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Mastering ML & AI Interviews - 375+ Questions Across All Core Topics
Skip the Buzzwords. Master What Actually Gets You Hired.
Instructor: Ashish Singh
Language: English
Validity Period: 365 days
₹1999 50% OFF
₹999
Course Overview
Machine Learning and Artificial Intelligence are more than just buzzwords—they're core skills powering the future of technology. But to land a top role in this space, you need more than just the ability to train a model. You need to design systems, explain decisions, understand trade-offs, deploy at scale, and communicate like a bar-raiser.
ML & AI Interview Mastery is your complete interview prep course for crushing machine learning and AI technical interviews. This is more than a theory course—this is your hands-on guide to preparing for real-world ML/AI questions asked by top tech companies, startups, and research labs.
You'll dive deep into everything from ML fundamentals and deep learning architectures to system design, fairness, and operationalization. Whether you're targeting Big Tech or a fast-moving AI startup, this course gives you the clarity, confidence, and competitive edge to ace your interviews.
Who This Course Is For
- Aspiring ML Engineers and Data Scientists preparing for technical interviews
- Software Engineers pivoting into AI/ML roles
- Researchers seeking industry jobs
- University students targeting ML internships
- Professionals upskilling to land roles at Great Companies
Why This Course is Different
We don’t just cover definitions—we prepare you to speak about AI systems, structure your thinking, and handle real interview challenges. You’ll get frameworks to answer questions clearly, code examples to explain concepts, and breakdowns of what top interviewers look for.
This is not another tutorial. This is a strategic playbook.
You’ll learn to:
- Explain model design choices with clarity
- Break down ML system design in a structured way
- Think aloud in interviews and communicate trade-offs
- Articulate ethical and fairness considerations in AI
What You'll Learn (Course Modules)
ML Design Patterns
Understand reusable patterns like data versioning, training-serving skew prevention, pipeline reuse, and continuous training. Learn how to talk like a systems thinker.
Machine Learning Concepts
Brush up on bias-variance trade-off, regularization, overfitting, loss functions, optimization, and hyperparameter tuning.
Recommendation & Reinforcement Learning
Cover collaborative filtering, embeddings, matrix factorization, multi-armed bandits, Q-learning, and personalization algorithms.
Clustering & Dimensionality Reduction
Dive into K-Means, DBSCAN, PCA, t-SNE, and UMAP. Learn how to explain unsupervised learning in both theoretical and practical contexts.
Anomaly Detection
Learn both statistical and ML-based approaches like Isolation Forests and Autoencoders. Useful for fraud detection and monitoring roles.
Deep Learning Core Concepts
Understand neural network building blocks: layers, activation functions, backpropagation, optimization (Adam, SGD), vanishing gradients, and weight initialization.
Deep Learning Architectures
Master CNNs, RNNs, LSTMs, GRUs, Transformers, ResNets, and UNet. Learn how to explain them and when to use each.
Computer Vision & NLP
Prepare for real-world questions in image classification, object detection, and NLP topics like tokenization, attention, BERT, and embeddings.
ML Operationalization & Evaluation
Precision, recall, F1, AUC, RMSE—learn how to use them and when. Understand bootstrapping, confidence intervals, calibration, and model deployment pipelines.
Explainability, Fairness & Ethics
You’ll explore the foundations of Explainable AI (XAI), learn the difference between interpretable and black-box models, and gain fluency in leading XAI techniques like SHAP, LIME, and feature importance.
What You’ll Get
- 375+ deeply curated, interview-relevant lessons
- Concept explanations with examples
- Real interview questions with structured answers
- ML system design breakdowns
Join This Course If You Want To:
- Stop memorizing algorithms and start understanding trade-offs
- Prepare for ML interviews with a structure and a strategy
- Gain vocabulary and frameworks to talk like a senior engineer
- Position yourself for top AI roles in a competitive job market
Final Word
AI interviews are tough—but they’re beatable with the right preparation. This course helps you develop not just technical competence, but also the ability to communicate your knowledge with confidence. You’ll walk away interview-ready, future-ready, and more aligned with what top AI teams expect.
So if you’re tired of generic prep and want a course that actually moves the needle—this is for you.
Let’s turn your knowledge into offers. Let’s master the interview.
| ML Design Patterns | |||
| Basics of ML Design Patterns | |||
| What are Machine Learning Design Patterns? | |||
| Can you explain the concept of the ‘Baseline’ design pattern? | |||
| Describe the ‘Feature Store’ design pattern and its advantages. | |||
| How does the ‘Pipelines’ design pattern help in structuring ML workflows? | |||
| Discuss the purpose of the ‘Replay’ design pattern in machine learning. | |||
| Explain the ‘Model Ensemble’ design pattern and when you would use it. | |||
| Describe the ‘Checkpoint’ design pattern in the context of machine learning training. | |||
| What is the ‘Batch Serving’ design pattern and where is it applied? | |||
| Explain the ‘Transformation’ design pattern and its significance in data preprocessing. | |||
| How does the ‘Regularization’ design pattern help in preventing overfitting? | |||
| Machine Learning Concepts | |||
| Bias and Variance | |||
| What do you understand by the terms bias and variance in machine learning? | |||
| How do bias and variance contribute to the overall error in a predictive model? | |||
| Can you explain the difference between a high-bias model and a high-variance model? | |||
| What is the bias-variance trade-off? | |||
| Why is it impossible to simultaneously minimize both bias and variance? | |||
| How does model complexity relate to bias and variance? | |||
| What could be the potential causes of high variance in a model? | |||
| What might be the reasons behind a model’s high bias? | |||
| How would you diagnose bias and variance issues using learning curves? | |||
| What is the expected test error, and how does it relate to bias and variance? | |||
| How do you use cross-validation to estimate bias and variance? | |||
| What techniques are used to reduce bias in machine learning models? | |||
| Can you list some methods to lower variance in a model without increasing bias? | |||
| What is regularization, and how does it help with bias and variance? | |||
| Describe how boosting helps to reduce bias. | |||
| How does bagging help to reduce variance? | |||
| In what ways can feature selection impact bias and variance? | |||
| How does increasing the size of the training set affect bias and variance? | |||
| How would you balance bias and variance while developing models? | |||
| Can you discuss some strategies to overcome underfitting and overfitting? | |||
| Cost Function | |||
| What is a cost function in machine learning? | |||
| How does a cost function differ from a loss function? | |||
| Explain the purpose of a cost function in the context of model training. | |||
| What are the characteristics of a good cost function? | |||
| Differentiate between convex and non-convex cost functions. | |||
| Why is convexity important in cost functions? | |||
| What is the significance of the global minimum in a cost function? | |||
| How does the choice of cost function affect the generalization of a model? | |||
| Describe the Mean Squared Error (MSE) cost function and when to use it. | |||
| Explain the Cross-Entropy cost function and its applications. | |||
| What is the Hinge loss, and in which scenarios is it applied? | |||
| How is the Log Loss function used in logistic regression? | |||
| Discuss the role of the Huber loss and where it is preferable over MSE. | |||
| What is the 0-1 loss function, and why is it often impractical? | |||
| Explain the concept of Regularization in cost functions. | |||
| Curse of Dimensionality | |||
| What is meant by the “Curse of Dimensionality” in the context of Machine Learning? | |||
| Explain how the Curse of Dimensionality affects distance measurements in high-dimensional spaces. | |||
| What are some common problems encountered in high-dimensional data analysis? | |||
| Discuss the concept of sparsity in relation to the Curse of Dimensionality. | |||
| How does the Curse of Dimensionality impact the training of machine learning models? | |||
| Can you provide a simple example illustrating the Curse of Dimensionality using the volume of a hypercube? | |||
| What role does feature selection play in mitigating the Curse of Dimensionality? | |||
| How does the curse of dimensionality affect the performance of K-nearest neighbors (KNN) algorithm? | |||
| Explain how dimensionality reduction techniques help to overcome the Curse of Dimensionality. | |||
| What is Principal Component Analysis (PCA) and how does it address high dimensionality? | |||
| Discuss the differences between feature extraction and feature selection in the context of high-dimensional data. | |||
| Briefly describe the idea behind t-Distributed Stochastic Neighbor Embedding (t-SNE) and its application to high-dimensional data. | |||
| Can Random Forests effectively handle high-dimensional data without overfitting? | |||
| How does regularization help in dealing with the Curse of Dimensionality? | |||
| Ensemble Learning | |||
| What is ensemble learning in machine learning? | |||
| Can you explain the difference between bagging, boosting, and stacking? | |||
| Describe what a weak learner is and how it’s used in ensemble methods. | |||
| What are some advantages of using ensemble learning methods over single models? | |||
| How does ensemble learning help with the variance and bias trade-off? | |||
| What is a bootstrap sample and how is it used in bagging? | |||
| Explain the main idea behind the Random Forest algorithm. | |||
| How does the boosting technique improve weak learners? | |||
| What is model stacking and how do you select base learners for it? | |||
| How can ensemble learning be used for both classification and regression tasks? | |||
| Describe the AdaBoost algorithm and its process. | |||
| How does Gradient Boosting work and what makes it different from AdaBoost? | |||
| Explain XGBoost and its advantages over other boosting methods. | |||
| Discuss the principle behind the LightGBM algorithm. | |||
| How does the CatBoost algorithm handle categorical features differently from other boosting algorithms? | |||
| What is the concept of feature bagging and how does it relate to Random Forests? | |||
| Describe the voting classifier and when it should be used. | |||
| Explain the concept of homogeneous and heterogeneous ensembles. | |||
| What is the out-of-bag error in a Random Forest and how is it useful? | |||
| How does the ensemble diversity affect the performance of an ensemble model? | |||
| Basics of Optimization | |||
| What is optimization in the context of machine learning? | |||
| Can you explain the difference between a loss function and an objective function? | |||
| What is the role of gradients in optimization? | |||
| Why is convexity important in optimization problems? | |||
| Distinguish between local minima and global minima. | |||
| What is a hyperparameter, and how does it relate to the optimization process? | |||
| Explain the concept of a learning rate. | |||
| Discuss the trade-off between bias and variance in model optimization. | |||
| What is Gradient Descent, and how does it work? | |||
| Explain Stochastic Gradient Descent (SGD) and its benefits over standard Gradient Descent. | |||
| Describe the Momentum method in optimization. | |||
| What is the role of second-order methods in optimization, and how do they differ from first-order methods? | |||
| How does the AdaGrad algorithm work, and what problem does it address? | |||
| Can you explain the concept of RMSprop? | |||
| Discuss the Adam optimization algorithm and its key features. | |||
| When would you choose to use a conjugate gradient method? | |||
| Recommendation & Reinforcement Learning | |||
| Basics of Recommendation Systems | |||
| What is a recommendation system and how does it work? | |||
| Can you explain the difference between collaborative filtering and content-based recommendations? | |||
| What are the main challenges in building recommendation systems? | |||
| How do cold start problems impact recommendation systems and how can they be mitigated? | |||
| Discuss the importance of serendipity, novelty, and diversity in recommendation systems. | |||
| How do matrix factorization techniques work in recommendation engines? | |||
| What are the roles of user profiles and item profiles in a recommendation system? | |||
| Describe the concept of implicit versus explicit feedback in the context of recommendation systems. | |||
| Basics of Reinforcement Learning | |||
| What is reinforcement learning, and how does it differ from supervised and unsupervised learning? | |||
| Define the terms: agent, environment, state, action, and reward in the context of reinforcement learning. | |||
| Can you explain the concept of the Markov Decision Process (MDP) in reinforcement learning? | |||
| What is the role of a policy in reinforcement learning? | |||
| What are value functions and how do they relate to reinforcement learning policies? | |||
| Describe the difference between on-policy and off-policy learning. | |||
| What is the exploration vs. exploitation trade-off in reinforcement learning? | |||
| What are the Bellman equations, and how are they used in reinforcement learning? | |||
| Basics of Q-learning | |||
| What is Q-learning, and how does it fit in the field of reinforcement learning? | |||
| Can you describe the concept of the Q-table in Q-learning? | |||
| How does Q-learning differ from other types of reinforcement learning such as policy gradient methods? | |||
| Explain what is meant by the term ‘action-value function’ in the context of Q-learning. | |||
| Describe the role of the learning rate (α) and discount factor (γ) in the Q-learning algorithm. | |||
| What is the exploration-exploitation trade-off in Q-learning, and how is it typically handled? | |||
| Define what an episode is in the context of Q-learning. | |||
| Discuss the concept of state and action space in Q-learning. | |||
| Clustering & Dimensionality Reduction | |||
| Basics of Cluster Analysis | |||
| What is cluster analysis in the context of machine learning? | |||
| Can you explain the difference between supervised and unsupervised learning with respect to cluster analysis? | |||
| What are some common use cases for cluster analysis? | |||
| How does cluster analysis help in data segmentation? | |||
| What are the main challenges associated with clustering high-dimensional data? | |||
| Discuss the importance of scaling and normalization in cluster analysis. | |||
| How would you determine the number of clusters in a dataset? | |||
| What is the silhouette coefficient, and how is it used in assessing clustering performance? | |||
| Explain the difference between hard and soft clustering. | |||
| Can you describe the K-means clustering algorithm and its limitations? | |||
| How does hierarchical clustering differ from K-means? | |||
| What is the role of the distance metric in clustering, and how do different metrics affect the result? | |||
| Explain the basic idea behind DBSCAN (Density-Based Spatial Clustering of Applications with Noise). | |||
| How does the Mean Shift algorithm work, and in what situations would you use it? | |||
| Discuss the Expectation-Maximization (EM) algorithm and its application in clustering. | |||
| How do Gaussian Mixture Models (GMM) contribute to cluster analysis? | |||
| Basics of PCA (Principal Component Analysis) | |||
| What is Principal Component Analysis (PCA)? | |||
| How is PCA used for dimensionality reduction? | |||
| Can you explain the concept of eigenvalues and eigenvectors in PCA? | |||
| Describe the role of the covariance matrix in PCA. | |||
| What is the variance explained by a principal component? | |||
| How does scaling of features affect PCA? | |||
| What is the difference between PCA and Factor Analysis? | |||
| Why is PCA considered an unsupervised technique? | |||
| Can you explain the Singular Value Decomposition (SVD) and its relationship with PCA? | |||
| How do you determine the number of principal components to use? | |||
| What is meant by ‘loading’ in the context of PCA? | |||
| Explain the process of eigenvalue decomposition in PCA. | |||
| Discuss the importance of the trace of a matrix in the context of PCA. | |||
| What are the limitations of PCA when it comes to handling non-linear relationships? | |||
| Provide examples of how PCA can be used in image processing. | |||
| Basics of Dimensionality Reduction | |||
| Can you define dimensionality reduction and explain its importance in machine learning? | |||
| What are the potential issues caused by high-dimensional data? | |||
| Explain the concept of the “curse of dimensionality.” | |||
| How can dimensionality reduction prevent overfitting? | |||
| What is feature selection, and how is it different from feature extraction? | |||
| When would you use dimensionality reduction in the machine learning pipeline? | |||
| Discuss the difference between linear and nonlinear dimensionality reduction techniques. | |||
| Can dimensionality reduction be reversed? Why or why not? | |||
| Anomaly Detection | |||
| Anomaly Detection Basics | |||
| What is Anomaly Detection? | |||
| What are the main types of anomalies in data? | |||
| How does anomaly detection differ from noise removal? | |||
| Explain the concepts of outliers and their impact on dataset. | |||
| What is the difference between supervised and unsupervised anomaly detection? | |||
| What are some real world applications of anomaly detection? | |||
| What is the role of statistics in anomaly detection? | |||
| How do you handle high-dimensional data in anomaly detection? | |||
| Algorithm Understanding and Application | |||
| What are some common statistical methods for anomaly detection? | |||
| Explain the working principle of k-Nearest Neighbors (k-NN) in anomaly detection. | |||
| Deep Learning Core Concepts | |||
| Basics of Deep Learning | |||
| Define deep learning and how it differs from other machine learning approaches. | |||
| What is an artificial neural network? | |||
| Explain the concept of ‘depth’ in deep learning. | |||
| What are activation functions, and why are they necessary? | |||
| Describe the role of weights and biases in neural networks. | |||
| What is the vanishing gradient problem, and how can it be avoided? | |||
| Explain the difference between shallow and deep neural networks. | |||
| What is the universal approximation theorem? | |||
| How do dropout layers help prevent overfitting? | |||
| What is forward propagation and backpropagation? | |||
| What is a Convolutional Neural Network (CNN), and when would you use it? | |||
| Explain Recurrent Neural Networks (RNNs) and their use cases. | |||
| Discuss the architecture and applications of Long Short-Term Memory networks (LSTMs). | |||
| What is the significance of Residual Networks (ResNets)? | |||
| How does a Transformer architecture function, and in what context is it typically used? | |||
| Differentiate between a standard neural network and an Autoencoder. | |||
| What are Generative Adversarial Networks (GANs), and what are their applications? | |||
| Describe how U-Net architecture works for image segmentation tasks. | |||
| Explain the concept of attention mechanisms in deep learning. | |||
| What is a Siamese Neural Network? | |||
| Basics of Autoencoders | |||
| What is an autoencoder? | |||
| Explain the architecture of a basic autoencoder. | |||
| What is the difference between an encoder and a decoder? | |||
| How do autoencoders perform dimensionality reduction? | |||
| What are some key applications of autoencoders? | |||
| Describe the difference between a traditional autoencoder and a variational autoencoder (VAE). | |||
| What is meant by the latent space in the context of autoencoders? | |||
| How can autoencoders be used for unsupervised learning? | |||
| Explain the concept of a sparse autoencoder. | |||
| What is a denoising autoencoder and how does it work? | |||
| Describe how a contractive autoencoder operates and its benefits. | |||
| What are convolutional autoencoders and in what cases are they preferred? | |||
| How do recurrent autoencoders differ from feedforward autoencoders, and when might they be useful? | |||
| Explain the idea behind stacked autoencoders. | |||
| Discuss the role of regularization in training autoencoders. | |||
| Basics of Neural Networks | |||
| What is a neural network, and how does it resemble human brain functionality? | |||
| Elaborate on the structure of a basic artificial neuron. | |||
| Describe the architecture of a multi-layer perceptron (MLP). | |||
| How does feedforward neural network differ from recurrent neural networks (RNNs)? | |||
| What is backpropagation, and why is it important in neural networks? | |||
| Explain the role of an activation function. Give examples of some common activation functions. | |||
| Describe the concept of deep learning in relation to neural networks. | |||
| What’s the difference between fully connected and convolutional layers in a network? | |||
| What is a vanishing gradient problem? How does it affect training? | |||
| How does the exploding gradient problem occur, and what are the potential solutions? | |||
| Explain the trade-offs between bias and variance. | |||
| What is regularization in neural networks, and why is it used? | |||
| What are dropout layers, and how do they help in preventing overfitting? | |||
| How do batch normalization layers work, and what problem do they solve? | |||
| What are skip connections and residual blocks in neural networks? | |||
| Basics of Transfer Learning | |||
| What is transfer learning and how does it differ from traditional machine learning? | |||
| Can you explain the concept of domain and task in the context of transfer learning? | |||
| What are the benefits of using transfer learning techniques? | |||
| In which scenarios is transfer learning most effective? | |||
| Describe the difference between transductive transfer learning and inductive transfer learning. | |||
| Explain the concept of ‘negative transfer’. When can it occur? | |||
| What role do pre-trained models play in transfer learning? | |||
| How can transfer learning be deployed in small data scenarios? | |||
| What are feature extractors in the context of transfer learning? | |||
| Describe the process of fine-tuning a pre-trained neural network. | |||
| What is one-shot learning and how does it relate to transfer learning? | |||
| Explain the differences between few-shot learning and zero-shot learning. | |||
| How do multi-task learning and transfer learning compare? | |||
| Discuss the concept of self-taught learning within transfer learning. | |||
| Deep Learning Architectures | |||
| Basics of CNN (Convolutional Neural Network) | |||
| What is a Convolutional Neural Network (CNN)? | |||
| Can you explain the structure of a typical CNN architecture? | |||
| How does convolution work in the context of a CNN? | |||
| What is the purpose of pooling in a CNN, and what are the different types? | |||
| How do activation functions play a role in CNNs? | |||
| Can you describe what is meant by ‘depth’ in a convolutional layer? | |||
| How do CNNs deal with overfitting? | |||
| What is the difference between a fully connected layer and a convolutional layer? | |||
| What is feature mapping in CNNs? | |||
| How does parameter sharing work in convolutional layers? | |||
| Why are CNNs particularly well-suited for image recognition tasks? | |||
| Explain the concept of receptive fields in the context of CNNs. | |||
| What is local response normalization, and why might it be used in a CNN? | |||
| Can you explain what a stride is and how it affects the output size of the convolution layer? | |||
| How do dilated convolutions differ from regular convolutions? | |||
| Describe the backpropagation process in a CNN. | |||
| What are the advantages of using deep CNNs compared to shallow ones? | |||
| Explain the vanishing gradient problem and how it impacts CNNs. | |||
| What is transfer learning and fine-tuning in the context of CNNs? | |||
| What are some common strategies for initializing weights in CNNs? | |||
| Basics of Recurrent Neural Networks (RNNs) | |||
| What are Recurrent Neural Networks (RNNs), and how do they differ from Feedforward Neural Networks? | |||
| Explain the concept of time steps in the context of RNNs. | |||
| What types of sequences are RNNs good at modeling? | |||
| Can you describe how the hidden state in an RNN operates? | |||
| What are the challenges associated with training vanilla RNNs? | |||
| Discuss the importance of activation functions in RNNs. | |||
| How does backpropagation through time (BPTT) work in RNNs? | |||
| What are some limitations of BPTT, and how can they be mitigated? | |||
| Explain the vanishing gradient problem in RNNs and why it matters. | |||
| What is the exploding gradient problem, and how can it affect RNN performance? | |||
| What are Long Short-Term Memory (LSTM) networks, and how do they address the vanishing gradient problem? | |||
| Describe the gating mechanism of an LSTM cell. | |||
| Explain the differences between LSTM and GRU (Gated Recurrent Unit) networks. | |||
| How do attention mechanisms work in conjunction with RNNs? | |||
| What are Bidirectional RNNs, and when would you use them? | |||
| Describe the process of implementing an RNN with TensorFlow or PyTorch. | |||
| How would you preprocess text data for training an RNN? | |||
| Explain how you would use an RNN for generating text sequences. | |||
| Explain how you would use an RNN for generating text sequences. | |||
| How do you prevent overfitting while training an RNN model? | |||
| Basics of Generative Adversarial Networks (GANs) | |||
| What are Generative Adversarial Networks (GANs)? | |||
| Could you describe the architecture of a basic GAN? | |||
| Explain the roles of the generator and discriminator in a GAN. | |||
| How do GANs handle the generation of new, unseen data? | |||
| What loss functions are commonly used in GANs and why? | |||
| How is the training process different for the generator and discriminator? | |||
| What is mode collapse in GANs, and why is it problematic? | |||
| Can you describe the concept of Nash equilibrium in the context of GANs? | |||
| How can we evaluate the performance and quality of GANs? | |||
| What are some challenges in training GANs? | |||
| Explain the idea behind Conditional GANs (cGANs) and their uses. | |||
| What are Deep Convolutional GANs (DCGANs) and how do they differ from basic GANs? | |||
| Can you discuss the architecture and benefits of Wasserstein GANs (WGANs)? | |||
| Describe the concept of CycleGAN and its application to image-to-image translation. | |||
| Explain how GANs can be used for super-resolution imaging (SRGANs). | |||
| In what ways do GANs contribute to semi-supervised learning? | |||
| How do generative models like GANs handle feature matching? | |||
| Discuss Progressive Growing of GANs (PGGANs) and their unique training approach. | |||
| What are StyleGANs and how do they manage the generation of high-resolution images? | |||
| How does the GAN framework support tasks like text-to-image synthesis? | |||
| Basics of Large Language Models (LLMs) | |||
| What are Large Language Models (LLMs) and how do they work? | |||
| Describe the architecture of a transformer model that is commonly used in LLMs. | |||
| What are the main differences between LLMs and traditional statistical language models? | |||
| Can you explain the concept of attention mechanisms in transformer models? | |||
| What are positional encodings in the context of LLMs? | |||
| Discuss the significance of pre-training and fine-tuning in the context of LLMs. | |||
| How do LLMs handle context and long-term dependencies in text? | |||
| What is the role of transformers in achieving parallelization in LLMs? | |||
| What are some prominent applications of LLMs today? | |||
| How is GPT-4 different from its predecessors like GPT-3 in terms of capabilities and applications? | |||
| Can you mention any domain-specific adaptations of LLMs? | |||
| How do LLMs contribute to the field of sentiment analysis? | |||
| Describe how LLMs can be used in the generation of synthetic text. | |||
| In what ways can LLMs be utilized for language translation? | |||
| Discuss the application of LLMs in conversation AI and chatbots. | |||
| Computer Vision & NLP | |||
| Basics of Computer Vision | |||
| Computer VisionWhat is computer vision and how does it relate to human vision? | |||
| Describe the key components of a computer vision system. | |||
| Explain the concept of image segmentation in computer vision. | |||
| What is the difference between image processing and computer vision? | |||
| How does edge detection work in image analysis? | |||
| Discuss the role of convolutional neural networks (CNNs) in computer vision. | |||
| What’s the significance of depth perception in computer vision applications? | |||
| Explain the challenges of object recognition in varied lighting and orientations. | |||
| What are the common image preprocessing steps in a computer vision pipeline? | |||
| How does image resizing affect model performance? | |||
| What are some techniques to reduce noise in an image? | |||
| Explain how image augmentation can improve the performance of a vision model. | |||
| Discuss the concept of color spaces and their importance in image processing. | |||
| Basics of Natural Language Processing (NLP) | |||
| What is Natural Language Processing (NLP) and why is it important? | |||
| What do you understand by the terms ‘corpus’, ‘tokenization’, and ‘stopwords’ in NLP? | |||
| Distinguish between morphology and syntax in the context of NLP. | |||
| Explain the significance of Part-of-Speech (POS) tagging in NLP. | |||
| Describe lemmatization and stemming. When would you use one over the other? | |||
| What is a ‘named entity’ and how is Named Entity Recognition (NER) useful in NLP tasks? | |||
| Define ‘sentiment analysis’ and discuss its applications. | |||
| How does a dependency parser work, and what information does it provide? | |||
| What are n-grams, and how do they contribute to language modeling? | |||
| Describe what a ‘bag of words’ model is and its limitations. | |||
| ML Operationalization & Evaluation | |||
| Basics of MLOps | |||
| What is MLOps and how does it differ from DevOps? | |||
| Can you explain the MLOps lifecycle and its key stages? | |||
| What are some of the benefits of implementing MLOps practices in a machine learning project? | |||
| What is a model registry and what role does it play in MLOps? | |||
| What are feature stores, and why are they important in MLOps? | |||
| Explain the concept of continuous integration and continuous delivery (CI/CD) in the context of machine learning. | |||
| What are DataOps and how do they relate to MLOps? | |||
| Describe the significance of experiment tracking in MLOps. | |||
| What are some popular tools and platforms used for MLOps? | |||
| How do containerization and virtualization technologies support MLOps practices? | |||
| What is the role of cloud computing in MLOps? | |||
| How would you design a scalable machine learning infrastructure? | |||
| What considerations are important when choosing a computation resource for training machine learning models? | |||
| Explain environment reproducibility and its challenges in MLOps. | |||
| How does infrastructure as code (IaC) support machine learning operations? | |||
| Describe the process of setting up a CI/CD pipeline for a machine learning project. | |||
| How do you automate model testing and validation in an MLOps pipeline? | |||
| What are some strategies for managing dependencies and version control in machine learning projects? | |||
| Explain the concept of blue/green deployments in the context of machine learning models. | |||
| How do feature flags play into the deployment of new model features? | |||
| Basics of ML Evaluation | |||
| What is model evaluation in the context of machine learning? | |||
| Explain the difference between training, validation, and test datasets. | |||
| What is cross-validation, and why is it used? | |||
| Define precision, recall, and F1-score. | |||
| What do you understand by the term “Confusion Matrix”? | |||
| Explain the concept of the ROC curve and AUC. | |||
| Why is accuracy not always the best metric for model evaluation? | |||
| What is meant by ‘overfitting’ and ‘underfitting’ in machine learning models? | |||
| How can learning curves help in model evaluation? | |||
| What is the difference between explained variance and R-squared? | |||
| How do you evaluate a regression model’s performance? | |||
| What metrics would you use to evaluate a classifier’s performance? | |||
| Explain the use of the Mean Squared Error (MSE) in regression models. | |||
| How is the Area Under the Precision-Recall Curve (AUPRC) beneficial? | |||
| What is the distinction between macro-average and micro-average in classification metrics? | |||
| How do you interpret a model’s calibration curve? | |||
| What is the Brier score, and when would you use it? | |||
| Describe how you would use bootstrapping in model evaluation. | |||
| When is it appropriate to use the Matthews Correlation Coefficient (MCC)? | |||
| What are the trade-offs between the different model evaluation metrics? | |||
| Explainability, Fairness & Ethics | |||
| Basics of Explainability (XAI) | |||
| What is Explainable AI (XAI), and why is it important? | |||
| Can you explain the difference between interpretable and explainable models? | |||
| What are some challenges faced when trying to implement explainability in AI? | |||
| How does XAI relate to model transparency, and why is it needed in sensitive applications? | |||
| What are some of the trade-offs between model accuracy and explainability? | |||
| What are model-agnostic methods in XAI, and can you give an example? | |||
| How do model-specific methods differ from model-agnostic methods for explainability? | |||
| What are the advantages and disadvantages of using LIME (Local Interpretable Model-Agnostic Explanations)? | |||
| Can you explain what SHAP (Shapley Additive exPlanations) is and when it is used? | |||
| What is feature importance, and how can it help in explaining model predictions? | |||
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