:inline-block [&amp;_strong:has(+br)]:pb-2\">Combining multiple models to improve performance.<\/p>\n<p class=\"mb-2 mt-4 font-display font-semimedium text-base first:mt-0\">29. Difference between Bagging and Boosting?<\/p>\n<p class=\"my-2 [&amp;+p]:mt-4 [&amp;_strong:has(+br)]:inline-block [&amp;_strong:has(+br)]:pb-2\">Bagging reduces variance by averaging; Boosting reduces bias by sequentially correcting errors.<\/p>\n<p class=\"mb-2 mt-4 font-display font-semimedium text-base first:mt-0\">30. What is PCA (Principal Component Analysis)?<\/p>\n<p class=\"my-2 [&amp;+p]:mt-4 [&amp;_strong:has(+br)]:inline-block [&amp;_strong:has(+br)]:pb-2\">A technique to reduce dimensionality by projecting data onto principal components.<\/p>\n<p class=\"mb-2 mt-4 font-display font-semimedium text-base first:mt-0\">31. What is Clustering?<\/p>\n<p class=\"my-2 [&amp;+p]:mt-4 [&amp;_strong:has(+br)]:inline-block [&amp;_strong:has(+br)]:pb-2\">Grouping similar data points together.<\/p>\n<p class=\"mb-2 mt-4 font-display font-semimedium text-base first:mt-0\">32. Explain K-means Clustering.<\/p>\n<p class=\"my-2 [&amp;+p]:mt-4 [&amp;_strong:has(+br)]:inline-block [&amp;_strong:has(+br)]:pb-2\">An algorithm that partitions data into k clusters by minimizing distances.<\/p>\n<p class=\"mb-2 mt-4 font-display font-semimedium text-base first:mt-0\">33. What is Hierarchical Clustering?<\/p>\n<p class=\"my-2 [&amp;+p]:mt-4 [&amp;_strong:has(+br)]:inline-block [&amp;_strong:has(+br)]:pb-2\">Cluster data into a tree of clusters.<\/p>\n<p class=\"mb-2 mt-4 font-display font-semimedium text-base first:mt-0\">34. What is the Curse of Dimensionality?<\/p>\n<p class=\"my-2 [&amp;+p]:mt-4 [&amp;_strong:has(+br)]:inline-block [&amp;_strong:has(+br)]:pb-2\">High-dimensional data becomes sparse and affects model performance.<\/p>\n<p class=\"mb-2 mt-4 font-display font-semimedium text-base first:mt-0\">35. Types of Machine Learning Algorithms?<\/p>\n<p class=\"my-2 [&amp;+p]:mt-4 [&amp;_strong:has(+br)]:inline-block [&amp;_strong:has(+br)]:pb-2\">Supervised, Unsupervised, Reinforcement Learning algorithms.<\/p>\n<p class=\"mb-2 mt-4 font-display font-semimedium text-base first:mt-0\">36. What is a Neural Network?<\/p>\n<p class=\"my-2 [&amp;+p]:mt-4 [&amp;_strong:has(+br)]:inline-block [&amp;_strong:has(+br)]:pb-2\">A network of interconnected nodes inspired by the human brain.<\/p>\n<p class=\"mb-2 mt-4 font-display font-semimedium text-base first:mt-0\">37. What is Deep Learning?<\/p>\n<p class=\"my-2 [&amp;+p]:mt-4 [&amp;_strong:has(+br)]:inline-block [&amp;_strong:has(+br)]:pb-2\">A subset of ML using multi-layered neural networks.<\/p>\n<p class=\"mb-2 mt-4 font-display font-semimedium text-base first:mt-0\">38. What are Activation Functions?<\/p>\n<p class=\"my-2 [&amp;+p]:mt-4 [&amp;_strong:has(+br)]:inline-block [&amp;_strong:has(+br)]:pb-2\">Functions like ReLU, sigmoid used to introduce non-linearity in networks.<\/p>\n<p class=\"mb-2 mt-4 font-display font-semimedium text-base first:mt-0\">39. What is Backpropagation?<\/p>\n<p class=\"my-2 [&amp;+p]:mt-4 [&amp;_strong:has(+br)]:inline-block [&amp;_strong:has(+br)]:pb-2\">An algorithm for training neural networks by updating weights based on error gradient.<\/p>\n<p class=\"mb-2 mt-4 font-display font-semimedium text-base first:mt-0\">40. Difference between Batch and Online Learning?<\/p>\n<p class=\"my-2 [&amp;+p]:mt-4 [&amp;_strong:has(+br)]:inline-block [&amp;_strong:has(+br)]:pb-2\">Batch learns from all data at once; online learns incrementally.<\/p>\n<p class=\"mb-2 mt-4 font-display font-semimedium text-base first:mt-0\">41. What is a Confusion Matrix used for?<\/p>\n<p class=\"my-2 [&amp;+p]:mt-4 [&amp;_strong:has(+br)]:inline-block [&amp;_strong:has(+br)]:pb-2\">Evaluating classification model effectiveness.<\/p>\n<p class=\"mb-2 mt-4 font-display font-semimedium text-base first:mt-0\">42. What is Data Preprocessing?<\/p>\n<p class=\"my-2 [&amp;+p]:mt-4 [&amp;_strong:has(+br)]:inline-block [&amp;_strong:has(+br)]:pb-2\">Cleaning and transforming raw data before model training.<\/p>\n<p class=\"mb-2 mt-4 font-display font-semimedium text-base first:mt-0\">43. Why is Feature Scaling important?<\/p>\n<p class=\"my-2 [&amp;+p]:mt-4 [&amp;_strong:has(+br)]:inline-block [&amp;_strong:has(+br)]:pb-2\">Ensures features contribute equally to distance calculations in models.<\/p>\n<p class=\"mb-2 mt-4 font-display font-semimedium text-base first:mt-0\">44. What are Hyperparameters?<\/p>\n<p class=\"my-2 [&amp;+p]:mt-4 [&amp;_strong:has(+br)]:inline-block [&amp;_strong:has(+br)]:pb-2\">Settings used to control the learning process.<\/p>\n<p class=\"mb-2 mt-4 font-display font-semimedium text-base first:mt-0\">45. Difference between Parametric and Non-parametric Models?<\/p>\n<p class=\"my-2 [&amp;+p]:mt-4 [&amp;_strong:has(+br)]:inline-block [&amp;_strong:has(+br)]:pb-2\">Parametric assume fixed number of parameters; non-parametric grow with data.<\/p>\n<p class=\"mb-2 mt-4 font-display font-semimedium text-base first:mt-0\">46. What is ROC AUC?<\/p>\n<p class=\"my-2 [&amp;+p]:mt-4 [&amp;_strong:has(+br)]:inline-block [&amp;_strong:has(+br)]:pb-2\">Area under the ROC curve measuring classification performance.<\/p>\n<p class=\"mb-2 mt-4 font-display font-semimedium text-base first:mt-0\">47. Define Precision-Recall Curve.<\/p>\n<p class=\"my-2 [&amp;+p]:mt-4 [&amp;_strong:has(+br)]:inline-block [&amp;_strong:has(+br)]:pb-2\">Graph showing trade-off between precision and recall for different thresholds.<\/p>\n<p class=\"mb-2 mt-4 font-display font-semimedium text-base first:mt-0\">48. What is an Epoch?<\/p>\n<p class=\"my-2 [&amp;+p]:mt-4 [&amp;_strong:has(+br)]:inline-block [&amp;_strong:has(+br)]:pb-2\">One pass over entire training data in neural networks.<\/p>\n<p class=\"mb-2 mt-4 font-display font-semimedium text-base first:mt-0\">49. What is Over-sampling and Under-sampling?<\/p>\n<p class=\"my-2 [&amp;+p]:mt-4 [&amp;_strong:has(+br)]:inline-block [&amp;_strong:has(+br)]:pb-2\">Techniques to balance imbalanced datasets.<\/p>\n<p class=\"mb-2 mt-4 font-display font-semimedium text-base first:mt-0\">50. What is Feature Engineering?<\/p>\n<p class=\"my-2 [&amp;+p]:mt-4 [&amp;_strong:has(+br)]:inline-block [&amp;_strong:has(+br)]:pb-2\">Creating new features from raw data to improve model performance.<\/p>\n<h3>Free Tutorials To Learn<\/h3>\n<table dir=\"ltr\" border=\"1\" cellspacing=\"0\" cellpadding=\"0\">\n<colgroup>\n<col width=\"176\" \/>\n<col width=\"100\" \/><\/colgroup>\n<tbody>\n<tr>\n<td data-sheets-value=\"{&quot;1&quot;:2,&quot;2&quot;:&quot;SQL Tutorial for Beginners PDF - Learn SQL Basics&quot;}\" data-sheets-hyperlink=\"https:\/\/entri.app\/blog\/sql-tutorial\/\">SQL Tutorial for Beginners PDF &#8211; Learn SQL Basics<\/td>\n<td data-sheets-value=\"{&quot;1&quot;:2,&quot;2&quot;:&quot;Learn Now&quot;}\"><strong><div class=\"lead-gen-block\"><a href=\"https:\/\/entri.app\/blog\/sql-tutorial\/\" data-url=\"https:\/\/entri.app\/blog\/sql-tutorial\/\" class=\"lead-pdf-download\" data-id=\"25556853\"><\/strong><\/p>\n<p><button class=\"btn btn-default\">Learn Now<br \/>\n<\/button><\/p>\n<p><strong><\/a><\/div><\/strong><\/td>\n<\/tr>\n<tr>\n<td data-sheets-value=\"{&quot;1&quot;:2,&quot;2&quot;:&quot;HTML Exercises to Practice | HTML Tutorial&quot;}\" data-sheets-hyperlink=\"https:\/\/entri.app\/blog\/html-exercises-to-practice-html-tutorial\/\">HTML Exercises to Practice | HTML Tutorial<\/td>\n<td data-sheets-value=\"{&quot;1&quot;:2,&quot;2&quot;:&quot;Learn Now&quot;}\"><strong><div class=\"lead-gen-block\"><a href=\"https:\/\/entri.app\/blog\/html-exercises-to-practice-html-tutorial\/\" data-url=\"https:\/\/entri.app\/blog\/html-exercises-to-practice-html-tutorial\/\" class=\"lead-pdf-download\" data-id=\"25556853\"><\/strong><\/p>\n<p><button class=\"btn btn-default\">Learn Now<br \/>\n<\/button><\/p>\n<p><strong><\/a><\/div><\/strong><\/td>\n<\/tr>\n<tr>\n<td data-sheets-value=\"{&quot;1&quot;:2,&quot;2&quot;:&quot;DSA Practice Series | DSA Tutorials&quot;}\" data-sheets-hyperlink=\"https:\/\/entri.app\/blog\/dsa-practice-series-dsa-tutorials\/\">DSA Practice Series | DSA Tutorials<\/td>\n<td data-sheets-value=\"{&quot;1&quot;:2,&quot;2&quot;:&quot;Learn Now&quot;}\"><strong><div class=\"lead-gen-block\"><a href=\"https:\/\/entri.app\/blog\/dsa-practice-series-dsa-tutorials\/\" data-url=\"https:\/\/entri.app\/blog\/dsa-practice-series-dsa-tutorials\/\" class=\"lead-pdf-download\" data-id=\"25556853\"><\/strong><\/p>\n<p><button class=\"btn btn-default\">Learn Now<br \/>\n<\/button><\/p>\n<p><strong><\/a><\/div><\/strong><\/td>\n<\/tr>\n<tr>\n<td data-sheets-value=\"{&quot;1&quot;:2,&quot;2&quot;:&quot;Java Programming Notes PDF 2023&quot;}\" data-sheets-hyperlink=\"https:\/\/entri.app\/blog\/java-programming-notes-pdf\/\">Java Programming Notes PDF 2023<\/td>\n<td data-sheets-value=\"{&quot;1&quot;:2,&quot;2&quot;:&quot;Learn Now&quot;}\"><strong><div class=\"lead-gen-block\"><a href=\"https:\/\/entri.app\/blog\/java-programming-notes-pdf\/\" data-url=\"https:\/\/entri.app\/blog\/java-programming-notes-pdf\/\" class=\"lead-pdf-download\" data-id=\"25556853\"><\/strong><\/p>\n<p><a href=\"https:\/\/entri.app\/blog\/java-programming-notes-pdf\/\"><button class=\"btn btn-default\">Learn Now<br \/>\n<\/button><\/a><\/p>\n<p><strong><\/a><\/div><\/strong><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p class=\"mb-2 mt-4 font-display font-semimedium text-base first:mt-0\">51. What is a Confounder?<\/p>\n<p class=\"my-2 [&amp;+p]:mt-4 [&amp;_strong:has(+br)]:inline-block [&amp;_strong:has(+br)]:pb-2\">A variable influencing both independent and dependent variables.<\/p>\n<p class=\"mb-2 mt-4 font-display font-semimedium text-base first:mt-0\">52. What is Concept Drift?<\/p>\n<p class=\"my-2 [&amp;+p]:mt-4 [&amp;_strong:has(+br)]:inline-block [&amp;_strong:has(+br)]:pb-2\">When the statistical properties of target variable change over time.<\/p>\n<p class=\"mb-2 mt-4 font-display font-semimedium text-base first:mt-0\">53. What is a Kernel in SVM?<\/p>\n<p class=\"my-2 [&amp;+p]:mt-4 [&amp;_strong:has(+br)]:inline-block [&amp;_strong:has(+br)]:pb-2\">Function that transforms data to higher dimension for linear separability.<\/p>\n<p class=\"mb-2 mt-4 font-display font-semimedium text-base first:mt-0\">54. Difference between Parametric and Non-Parametric SVM?<\/p>\n<p class=\"my-2 [&amp;+p]:mt-4 [&amp;_strong:has(+br)]:inline-block [&amp;_strong:has(+br)]:pb-2\">Parametric uses fixed parameters; non-parametric depends on the data points (support vectors).<\/p>\n<p class=\"mb-2 mt-4 font-display font-semimedium text-base first:mt-0\">55. What is Bias-Variance Tradeoff?<\/p>\n<p class=\"my-2 [&amp;+p]:mt-4 [&amp;_strong:has(+br)]:inline-block [&amp;_strong:has(+br)]:pb-2\">Balancing underfitting (bias) and overfitting (variance) in model.<\/p>\n<p class=\"mb-2 mt-4 font-display font-semimedium text-base first:mt-0\">56. What is Early Stopping?<\/p>\n<p class=\"my-2 [&amp;+p]:mt-4 [&amp;_strong:has(+br)]:inline-block [&amp;_strong:has(+br)]:pb-2\">Halting model training when performance on validation set stops improving.<\/p>\n<p class=\"mb-2 mt-4 font-display font-semimedium text-base first:mt-0\">57. What is Data Leakage?<\/p>\n<p class=\"my-2 [&amp;+p]:mt-4 [&amp;_strong:has(+br)]:inline-block [&amp;_strong:has(+br)]:pb-2\">When information from outside training data influences the model.<\/p>\n<p class=\"mb-2 mt-4 font-display font-semimedium text-base first:mt-0\">58. What is the purpose of Softmax?<\/p>\n<p class=\"my-2 [&amp;+p]:mt-4 [&amp;_strong:has(+br)]:inline-block [&amp;_strong:has(+br)]:pb-2\">Converts outputs into probability distribution for multi-class classification.<\/p>\n<p class=\"mb-2 mt-4 font-display font-semimedium text-base first:mt-0\">59. What is a Confusion Matrix?<\/p>\n<p class=\"my-2 [&amp;+p]:mt-4 [&amp;_strong:has(+br)]:inline-block [&amp;_strong:has(+br)]:pb-2\">See Question 10.<\/p>\n<p class=\"mb-2 mt-4 font-display font-semimedium text-base first:mt-0\">60. What is Clustering?<\/p>\n<p class=\"my-2 [&amp;+p]:mt-4 [&amp;_strong:has(+br)]:inline-block [&amp;_strong:has(+br)]:pb-2\">See Question 31.<\/p>\n<p class=\"mb-2 mt-4 font-display font-semimedium text-base first:mt-0\">61. What is the difference between Logistic Regression and Linear Regression?<\/p>\n<p class=\"my-2 [&amp;+p]:mt-4 [&amp;_strong:has(+br)]:inline-block [&amp;_strong:has(+br)]:pb-2\">Logistic for classification; linear for regression problems.<\/p>\n<p class=\"mb-2 mt-4 font-display font-semimedium text-base first:mt-0\">62. What is Gradient Boosting?<\/p>\n<p class=\"my-2 [&amp;+p]:mt-4 [&amp;_strong:has(+br)]:inline-block [&amp;_strong:has(+br)]:pb-2\">An ensemble technique combining weak learners to create strong prediction.<\/p>\n<p class=\"mb-2 mt-4 font-display font-semimedium text-base first:mt-0\">63. What is AdaBoost?<\/p>\n<p class=\"my-2 [&amp;+p]:mt-4 [&amp;_strong:has(+br)]:inline-block [&amp;_strong:has(+br)]:pb-2\">Adaptive boosting algorithm that adjusts weights of training examples.<\/p>\n<p class=\"mb-2 mt-4 font-display font-semimedium text-base first:mt-0\">64. What is XGBoost?<\/p>\n<p class=\"my-2 [&amp;+p]:mt-4 [&amp;_strong:has(+br)]:inline-block [&amp;_strong:has(+br)]:pb-2\">An optimized gradient boosting framework.<\/p>\n<p class=\"mb-2 mt-4 font-display font-semimedium text-base first:mt-0\">65. What is Bagging?<\/p>\n<p class=\"my-2 [&amp;+p]:mt-4 [&amp;_strong:has(+br)]:inline-block [&amp;_strong:has(+br)]:pb-2\">Bootstrap aggregating that trains multiple models on random subsets.<\/p>\n<p class=\"mb-2 mt-4 font-display font-semimedium text-base first:mt-0\">66. What are Outliers?<\/p>\n<p class=\"my-2 [&amp;+p]:mt-4 [&amp;_strong:has(+br)]:inline-block [&amp;_strong:has(+br)]:pb-2\">Data points distant from others, potentially skewing models.<\/p>\n<p class=\"mb-2 mt-4 font-display font-semimedium text-base first:mt-0\">67. What is the Central Limit Theorem?<\/p>\n<p class=\"my-2 [&amp;+p]:mt-4 [&amp;_strong:has(+br)]:inline-block [&amp;_strong:has(+br)]:pb-2\">Sum of distributions tends toward normal distribution as sample size increases.<\/p>\n<p class=\"mb-2 mt-4 font-display font-semimedium text-base first:mt-0\">68. What is Cross-Entropy Loss?<\/p>\n<p class=\"my-2 [&amp;+p]:mt-4 [&amp;_strong:has(+br)]:inline-block [&amp;_strong:has(+br)]:pb-2\">Loss function for classification measuring difference between two probability distributions.<\/p>\n<p class=\"mb-2 mt-4 font-display font-semimedium text-base first:mt-0\">69. What is RMSE?<\/p>\n<p class=\"my-2 [&amp;+p]:mt-4 [&amp;_strong:has(+br)]:inline-block [&amp;_strong:has(+br)]:pb-2\">Root Mean Square Error, a metric for regression accuracy.<\/p>\n<p class=\"mb-2 mt-4 font-display font-semimedium text-base first:mt-0\">70. What is Mean Absolute Error?<\/p>\n<p class=\"my-2 [&amp;+p]:mt-4 [&amp;_strong:has(+br)]:inline-block [&amp;_strong:has(+br)]:pb-2\">Average of absolute errors between predicted and actual values.<\/p>\n<p class=\"mb-2 mt-4 font-display font-semimedium text-base first:mt-0\">71. What is Hyperparameter Tuning?<\/p>\n<p class=\"my-2 [&amp;+p]:mt-4 [&amp;_strong:has(+br)]:inline-block [&amp;_strong:has(+br)]:pb-2\">Optimizing model parameters for best performance.<\/p>\n<p class=\"mb-2 mt-4 font-display font-semimedium text-base first:mt-0\">72. What is Grid Search?<\/p>\n<p class=\"my-2 [&amp;+p]:mt-4 [&amp;_strong:has(+br)]:inline-block [&amp;_strong:has(+br)]:pb-2\">Systematic way of tuning hyperparameters.<\/p>\n<p class=\"mb-2 mt-4 font-display font-semimedium text-base first:mt-0\">73. What is Random Search?<\/p>\n<p class=\"my-2 [&amp;+p]:mt-4 [&amp;_strong:has(+br)]:inline-block [&amp;_strong:has(+br)]:pb-2\">Selecting random combinations of hyperparameters to tune.<\/p>\n<p class=\"mb-2 mt-4 font-display font-semimedium text-base first:mt-0\">74. What is the difference between Generative and Discriminative models?<\/p>\n<p class=\"my-2 [&amp;+p]:mt-4 [&amp;_strong:has(+br)]:inline-block [&amp;_strong:has(+br)]:pb-2\">Generative models learn joint probability; discriminative learn decision boundaries.<\/p>\n<p class=\"mb-2 mt-4 font-display font-semimedium text-base first:mt-0\">75. What is a Markov Chain?<\/p>\n<p class=\"my-2 [&amp;+p]:mt-4 [&amp;_strong:has(+br)]:inline-block [&amp;_strong:has(+br)]:pb-2\">Model describing sequence of events with probabilities based on current state.<\/p>\n<p class=\"mb-2 mt-4 font-display font-semimedium text-base first:mt-0\">76. What is the Curse of Dimensionality?<\/p>\n<p class=\"my-2 [&amp;+p]:mt-4 [&amp;_strong:has(+br)]:inline-block [&amp;_strong:has(+br)]:pb-2\">See Question 34.<\/p>\n<p class=\"mb-2 mt-4 font-display font-semimedium text-base first:mt-0\">77. What is Bootstrapping?<\/p>\n<p class=\"my-2 [&amp;+p]:mt-4 [&amp;_strong:has(+br)]:inline-block [&amp;_strong:has(+br)]:pb-2\">Sampling method to estimate statistics from data.<\/p>\n<p class=\"mb-2 mt-4 font-display font-semimedium text-base first:mt-0\">78. What is the No-Free-Lunch Theorem?<\/p>\n<p class=\"my-2 [&amp;+p]:mt-4 [&amp;_strong:has(+br)]:inline-block [&amp;_strong:has(+br)]:pb-2\">No one model works best for all problems.<\/p>\n<p class=\"mb-2 mt-4 font-display font-semimedium text-base first:mt-0\">79. What is Transfer Learning?<\/p>\n<p class=\"my-2 [&amp;+p]:mt-4 [&amp;_strong:has(+br)]:inline-block [&amp;_strong:has(+br)]:pb-2\">Using knowledge from one task to improve performance on another.<\/p>\n<p class=\"mb-2 mt-4 font-display font-semimedium text-base first:mt-0\">80. What is the difference between ANN and CNN?<\/p>\n<p class=\"my-2 [&amp;+p]:mt-4 [&amp;_strong:has(+br)]:inline-block [&amp;_strong:has(+br)]:pb-2\">ANNs are general; CNNs specialize in spatial hierarchies like images.<\/p>\n<p class=\"mb-2 mt-4 font-display font-semimedium text-base first:mt-0\">81. What is Dropout in Neural Networks?<\/p>\n<p class=\"my-2 [&amp;+p]:mt-4 [&amp;_strong:has(+br)]:inline-block [&amp;_strong:has(+br)]:pb-2\">Technique to prevent overfitting by randomly disabling neurons.<\/p>\n<p class=\"mb-2 mt-4 font-display font-semimedium text-base first:mt-0\">82. What is Batch Normalization?<\/p>\n<p class=\"my-2 [&amp;+p]:mt-4 [&amp;_strong:has(+br)]:inline-block [&amp;_strong:has(+br)]:pb-2\">Method to stabilize and accelerate training deep networks.<\/p>\n<p class=\"mb-2 mt-4 font-display font-semimedium text-base first:mt-0\">83. What is Loss Function?<\/p>\n<p class=\"my-2 [&amp;+p]:mt-4 [&amp;_strong:has(+br)]:inline-block [&amp;_strong:has(+br)]:pb-2\">Function that quantifies error during training.<\/p>\n<p class=\"mb-2 mt-4 font-display font-semimedium text-base first:mt-0\">84. What is Epoch?<\/p>\n<p class=\"my-2 [&amp;+p]:mt-4 [&amp;_strong:has(+br)]:inline-block [&amp;_strong:has(+br)]:pb-2\">See Question 48.<\/p>\n<p class=\"mb-2 mt-4 font-display font-semimedium text-base first:mt-0\">85. What is Learning Rate?<\/p>\n<p class=\"my-2 [&amp;+p]:mt-4 [&amp;_strong:has(+br)]:inline-block [&amp;_strong:has(+br)]:pb-2\">Step size in updating model parameters during training.<\/p>\n<p class=\"mb-2 mt-4 font-display font-semimedium text-base first:mt-0\">86. What is Gradient Vanishing Problem?<\/p>\n<p class=\"my-2 [&amp;+p]:mt-4 [&amp;_strong:has(+br)]:inline-block [&amp;_strong:has(+br)]:pb-2\">When gradients become too small for parameters to update.<\/p>\n<p class=\"mb-2 mt-4 font-display font-semimedium text-base first:mt-0\">87. What is Early Stopping?<\/p>\n<p class=\"my-2 [&amp;+p]:mt-4 [&amp;_strong:has(+br)]:inline-block [&amp;_strong:has(+br)]:pb-2\">See Question 56.<\/p>\n<p class=\"mb-2 mt-4 font-display font-semimedium text-base first:mt-0\">88. What is LSTM?<\/p>\n<p class=\"my-2 [&amp;+p]:mt-4 [&amp;_strong:has(+br)]:inline-block [&amp;_strong:has(+br)]:pb-2\">Long Short-Term Memory networks good for sequential data.<\/p>\n<p class=\"mb-2 mt-4 font-display font-semimedium text-base first:mt-0\">89. What is Reinforcement Learning?<\/p>\n<p class=\"my-2 [&amp;+p]:mt-4 [&amp;_strong:has(+br)]:inline-block [&amp;_strong:has(+br)]:pb-2\">See Question 3.<\/p>\n<p class=\"mb-2 mt-4 font-display font-semimedium text-base first:mt-0\">90. What is Deep Learning?<\/p>\n<p class=\"my-2 [&amp;+p]:mt-4 [&amp;_strong:has(+br)]:inline-block [&amp;_strong:has(+br)]:pb-2\">Subset of ML using deep neural networks.<\/p>\n<p class=\"mb-2 mt-4 font-display font-semimedium text-base first:mt-0\">91. What are Convolutional Layers?<\/p>\n<p class=\"my-2 [&amp;+p]:mt-4 [&amp;_strong:has(+br)]:inline-block [&amp;_strong:has(+br)]:pb-2\">Layers in CNN that detect spatial features.<\/p>\n<p class=\"mb-2 mt-4 font-display font-semimedium text-base first:mt-0\">92. What are Recurrent Neural Networks?<\/p>\n<p class=\"my-2 [&amp;+p]:mt-4 [&amp;_strong:has(+br)]:inline-block [&amp;_strong:has(+br)]:pb-2\">Networks designed for sequence data processing.<\/p>\n<p class=\"mb-2 mt-4 font-display font-semimedium text-base first:mt-0\">93. What is Overfitting?<\/p>\n<p class=\"my-2 [&amp;+p]:mt-4 [&amp;_strong:has(+br)]:inline-block [&amp;_strong:has(+br)]:pb-2\">See Question 17.<\/p>\n<p class=\"mb-2 mt-4 font-display font-semimedium text-base first:mt-0\">94. What is the difference between Epoch and Batch?<\/p>\n<p class=\"my-2 [&amp;+p]:mt-4 [&amp;_strong:has(+br)]:inline-block [&amp;_strong:has(+br)]:pb-2\">Epoch is full pass over data; batch is subset passed to the model.<\/p>\n<p class=\"mb-2 mt-4 font-display font-semimedium text-base first:mt-0\">95. What is a Learning Curve?<\/p>\n<p class=\"my-2 [&amp;+p]:mt-4 [&amp;_strong:has(+br)]:inline-block [&amp;_strong:has(+br)]:pb-2\">Graph of model performance vs number of training samples.<\/p>\n<p class=\"mb-2 mt-4 font-display font-semimedium text-base first:mt-0\">96. What is Feature Selection?<\/p>\n<p class=\"my-2 [&amp;+p]:mt-4 [&amp;_strong:has(+br)]:inline-block [&amp;_strong:has(+br)]:pb-2\">Choosing most relevant features for better model.<\/p>\n<p class=\"mb-2 mt-4 font-display font-semimedium text-base first:mt-0\">97. What are Dimensionality Reduction techniques?<\/p>\n<p class=\"my-2 [&amp;+p]:mt-4 [&amp;_strong:has(+br)]:inline-block [&amp;_strong:has(+br)]:pb-2\">Methods like PCA, t-SNE to reduce data features.<\/p>\n<p class=\"mb-2 mt-4 font-display font-semimedium text-base first:mt-0\">98. What is a Decision Boundary?<\/p>\n<p class=\"my-2 [&amp;+p]:mt-4 [&amp;_strong:has(+br)]:inline-block [&amp;_strong:has(+br)]:pb-2\">The line\/surface separating classes in classification tasks.<\/p>\n<p class=\"mb-2 mt-4 font-display font-semimedium text-base first:mt-0\">99. What are Support Vectors?<\/p>\n<p class=\"my-2 [&amp;+p]:mt-4 [&amp;_strong:has(+br)]:inline-block [&amp;_strong:has(+br)]:pb-2\">Data points closest to decision boundary in SVM.<\/p>\n<p class=\"mb-2 mt-4 font-display font-semimedium text-base first:mt-0\">100. What is Model Evaluation?<\/p>\n<p class=\"my-2 [&amp;+p]:mt-4 [&amp;_strong:has(+br)]:inline-block [&amp;_strong:has(+br)]:pb-2\">Process of assessing model\u2019s predictive performance using metrics.<\/p>\n<\/div>\n<h2><span class=\"ez-toc-section\" id=\"Conclusion\"><\/span><strong>Conclusion<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>Preparing for a machine learning interview\u00a0can be daunting. To help you\u00a0prepare confidently, this guide covers\u00a0the 100 most commonly asked ML interview questions\u00a0and their clear, concise answers. These questions\u00a0cover fundamental\u00a0concepts, algorithms, practical know-how, and interview tips to help you\u00a0succeed whether\u00a0you\u2019re a fresher or experienced\u00a0candidate.<\/p>\n<h3><strong style=\"font-size: 1.25em;\">Related Articles<\/strong><\/h3>\n<div class=\"table-responsive wprt_style_display\">\n<div class=\"table-responsive wprt_style_display\">\n<table class=\"table\" dir=\"ltr\" border=\"1\" cellspacing=\"0\" cellpadding=\"0\">\n<colgroup>\n<col width=\"329\" \/>\n<col width=\"309\" \/><\/colgroup>\n<tbody>\n<tr>\n<td data-sheets-value=\"{&quot;1&quot;:2,&quot;2&quot;:&quot;Kerala PSC VFA Syllabus&quot;}\" data-sheets-hyperlink=\"https:\/\/entri.app\/blog\/kerala-psc-village-field-assistant-vfa-syllabus-exam-pattern\/\"><strong><a class=\"in-cell-link\" href=\"https:\/\/entri.app\/blog\/data-science-jobs-in-kerala\/\" target=\"_blank\" rel=\"noopener\">Data Science Jobs in Kerala<\/a><\/strong><\/td>\n<td 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aria-hidden=\"true\"><\/div>\n<\/form>\n<\/div>\n\n<\/div><\/div>\n<\/div>\n<\/div>\n","protected":false},"excerpt":{"rendered":"<p>Introduction Preparing for a machine learning interview can feel overwhelming with the vast amount of knowledge involved. To help you navigate this journey confidently, we have compiled 100 of the most commonly asked interview questions along with concise, clear answers. Whether you\u2019re a fresher or a seasoned professional, this guide covers theory, algorithms, practical concepts, [&hellip;]<\/p>\n","protected":false},"author":72,"featured_media":25488591,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[802,1864,1841],"tags":[],"class_list":["post-25488600","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-articles","category-data-science-ml","category-entri-skilling"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v26.6 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Top 100 Machine Learning Interview Questions and Answers<\/title>\n<meta name=\"description\" content=\"This article contains 100 Machine Learning Interview Questions and Answers in pdf for you to download and attend the interview!\" \/>\n<meta name=\"robots\" content=\"index, follow, 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