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.
