Showing posts with label #AI. Show all posts
Showing posts with label #AI. Show all posts

24 June 2025

#Calculus

#Calculus
Calculus
  • Derivatives
  • Integrals
  • Gradient descent

#Statistics

#Statistics
Statistics
  • Descriptive statistics
  • inferential statistics
  • hypothesis testing
  • p-values
  • confidence intervals

#Probability

#Probability
Probability
  • Basic probability
  • Bayes' theorem
  • Probability distributions

#Linear Algebra

#Linear Algebra
What is a scalar?
Define a vector.
What is a matrix?
What is a tensor?
How do you represent a vector geometrically?
What is the difference between a row vector and a column vector?
What is a diagonal matrix?
What is an identity matrix?
What is a zero matrix?
What is a symmetric matrix?
What is a skew-symmetric matrix?
Define orthogonal matrix.
What is a triangular matrix?
What is the trace of a matrix?
What does it mean for a matrix to be invertible?
What is matrix addition?
What is scalar multiplication?
Define matrix multiplication.
When is matrix multiplication possible?
Is matrix multiplication commutative?
What is the associative property of matrix multiplication?
What is the distributive property?
What is the transpose of a matrix?
What are the properties of matrix transpose?
What is the inverse of a matrix?
What are the conditions for a matrix to have an inverse?
What is a singular matrix?
What is an orthogonal matrix?
How do you compute the determinant of a 2x2 matrix?
How do you compute the determinant of a 3x3 matrix?
What is the dot product of two vectors?
What is the cross product?
What does it mean for two vectors to be orthogonal?
What is the angle between two vectors?
What is the projection of one vector onto another?
What is a unit vector?
What is a basis vector?
How do you normalize a vector?
What is vector space?
What is subspace?
What is linear independence?
What is linear dependence?
How do you check if vectors are linearly independent?
What is the span of a set of vectors?
What is the basis of a vector space?
What is the dimension of a vector space?
Give an example of a dependent set of vectors.
What is the rank of a matrix?
How do you compute the rank?
What does full rank mean?
What is the null space (kernel) of a matrix?
How do you find the nullity of a matrix?
What is the column space?
What is the row space?
What is the orthogonal complement?
What is an eigenvalue?
What is an eigenvector?
How do you compute eigenvalues?
What is the characteristic polynomial?
How do you find eigenvectors?
What is geometric multiplicity?
What is algebraic multiplicity?
What are some properties of eigenvalues?
Why are eigenvalues important in machine learning?
What is the spectral theorem?
What is LU decomposition?
What is QR decomposition?
What is SVD (Singular Value Decomposition)?
What is the purpose of matrix decomposition?
What is the Cholesky decomposition?
What is the difference between LU and QR?
What is the Gram-Schmidt process?
How is SVD used in data compression?
What is the Jordan Normal Form?
What is the Schur decomposition?
What is the determinant of a matrix?
What is the physical meaning of a determinant?
How do you find the inverse of a 2x2 matrix?
How do you find the inverse using row reduction?
What is the adjoint of a matrix?
How is the determinant used to check invertibility?
What is a cofactor?
What is the Laplace expansion?
Can a non-square matrix be inverted?
What is a pseudoinverse?
What is the Moore-Penrose pseudoinverse?
What is a block matrix?
What is the Kronecker product?
What is the Hadamard product?
What is matrix exponentiation?
What is a condition number?
What is a rank-deficient matrix?
What is a positive definite matrix?
What is a diagonalizable matrix?
What is matrix similarity?
What is a vector norm?
What is the L1 norm?
What is the L2 norm?
What is the infinity norm?
What is the Frobenius norm?
How are norms used in machine learning?
What is the distance between two vectors?
How is cosine similarity calculated?
What is the relationship between norm and distance?
How does normalization affect vector norms?
What is an orthogonal set of vectors?
What is orthonormality?
How do you orthogonalize a set of vectors?
What is the projection matrix?
How do you project a vector onto a subspace?
What is the geometric interpretation of a projection?
When is a projection matrix idempotent?
What is the Gram matrix?
What is the orthogonal projection theorem?
How is projection used in least squares?
What is the least squares solution?
Why is least squares used in linear regression?
What is the normal equation?
How do you derive the least squares estimator?
What happens if the design matrix is not full rank?
What is the role of the pseudoinverse in least squares?
What is the residual vector?
How do you minimize the residual?
What is the cost function in linear regression?
What is the relationship between projection and least squares?
What is a linear transformation?
What is the matrix representation of a transformation?
What is a standard basis?
What is a change of basis?
How do you change a vector to a new basis?
What is a transition matrix?
How are linear transformations represented in different bases?
What is the role of similarity transformations?
What is the canonical form?
What is the matrix of a reflection or rotation?
What is a symmetric matrix?
What is a skew-symmetric matrix?
What is a positive definite matrix?
What is a positive semi-definite matrix?
How do you test for positive definiteness?
What are the properties of symmetric matrices?
What are applications of positive definite matrices in ML?
What is an idempotent matrix?
What is a nilpotent matrix?
What is the Cayley-Hamilton Theorem?
What is Gaussian elimination?
What is Gauss-Jordan elimination?
What is row echelon form?
What is reduced row echelon form?
What is pivoting?
What are leading and free variables?
What is backward substitution?
What is forward substitution?
What is the computational complexity of matrix multiplication?
What is sparse matrix representation?
What is SVD used for in NLP?
How is PCA related to eigenvectors?
What is the Eckart?Young theorem?
What is the thin SVD?
What is truncated SVD?
What is the application of QR decomposition in ML?
How does Cholesky compare to LU?
What is the Householder transformation?
What is the Givens rotation?
What is the role of decomposition in solving linear systems?
What is a dual space?
What is a linear functional?
What is the relationship between dual basis and basis?
How are linear maps between duals represented?
What is reflexivity in linear algebra?
How are eigenvectors used in spectral clustering?
What is Laplacian matrix in graph theory?
What are principal components?
How is PCA implemented using eigen decomposition?
How does dimensionality reduction work in PCA?
What is a vector subspace?
What is the annihilator of a subspace?
What is the quotient space?
What is the rank-nullity theorem?
What is a bilinear form?
What is a quadratic form?
How do you diagonalize a quadratic form?
What is matrix congruence?
What is orthogonal diagonalization?
What is a linear operator?
Why is linear algebra essential in machine learning?
How is linear algebra used in neural networks?
What is the role of dot product in attention mechanisms?
How are matrices used in image processing?
What is the Jacobian matrix in deep learning?
What is the Hessian matrix?
How is SVD used in recommendation systems?
How are tensors used in deep learning?
What is the shape of input data for neural networks?
How does dimensionality reduction improve performance?
What is a tensor?
What is a rank of a tensor?
How are tensors represented?
What are tensor contractions?
What is tensor decomposition?
Can a matrix have more than one inverse?
Can a non-square matrix be orthogonal?
Can a set of linearly independent vectors form a basis?
Is every orthonormal set linearly independent?
Can two vectors be orthogonal but not linearly independent?
What is the geometric meaning of a determinant?
How is the rank related to dimensionality?
How do transformations affect shapes and dimensions?
What is shearing?
What is scaling in linear transformations?
Difference between rank and dimension?
Compare dot product and cross product.
Difference between linear map and affine map?
Compare eigen decomposition and SVD.
Difference between orthogonal and orthonormal?
True or False: All orthogonal matrices are invertible.
True or False: A matrix with zero determinant is invertible.
True or False: Eigenvalues can be complex.
True or False: A matrix can be diagonalized only if it's square.
True or False: The transpose of a symmetric matrix is symmetric.
Describe how a matrix transforms a vector.
Explain the intuition behind the dot product.
Describe how matrix rank affects system solutions.
Explain the steps to solve a linear system using Gaussian elimination.
Describe why PCA reduces noise.
How do you compute matrix inverse in Python (NumPy)?
How do you compute eigenvalues in NumPy?
How do you use SVD in scikit-learn?
How do you create a projection matrix using NumPy?
How do you find the rank of a matrix programmatically?
Given 3 vectors, check if they are linearly independent.
Find the eigenvalues of a 2x2 matrix manually.
Solve a 3x3 linear system using matrix inversion.
Given a matrix, compute its rank.
Reduce a matrix to row echelon form.
What is the determinant of an identity matrix?
What is the transpose of a diagonal matrix?
What is the rank of a zero matrix?
What is the nullity of an invertible matrix?
What is the dimension of R³?
Why are orthogonal matrices preferred in numerical computations?
Why is SVD preferred over eigen decomposition in some cases?
How is linear algebra related to convolution in CNNs?
How do singular matrices affect training in ML models?
What matrix operations are most common in backpropagation?
What is a Vandermonde matrix?
What is the companion matrix?
What is the Toeplitz matrix?
What is a circulant matrix?
What are real-world examples of linear algebra in AI?
  • Scalars, Vectors, Matrices, and Tensors
  • Matrix Operations
  • Dot Product (Inner Product)
  • Matrix Multiplication
  • Transpose of a Matrix
  • Identity Matrix (I)
  • Inverse Matrix
  • Determinant
  • Rank of a Matrix
  • Linear Independence
  • Orthogonality
  • Eigenvalues and Eigenvectors
  • Singular Value Decomposition (SVD)
  • Norms (Vector Magnitude)
  • Projection
  • Row Space and Column Space
  • Null Space (Kernel)
  • Basis and Dimension
  • Diagonalization of Matrices
  • Trace of a Matrix
  • Symmetric Matrices
  • Skew-Symmetric Matrices
  • Orthogonal Matrices
  • Positive Definite Matrices
  • LU Decomposition
  • QR Decomposition
  • Cholesky Decomposition
  • Rank-Deficient Matrices
  • Gram-Schmidt Process
  • Moore-Penrose Pseudoinverse
  • Condition Number of a Matrix
  • Matrix Norms
  • Block Matrices
  • Sparse Matrices
  • Band Matrices
  • Triangular Matrices (Upper and Lower)
  • Permutation Matrices
  • Idempotent Matrices
  • Nilpotent Matrices
  • Jordan Normal Form
  • Cayley-Hamilton Theorem
  • Schur Decomposition
  • Householder Transformation
  • Givens Rotation
  • Rank-One Update
  • Outer Product of Vectors
  • Kronecker Product
  • Hadamard Product
  • Matrix Exponential

#Core_AI

#Core_AI
What is Artificial Intelligence?
What are the different types of AI (Weak, Strong, General, Narrow)?
Define the Turing Test. Why is it important?
Explain the difference between AI, ML, and Deep Learning.
What are the key components of an intelligent system?
What is the difference between supervised, unsupervised, and reinforcement learning?
What are the main goals of AI?
What are intelligent agents in AI?
What are the characteristics of an intelligent agent?
What is the difference between deterministic and stochastic environments?
What is a state space in AI?
Explain BFS and DFS with examples.
What is heuristic search? Give examples.
What is A* algorithm and how does it work?
What is the difference between informed and uninformed search?
What is the hill-climbing algorithm?
What is adversarial search?
How does Minimax algorithm work?
What is alpha-beta pruning?
What is the role of constraint satisfaction in AI?
What is knowledge representation?
What are the types of knowledge in AI?
What is propositional logic?
What is first-order logic?
What is semantic network?
What is an ontology in AI?
What is fuzzy logic and how is it used?
What is a knowledge base?
How does forward chaining differ from backward chaining?
What is non-monotonic reasoning?
What is the difference between classification and regression?
What is overfitting and underfitting?
What is cross-validation?
Explain bias-variance trade-off.
What is a confusion matrix?
What is entropy in decision trees?
What is the role of cost function?
What is gradient descent?
What are the different types of distance metrics?
What is feature selection and dimensionality reduction?
What is planning in AI?
What is STRIPS in planning?
What is reinforcement learning? Give examples.
Explain Q-learning.
What is the Markov Decision Process (MDP)?
What is temporal difference learning?
What is policy vs value function in reinforcement learning?
What is model-free vs model-based RL?
Explain exploration vs exploitation dilemma.
What are eligibility traces?
What is NLP?
What is tokenization in NLP?
What is stemming vs lemmatization?
What is Part-of-Speech (POS) tagging?
What are n-grams?
What is Named Entity Recognition (NER)?
What is the bag-of-words model?
What is TF-IDF?
What is sentiment analysis?
What is language modeling?
What is a perceptron?
What is the difference between a single-layer and multi-layer perceptron?
What is backpropagation?
What is an activation function? Name a few.
What are convolutional neural networks (CNNs)?
What are recurrent neural networks (RNNs)?
What is the vanishing gradient problem?
What is dropout in neural networks?
What is batch normalization?
What are hyperparameters in neural networks?
What is the role of AI in robotics?
What are sensors and effectors in robotics?
What is SLAM (Simultaneous Localization and Mapping)?
What is path planning in robotics?
What is computer vision and how does it relate to AI?
What are the ethical concerns in AI?
What is explainable AI (XAI)?
What is AI bias and how can it be mitigated?
What is AGI (Artificial General Intelligence)?
What is the future of AI in society?
  • Retrieval-Augmented Generation (RAG)
  • Supervised Learning & Unsupervised Learning
  • Reinforcement Learning
  • Semi-supervised Learning
  • Self-supervised Learning
  • Few-shot & Zero-shot Learning
  • Federated Learning & Contrastive Learning
  • Feature Engineering
  • Bias-Variance Tradeoff
  • Model Evaluation Metrics (Accuracy, Precision, Recall, F1 Score)
  • Cross-Validation
  • Transformer Variants
  • Overfitting and Underfitting
  • Regularization (L1, L2)
  • Hyperparameter Tuning
  • Gradient Descent Optimization
  • Training vs Testing vs Validation Sets
  • Data Leakage and Prevention Techniques
   Large Language Models (LLMs)       Multimodal AI       Agentic AI       AI Agents       Edge AI       AutoML       Explainable AI (XAI)   
   TinyML   

#Mathematics_for_AI

#Mathematics for AI
  • Linear Algebra
  • Probability and Statistics
  • Calculus
  • Optimization
  • Distance and Similarity Measures
  • Information Theory
  • Sampling and Estimation Techniques
  • Markov Chains and Probabilistic Models
  • Matrix Factorization Techniques
  • Numerical Methods in AI
   Linear Algebra       Probability       Statistics       Calculus   

12 November 2024

#Semi_Supervised_Learning

#Semi_Supervised_Learning
Question Option A Option B Option C Option D

#Unsupervised Learning

#Unsupervised_Learning
Unsupervised Learning
  • Trained on unlabeled data (data without known outputs)
  • Tries to find patterns and relationships in the data on its own
  • Used for clustering tasks (e.g., grouping customers together based on their purchase history) and dimensionality reduction tasks (e.g., reducing the number of features in a dataset without losing too much information)
Question Option A Option B Option C Option D

#Supervised_Learning

#Supervised_Learning
Supervised Learning
  • Trained on labeled data (data with known outputs)
  • Learns to predict the output for new data based on patterns learned from the training data
  • Used for classification (e.g., predicting whether an email is spam or not) and regression tasks (e.g., predicting the price of a house)
Question Option A Option B Option C Option D

26 August 2024

#Generative_AI

#Generative_AI
Generative_AI
Question Option A Option B Option C Option D

17 August 2024

#Neural_Network

#Neural_Network
Neural_Network
Question Option A Option B Option C Option D

01 January 2021

#Deep_Learning

#Deep Learning
Level Topic Subtopics
Basic Introduction to Deep Learning What is Deep Learning, Difference between ML and DL, History of DL, Applications, Types of Neural Networks
Neural Network Fundamentals Perceptron, Neurons, Layers, Activation Functions (Sigmoid, ReLU, Tanh), Forward Propagation
Loss Functions & Optimization Mean Squared Error, Cross-Entropy, Gradient Descent, Learning Rate, Optimizers Overview
Tools & Frameworks Python, TensorFlow, Keras, PyTorch, Jupyter Notebook, Google Colab
Ethics & Safety Bias in DL models, Responsible AI, Model Interpretability, Privacy Concerns, Fairness
Intermediate Feedforward & Convolutional Networks Multi-Layer Perceptron (MLP), Forward & Backpropagation, CNN Architecture, Convolution & Pooling, Image Classification
Recurrent Neural Networks RNN, LSTM, GRU, Sequence Modeling, Time Series Forecasting, Text Generation
Regularization Techniques Dropout, L2/L1 Regularization, Batch Normalization, Early Stopping, Data Augmentation
Model Evaluation Confusion Matrix, Accuracy, Precision, Recall, F1 Score, ROC-AUC, Loss Curves
Transfer Learning Pretrained Models, Feature Extraction, Fine-Tuning, Applications in CV & NLP
Advanced Advanced Architectures GANs, Variational Autoencoders (VAE), Attention Mechanism, Transformers, Residual Networks (ResNet)
Natural Language Processing Tokenization, Embeddings, Word2Vec, GloVe, BERT, GPT, Sequence-to-Sequence Models
Computer Vision Advanced Object Detection, Image Segmentation, Instance Segmentation, Attention in CV, Vision Transformers
Optimization & Training Advanced Optimizers (Adam, RMSProp), Learning Rate Scheduling, Gradient Clipping, Mixed Precision Training
Multi-Modal Learning Text-to-Image, Text-to-Audio, Cross-Modal Representations, Multi-Modal Transformers, Fusion Techniques
Expert Reinforcement Learning Markov Decision Processes, Q-Learning, Policy Gradient Methods, Actor-Critic, Multi-Agent RL
Generative Deep Learning Advanced GANs (StyleGAN, CycleGAN, BigGAN), Diffusion Models, Generative Transformers, Latent Space Manipulation
Explainable & Interpretable DL SHAP, LIME, Counterfactual Analysis, Attention Visualization, Understanding Latent Representations
Model Deployment & MLOps Serving Models, APIs, Cloud Deployment, Model Monitoring, CI/CD for DL, Model Versioning
Research & Emerging Trends Self-Supervised Learning, Few-Shot & Zero-Shot Learning, Foundation Models, AI Alignment, Responsible Deployment

1. Deep Learning Basics

  1. What is Deep Learning?
  2. Difference between Machine Learning and Deep Learning.
  3. What are artificial neural networks (ANN)?
  4. Explain the perceptron model.
  5. What are neurons and layers in neural networks?
  6. Explain activation functions: Sigmoid, ReLU, Tanh.
  7. What is forward propagation?
  8. What is backpropagation?
  9. Explain the concept of loss functions.
  10. What are common loss functions: MSE, Cross-Entropy?
  11. Explain gradient descent.
  12. What is learning rate and its importance?
  13. What are optimizers in deep learning?
  14. Difference between batch, stochastic, and mini-batch gradient descent.
  15. What is overfitting in deep learning?
  16. What is underfitting in deep learning?
  17. How do you prevent overfitting?
  18. Explain dropout regularization.
  19. What is batch normalization?
  20. What is data augmentation?
  21. Explain train-validation-test split.
  22. What are epochs and iterations?
  23. What are common datasets for deep learning?
  24. Explain supervised vs unsupervised deep learning.
  25. What are practical applications of deep learning?

2. Feedforward & Convolutional Networks

  1. What is a feedforward neural network (FNN)?
  2. Difference between FNN and RNN.
  3. Explain convolutional neural networks (CNN).
  4. What is a convolution layer?
  5. Explain pooling layers: max pooling, average pooling.
  6. How do you apply padding and stride in CNN?
  7. Explain fully connected layers in CNN.
  8. How do CNNs work for image classification?
  9. Explain feature maps in CNN.
  10. What is transfer learning?
  11. Difference between feature extraction and fine-tuning.
  12. Explain common CNN architectures: LeNet, AlexNet, VGG, ResNet.
  13. What is residual connection in ResNet?
  14. How do you prevent overfitting in CNNs?
  15. Explain regularization techniques in CNNs.
  16. What are common CNN applications?
  17. Explain object detection using CNNs.
  18. Explain image segmentation using CNNs.
  19. What is instance segmentation?
  20. How do you implement CNN in TensorFlow/PyTorch?
  21. What is activation map visualization?
  22. How do you handle class imbalance in image datasets?
  23. Explain image augmentation techniques.
  24. How do you optimize CNN training?
  25. Explain evaluation metrics for image classification.

3. Recurrent & Sequence Models

  1. What are recurrent neural networks (RNN)?
  2. Difference between RNN and CNN.
  3. Explain vanishing and exploding gradient problem in RNN.
  4. What is Long Short-Term Memory (LSTM)?
  5. Explain Gated Recurrent Unit (GRU).
  6. Difference between LSTM and GRU.
  7. What are sequence-to-sequence models?
  8. Explain attention mechanism.
  9. What is encoder-decoder architecture?
  10. Explain machine translation using RNNs.
  11. How do you perform text summarization?
  12. How do you perform sentiment analysis with RNNs?
  13. Explain time series forecasting using RNNs.
  14. How do you handle long sequences in RNN?
  15. How do you prevent overfitting in RNNs?
  16. Explain teacher forcing in sequence models.
  17. What are bidirectional RNNs?
  18. How do you implement attention in RNNs?
  19. Explain evaluation metrics for sequence models.
  20. Difference between sequence classification and sequence generation.
  21. How do you visualize RNN outputs?
  22. Explain embedding layers in sequence models.
  23. How do you handle variable-length sequences?
  24. Explain hierarchical RNNs.
  25. How do you optimize RNN training performance?

4. Advanced Deep Learning

  1. Explain Generative Adversarial Networks (GANs).
  2. What are the components of GAN: generator and discriminator?
  3. Explain Variational Autoencoders (VAE).
  4. What is latent space in VAEs?
  5. Explain Transformer architecture.
  6. Difference between Transformer and RNN.
  7. Explain self-attention mechanism.
  8. What is multi-head attention?
  9. Explain positional encoding in Transformers.
  10. Explain BERT architecture.
  11. Explain GPT architecture.
  12. Difference between encoder, decoder, and encoder-decoder models.
  13. Explain fine-tuning pre-trained Transformer models.
  14. What are foundation models?
  15. Explain sequence-to-sequence modeling using Transformers.
  16. Explain cross-modal learning (text-to-image, text-to-audio).
  17. How do you implement transfer learning in Transformers?
  18. Explain reinforcement learning in deep learning.
  19. How do you perform hyperparameter tuning for deep learning models?
  20. Explain mixed precision training.
  21. How do you handle memory optimization in large models?
  22. Explain explainable AI techniques in deep learning.
  23. How do you monitor deep learning model performance?
  24. Explain deployment strategies for deep learning models.
  25. What are emerging trends in deep learning research?

17 November 2020

#MachineLearning

#Machine Learning
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?

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?

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?

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?

Most views on this month

Popular Posts