Key Concepts for Machine Learning Interviews
1. Supervised Learning: Basics of training models on labeled data. Important algorithms: Linear Regression, Logistic Regression, SVM, k-NN, Decision Trees, Random Forests.
2. Unsupervised Learning: Techniques for working with unlabeled data. Key methods: k-Means, Hierarchical Clustering, PCA, t-SNE.
3. Model Evaluation Metrics: Understanding metrics like accuracy, precision, recall, F1 score, ROC-AUC, MSE, R-squared, and when to use them.
4. Overfitting and Underfitting: Know how to detect and fix overfitting/underfitting using cross-validation, regularization (L1, L2), and pruning.
5. Feature Engineering: Create new features to improve models with methods like one-hot encoding, scaling, polynomial features, and feature selection like RFE.
6. Hyperparameter Tuning: Improve model accuracy by tuning parameters using Grid Search, Random Search, and Bayesian Optimization.
7. Ensemble Methods: Combine multiple models for better accuracy. Includes Bagging (Random Forests), Boosting (AdaBoost, XGBoost), and Stacking.
8. Neural Networks & Deep Learning: Understand basics like activation functions, backpropagation, gradient descent; and architectures like CNNs and RNNs.
9. Natural Language Processing (NLP): Key methods include tokenization, stemming, lemmatization, word embeddings (Word2Vec, GloVe), transformers (BERT, GPT), sentiment analysis.
10. Dimensionality Reduction: Reduce features while keeping info with PCA, SVD, and feature importance techniques.
11. Reinforcement Learning: Agents learn decisions via rewards/penalties. Concepts: MDPs, Q-learning, policy gradients.
12. Big Data & Scalable ML: Handle large datasets and scale models using Apache Spark, Hadoop, and distributed training.
13. Model Deployment & Monitoring: Deploy and monitor ML models with tools like TensorFlow Serving, AWS SageMaker, Docker, Flask.
14. Ethics in Machine Learning: Understand bias, fairness, transparency, and accountability to build ethical and accurate models.
15. Bayesian Inference: Update probabilities with new evidence. Learn Bayes’ theorem, prior/posterior distributions, Bayesian networks.
1. Supervised Learning: Basics of training models on labeled data. Important algorithms: Linear Regression, Logistic Regression, SVM, k-NN, Decision Trees, Random Forests.
2. Unsupervised Learning: Techniques for working with unlabeled data. Key methods: k-Means, Hierarchical Clustering, PCA, t-SNE.
3. Model Evaluation Metrics: Understanding metrics like accuracy, precision, recall, F1 score, ROC-AUC, MSE, R-squared, and when to use them.
4. Overfitting and Underfitting: Know how to detect and fix overfitting/underfitting using cross-validation, regularization (L1, L2), and pruning.
5. Feature Engineering: Create new features to improve models with methods like one-hot encoding, scaling, polynomial features, and feature selection like RFE.
6. Hyperparameter Tuning: Improve model accuracy by tuning parameters using Grid Search, Random Search, and Bayesian Optimization.
7. Ensemble Methods: Combine multiple models for better accuracy. Includes Bagging (Random Forests), Boosting (AdaBoost, XGBoost), and Stacking.
8. Neural Networks & Deep Learning: Understand basics like activation functions, backpropagation, gradient descent; and architectures like CNNs and RNNs.
9. Natural Language Processing (NLP): Key methods include tokenization, stemming, lemmatization, word embeddings (Word2Vec, GloVe), transformers (BERT, GPT), sentiment analysis.
10. Dimensionality Reduction: Reduce features while keeping info with PCA, SVD, and feature importance techniques.
11. Reinforcement Learning: Agents learn decisions via rewards/penalties. Concepts: MDPs, Q-learning, policy gradients.
12. Big Data & Scalable ML: Handle large datasets and scale models using Apache Spark, Hadoop, and distributed training.
13. Model Deployment & Monitoring: Deploy and monitor ML models with tools like TensorFlow Serving, AWS SageMaker, Docker, Flask.
14. Ethics in Machine Learning: Understand bias, fairness, transparency, and accountability to build ethical and accurate models.
15. Bayesian Inference: Update probabilities with new evidence. Learn Bayes’ theorem, prior/posterior distributions, Bayesian networks.
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The TRUTH About SOLID Principles: What Even Senior Devs Get Wrong!
Which SOLID principle matters MOST? Here's my take:
1️⃣ SRP (Single Responsibility Principle): Start here! Each class should have ONE job.
2️⃣ DIP (Dependency Inversion Principle): Make code flexible. Don't depend on details!
These give you instant benefits! Others (OCP & ISP) are for scaling.
❌ But STOP blindly following Liskov! It's about inheritance, and that's often a PROBLEM.
✅ Choose composition instead! Build things from smaller parts.
SOLID's powerful, but don't blindly over-engineer! Simpler code is often BETTER.
Which SOLID principle matters MOST? Here's my take:
1️⃣ SRP (Single Responsibility Principle): Start here! Each class should have ONE job.
2️⃣ DIP (Dependency Inversion Principle): Make code flexible. Don't depend on details!
These give you instant benefits! Others (OCP & ISP) are for scaling.
❌ But STOP blindly following Liskov! It's about inheritance, and that's often a PROBLEM.
✅ Choose composition instead! Build things from smaller parts.
SOLID's powerful, but don't blindly over-engineer! Simpler code is often BETTER.
Frontend Development Interview Questions
Beginner Level
1. What are semantic HTML tags?
2. Difference between id and class in HTML?
3. What is the Box Model in CSS?
4. Difference between margin and padding?
5. What is a responsive web design?
6. What is the use of the <meta viewport> tag?
7. Difference between inline, block, and inline-block elements?
8. What is the difference between == and === in JavaScript?
9. What are arrow functions in JavaScript?
10. What is DOM and how is it used?
Intermediate Level
1. What are pseudo-classes and pseudo-elements in CSS?
2. How do media queries work in responsive design?
3. Difference between relative, absolute, fixed, and sticky positioning?
4. What is the event loop in JavaScript?
5. Explain closures in JavaScript with an example.
6. What are Promises and how do you handle errors with .catch()?
7. What is a higher-order function?
8. What is the difference between localStorage and sessionStorage?
9. How does this keyword work in different contexts?
10. What is JSX in React?
Advanced Level
1. How does the virtual DOM work in React?
2. What are controlled vs uncontrolled components in React?
3. What is useMemo and when should you use it?
4. How do you optimize a large React app for performance?
5. What are React lifecycle methods (class-based) and their hook equivalents?
6. How does Redux work and when should you use it?
7. What is code splitting and why is it useful?
8. How do you secure a frontend app from XSS attacks?
9. Explain the concept of Server-Side Rendering (SSR) vs Client-Side Rendering (CSR).
10. What are Web Components and how do they work?
@coding_interview_preparation
Beginner Level
1. What are semantic HTML tags?
2. Difference between id and class in HTML?
3. What is the Box Model in CSS?
4. Difference between margin and padding?
5. What is a responsive web design?
6. What is the use of the <meta viewport> tag?
7. Difference between inline, block, and inline-block elements?
8. What is the difference between == and === in JavaScript?
9. What are arrow functions in JavaScript?
10. What is DOM and how is it used?
Intermediate Level
1. What are pseudo-classes and pseudo-elements in CSS?
2. How do media queries work in responsive design?
3. Difference between relative, absolute, fixed, and sticky positioning?
4. What is the event loop in JavaScript?
5. Explain closures in JavaScript with an example.
6. What are Promises and how do you handle errors with .catch()?
7. What is a higher-order function?
8. What is the difference between localStorage and sessionStorage?
9. How does this keyword work in different contexts?
10. What is JSX in React?
Advanced Level
1. How does the virtual DOM work in React?
2. What are controlled vs uncontrolled components in React?
3. What is useMemo and when should you use it?
4. How do you optimize a large React app for performance?
5. What are React lifecycle methods (class-based) and their hook equivalents?
6. How does Redux work and when should you use it?
7. What is code splitting and why is it useful?
8. How do you secure a frontend app from XSS attacks?
9. Explain the concept of Server-Side Rendering (SSR) vs Client-Side Rendering (CSR).
10. What are Web Components and how do they work?
@coding_interview_preparation
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