#MachineLearning

Last updated    - V4 (13-Sep-2025)

Level Topic Subtopics Basic Introduction to ML What is Machine Learning, History of ML, Applications, Types of ML (Supervised, Unsupervised, Reinforcement), Difference between AI, ML, and DL Data Preparation & Cleaning Data Collection, Data Cleaning, Handling Missing Values, Feature Engineering, Data Normalization, Data Splitting Supervised Learning Regression, Classification, Linear Regression, Logistic Regression, Decision Trees Evaluation Metrics Accuracy, Precision, Recall, F1 Score, Confusion Matrix, ROC-AUC, Cross-Validation ML Tools & Libraries Python, NumPy, Pandas, Scikit-learn, Matplotlib, Seaborn, Jupyter Notebooks Intermediate Unsupervised Learning Clustering (K-Means, Hierarchical), Dimensionality Reduction (PCA, t-SNE), Association Rules, Anomaly Detection Feature Selection & Engineering Feature Importance, Correlation Analysis, One-Hot Encoding, Scaling, Normalization, Feature Transformation Model Selection & Tuning Hyperparameter Tuning, Grid Search, Random Search, Model Validation, Bias-Variance Tradeoff Ensemble Methods Bagging, Boosting, Random Forest, Gradient Boosting, XGBoost, AdaBoost ML Pipelines & Workflow Pipeline Creation, Data Preprocessing, Model Training, Validation, Deployment, Model Monitoring Advanced Deep Learning Basics Neural Networks, Perceptron, Backpropagation, Activation Functions, Gradient Descent, Loss Functions Convolutional Neural Networks CNN Architecture, Convolution & Pooling Layers, Image Classification, Object Detection, Transfer Learning Recurrent Neural Networks RNN, LSTM, GRU, Sequence Modeling, Time Series Forecasting, Text Generation Optimization & Regularization Learning Rate, Momentum, Adam, RMSProp, Dropout, Batch Normalization, Early Stopping Model Evaluation & Interpretability Confusion Matrix, ROC-AUC, SHAP, LIME, Feature Importance, Error Analysis Expert Advanced ML & DL Techniques GANs, Variational Autoencoders, Reinforcement Learning, Deep Q-Networks, Policy Gradient, Multi-Agent RL Natural Language Processing Tokenization, Word Embeddings, Transformers, BERT, GPT, Sequence-to-Sequence Models Computer Vision Advanced Image Segmentation, Object Detection, Instance Segmentation, Attention Mechanisms, Vision Transformers Model Deployment & MLOps Model Serving, API Creation, Model Monitoring, A/B Testing, CI/CD for ML, Cloud Deployment AI Ethics & Research Bias Mitigation, Explainable AI, Responsible AI, Research Trends, Few-Shot & Zero-Shot Learning

1. ML Basics

1. What is Machine Learning and how does it differ from AI?
2. Explain types of ML: supervised, unsupervised, reinforcement.
3. What are some real-world applications of ML?
4. Difference between AI, ML, and Deep Learning.
5. Explain linear regression.
6. Explain logistic regression.
7. What is overfitting and underfitting?
8. How do you handle missing values in a dataset?
9. Difference between classification and regression.
10. Explain the bias-variance tradeoff.
11. What is cross-validation?
12. Difference between hold-out and k-fold validation.
13. What are confusion matrix, precision, and recall?
14. Explain F1 score and when to use it.
15. What is ROC-AUC and why is it important?
16. How do you handle categorical variables?
17. Difference between feature scaling and normalization.
18. Explain gradient descent.
19. How do you choose an appropriate ML algorithm?
20. Difference between parametric and non-parametric models.
21. Explain the difference between batch, stochastic, and mini-batch gradient descent.
22. What is a learning curve?
23. How do you handle imbalanced datasets?
24. What are some common Python libraries for ML?
25. How do you interpret model coefficients?

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2. Intermediate ML Concepts

1. Explain K-Means clustering.
2. Explain hierarchical clustering.
3. What is Principal Component Analysis (PCA)?
4. Difference between PCA and t-SNE.
5. Explain Random Forest algorithm.
6. What is Bagging and Boosting?
7. Explain Gradient Boosting Machines (GBM).
8. Explain AdaBoost.
9. What is XGBoost?
10. How do you perform hyperparameter tuning?
11. Difference between Grid Search and Random Search.
12. What is feature engineering and why is it important?
13. How do you select important features?
14. Explain correlation analysis.
15. What is One-Hot Encoding?
16. What is feature scaling and why is it important?
17. Explain pipeline creation in ML.
18. How do you validate model performance?
19. What is bias and variance in models?
20. Explain ensemble methods and their advantages.
21. Difference between bagging and boosting in ensemble methods.
22. How do you detect outliers in data?
23. Explain anomaly detection techniques.
24. How do you handle high-dimensional data?
25. How do you prevent data leakage?

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3. Advanced ML & Deep Learning

1. Explain the architecture of a neural network.
2. What is backpropagation?
3. Explain activation functions: ReLU, Sigmoid, Tanh.
4. What is dropout and why is it used?
5. Explain batch normalization.
6. Difference between CNN and RNN.
7. Explain convolutional neural networks (CNN).
8. How do you perform image classification with CNNs?
9. Explain pooling layers: max pooling, average pooling.
10. Explain recurrent neural networks (RNN).
11. What is LSTM and why is it used?
12. Explain GRU (Gated Recurrent Unit).
13. How do you perform time series forecasting with ML?
14. What is sequence-to-sequence modeling?
15. Explain autoencoders and their applications.
16. What is a Variational Autoencoder (VAE)?
17. Explain Generative Adversarial Networks (GANs).
18. How do you prevent overfitting in deep learning models?
19. Explain learning rate and its importance.
20. How do you optimize a neural network?
21. Explain optimizer algorithms: SGD, Adam, RMSProp.
22. How do you perform model interpretability?
23. Explain SHAP and LIME.
24. How do you handle imbalanced classes in deep learning?
25. What are some common pitfalls in training deep learning models?

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4. Expert-Level ML Concepts

1. Explain reinforcement learning and its key components.
2. What is a Markov Decision Process (MDP)?
3. Explain Q-Learning.
4. Explain Policy Gradient methods.
5. Difference between model-based and model-free RL.
6. Explain multi-agent reinforcement learning.
7. What is transfer learning?
8. How do you apply transfer learning in CNNs?
9. What is few-shot learning?
10. What is zero-shot learning?
11. Explain self-supervised learning.
12. Explain contrastive learning.
13. How do you implement retrieval-augmented generation (RAG)?
14. Explain multi-modal learning.
15. How do you handle cross-modal embeddings?
16. Explain explainable AI (XAI) techniques.
17. How do you implement AI ethics in ML systems?
18. What are bias and fairness metrics in ML?
19. How do you deploy ML models to production?
20. Explain MLOps pipelines.
21. How do you monitor model performance in production?
22. Explain CI/CD for ML workflows.
23. How do you handle model drift and data drift?
24. Explain energy-efficient ML techniques.
25. What are the emerging trends in ML research?