13 September 2025

#AI

#AI

Key Concepts


Topic SubTopics Basic Intermediate Advanced Expert
Introduction to AI Definition, History, Applications, AI vs Human Intelligence ✅
AI Types Narrow AI, General AI, Superintelligence, Reactive Machines, Limited Memory ✅ ✅
Machine Learning (ML) Supervised, Unsupervised, Reinforcement Learning, Algorithms ✅ ✅ ✅
Deep Learning Neural Networks, CNN, RNN, LSTM, Transformers ✅ ✅ ✅
Natural Language Processing (NLP) Tokenization, POS Tagging, Named Entity Recognition, Sentiment Analysis ✅ ✅ ✅
Computer Vision Image Classification, Object Detection, Image Segmentation, OCR ✅ ✅ ✅
Robotics & Automation Robot Perception, Path Planning, Control Systems ✅ ✅ ✅ ✅
Knowledge Representation Logic, Ontologies, Semantic Networks, Knowledge Graphs ✅ ✅ ✅
Search & Optimization A* Search, Genetic Algorithms, Simulated Annealing, Hill Climbing ✅ ✅ ✅
Expert Systems Rule-based Systems, Inference Engines, Forward/Backward Chaining ✅ ✅ ✅
AI Planning Classical Planning, Hierarchical Task Networks, Automated Planning ✅ ✅ ✅
Reinforcement Learning (RL) Q-Learning, Policy Gradient, Deep RL, Multi-Agent RL ✅ ✅ ✅
AI in Data Science Predictive Analytics, Classification, Regression, Clustering ✅ ✅ ✅ ✅
Ethics & AI Safety Bias, Fairness, Explainable AI, AI Governance ✅ ✅ ✅ ✅
Generative AI GANs, Variational Autoencoders, Diffusion Models, ChatGPT ✅ ✅ ✅
AI Frameworks & Tools TensorFlow, PyTorch, Keras, Scikit-learn, OpenAI API ✅ ✅ ✅ ✅
AI in Industry Healthcare, Finance, Retail, Autonomous Vehicles ✅ ✅ ✅ ✅
Optimization & Hyperparameter Tuning Grid Search, Random Search, Bayesian Optimization ✅ ✅ ✅
AI Scalability & Deployment Model Serving, Cloud AI, Edge AI, MLOps ✅ ✅ ✅
AI Evaluation Metrics Accuracy, Precision, Recall, F1 Score, ROC-AUC ✅ ✅ ✅ ✅
AI Security Adversarial Attacks, Model Poisoning, Data Privacy ✅ ✅ ✅
Explainable AI (XAI) SHAP, LIME, Counterfactual Explanations ✅ ✅ ✅
Time Series AI Forecasting, Anomaly Detection, ARIMA, LSTM, Prophet ✅ ✅ ✅
Emerging AI AI Alignment, Quantum AI, Self-Supervised Learning, AI Agents ✅ ✅
AI Research & Trends Current Papers, Benchmarks, Open Problems, Large Language Models ✅ ✅

Interview question

🟢 Basic Level

  1. What is Artificial Intelligence?
  2. Difference between AI, Machine Learning, and Deep Learning.
  3. What are the main goals of AI?
  4. What is an intelligent agent?
  5. What is a rational agent?
  6. What is the Turing Test?
  7. What is machine learning?
  8. What are the different types of machine learning?
  9. What is supervised learning?
  10. What is unsupervised learning?
  11. What is reinforcement learning?
  12. What is a dataset in AI?
  13. What is a model in AI?
  14. What is training data?
  15. What is testing data?
  16. What is accuracy in a model?
  17. What are features in AI?
  18. What is a label in supervised learning?
  19. What is overfitting?
  20. What is underfitting?
  21. What is a neural network?
  22. What is an activation function?
  23. What is a decision tree?
  24. What is clustering?
  25. What is a chatbot?

🟡 Intermediate Level

  1. What is a cost function?
  2. What is gradient descent?
  3. What are hyperparameters?
  4. What is regularization in ML?
  5. Difference between L1 and L2 regularization.
  6. What is cross-validation?
  7. What is a confusion matrix?
  8. Explain precision, recall, and F1 score.
  9. What is logistic regression?
  10. What is Naive Bayes algorithm?
  11. What is k-nearest neighbors (KNN)?
  12. What is support vector machine (SVM)?
  13. What is PCA (Principal Component Analysis)?
  14. What is dimensionality reduction?
  15. What is a random forest?
  16. What is bagging?
  17. What is boosting?
  18. What is gradient boosting?
  19. What is XGBoost?
  20. What are embeddings?
  21. What is transfer learning?
  22. What is computer vision?
  23. What is natural language processing (NLP)?
  24. Explain tokenization.
  25. What is time-series prediction?

🔵 Advanced Level

  1. Explain backpropagation algorithm.
  2. What is a convolutional neural network (CNN)?
  3. What are pooling layers?
  4. What is a recurrent neural network (RNN)?
  5. What is LSTM?
  6. What is GRU?
  7. Explain attention mechanism.
  8. What is sequence-to-sequence learning?
  9. What is the vanishing gradient problem?
  10. What is batch normalization?
  11. What is dropout?
  12. What is a GAN (Generative Adversarial Network)?
  13. What are autoencoders?
  14. What is reinforcement learning environment?
  15. Explain policy-based RL.
  16. What is Q-learning?
  17. What is a Markov Decision Process (MDP)?
  18. What is federated learning?
  19. What is edge AI?
  20. What is explainable AI (XAI)?
  21. What is a knowledge graph?
  22. What is constraint satisfaction problem (CSP)?
  23. What is heuristic search?
  24. What is A* search algorithm?
  25. What is adversarial attack in AI?

🔴 Expert Level

  1. Explain transformer architecture in detail.
  2. What are Large Language Models (LLMs)?
  3. What is self-attention?
  4. Explain multi-head attention.
  5. What is prompt engineering?
  6. What is fine-tuning in LLMs?
  7. What is RAG (Retrieval-Augmented Generation)?
  8. What is vector embedding space?
  9. What are diffusion models?
  10. What is zero-shot learning?
  11. What is few-shot learning?
  12. Explain multi-modal AI.
  13. What is AGI (Artificial General Intelligence)?
  14. What is symbolic AI?
  15. What is hybrid AI (Symbolic + Neural)?
  16. Explain knowledge distillation.
  17. What is curriculum learning?
  18. What are RLHF techniques?
  19. Explain safety alignment in AI models.
  20. What is model hallucination?
  21. What is distributed training?
  22. What is parallelism (data/model/pipeline)?
  23. What is AI governance?
  24. How do you deploy large AI models in production?
  25. What is the future of AI (emerging trends & research areas)?

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