Learning AI

Load and inspect a tabular dataset using pandas, including checking data types and missing entries.

Generate and interpret summary statistics for a dataset's numerical and categorical features.

Visualize feature distributions using histograms and box plots to identify skewness and outliers.

Analyze relationships between pairs of variables using scatter plots and correlation matrices.

Handle missing values using various imputation strategies and justify the choice of method.

Apply feature scaling techniques (standardization, normalization) and explain when each is appropriate.

Encode categorical variables using one-hot encoding and label encoding.

Perform feature engineering using techniques such as polynomial features and binning.

Derive the closed-form solution for ordinary least squares (OLS) linear regression.

Implement linear regression from scratch using gradient descent.

Apply L1 (Lasso) and L2 (Ridge) regularization to linear models and explain their geometric interpretations.

Derive the logistic regression model from the concept of log-odds and explain the cross-entropy loss.

Explain the decision tree learning algorithm using metrics like Gini impurity or information gain.

Describe the bootstrap aggregating (bagging) technique and its implementation in Random Forests.

Explain the sequential model-building process of gradient boosting machines (e.g., XGBoost, LightGBM).

Compare linear models with tree-based ensembles in terms of performance, interpretability, and use cases.

For a classification task, compute and interpret accuracy, precision, recall, F1-score, and the confusion matrix.

Plot and interpret a Receiver Operating Characteristic (ROC) curve and compute the Area Under the Curve (AUC).

Implement k-fold cross-validation to obtain a robust estimate of model performance.

Diagnose model performance issues (bias vs. variance) using learning and validation curves.

Perform hyperparameter tuning for a given model using grid search and random search.

Select and justify the choice of evaluation metrics for a specific classification or regression problem.

Compare multiple trained models and select the best performer based on cross-validation results.

Explain the iterative process of the k-means clustering algorithm.

Implement the k-means algorithm from scratch using NumPy.

Evaluate clustering quality using the silhouette score and the elbow method.

Describe hierarchical clustering and interpret the resulting dendrogram.

Derive Principal Component Analysis (PCA) as an eigenvalue problem on the covariance matrix.

Apply PCA for dimensionality reduction and visualize high-dimensional data in 2D.

Describe common business use cases for clustering and dimensionality reduction.

Describe the architecture of a feedforward neural network, including layers, weights, biases, and activation functions.

Derive the backpropagation algorithm for a multi-layer network using the chain rule.

Implement the forward and backward passes of a simple neural network from scratch using NumPy.

Explain the core abstractions of a deep learning framework: Tensors, Autograd, and Modules.

Build and train a neural network for a classification task using PyTorch.

Explain the role of optimizers (e.g., SGD, Adam) and learning rate scheduling.

Apply regularization techniques (Dropout, Weight Decay, Batch Normalization) to a PyTorch model.

Use TensorBoard or a similar tool to visualize training progress and model graphs.

Explain the 2D convolution operation and the role of filters, stride, and padding.

Describe the architecture of a CNN, including convolutional, pooling, and fully connected layers.

Implement a custom CNN architecture in PyTorch for image classification.

Set up a data loading and transformation pipeline for an image dataset using torchvision.

Train a custom CNN on an image dataset and evaluate its performance.

Explain the concept of transfer learning via feature extraction and fine-tuning.

Fine-tune a pretrained CNN model (e.g., ResNet) for a new image classification task.

Apply data augmentation techniques to improve model generalization for image tasks.

Describe the architecture of a Recurrent Neural Network (RNN) and explain the vanishing/exploding gradient problem.

Explain the LSTM and GRU architectures and how their gating mechanisms address the vanishing gradient problem.

Train an LSTM-based model for a sequence prediction task in PyTorch.

Explain the scaled dot-product self-attention mechanism and its advantages over recurrence.

Describe the complete Transformer architecture, including multi-head attention, positional encoding, and residual connections.

Explain the pre-training objectives for large language models (e.g., masked language modeling for BERT, causal language modeling for GPT).

Fine-tune a pretrained Transformer model (e.g., from Hugging Face) for a downstream NLP task.

Explain the encoder-decoder architecture of an autoencoder for feature learning and compression.

Describe the probabilistic framework of a Variational Autoencoder (VAE), including its loss function and the reparameterization trick.

Describe the adversarial training process in Generative Adversarial Networks (GANs).

Implement the generator and discriminator for a simple GAN in PyTorch.

Implement the adversarial training loop for a GAN and discuss common failure modes and stabilization techniques.

Explain the core concepts of diffusion models at a high level.

Compare and contrast the applications and characteristics of VAEs, GANs, and diffusion models.

Define the components of a Markov Decision Process (MDP): states, actions, rewards, transitions, and discount factor.

Explain the Bellman equations and their role in computing value functions.

Implement the Q-learning algorithm in a simple grid-world environment.

Explain how Deep Q-Networks (DQN) use neural networks for function approximation and experience replay.

Describe the policy gradient theorem and the REINFORCE algorithm.

Explain the core principles of Actor-Critic methods and contrast them with value-based and policy-based methods.

Implement a simple Actor-Critic algorithm (e.g., A2C) for a classic control task.

Outline the key stages of an end-to-end ML lifecycle, from data ingestion to monitoring.

Containerize a machine learning application using Docker.

Save and load a trained model using framework-specific methods and the ONNX format for interoperability.

Create a REST API endpoint using Flask or FastAPI to serve model predictions.

Automate the training and deployment process using a simple CI/CD pipeline with GitHub Actions.

Explain the importance of model versioning and experiment tracking using tools like MLflow or DVC.

Describe common ethical concerns in AI, including data privacy, algorithmic bias, and fairness.

Analyze a case study of AI bias and propose mitigation strategies.

Explain the principles of model interpretability and apply techniques like SHAP or LIME to a trained model.

Differentiate between career paths in AI (ML Engineer, Research Scientist, Data Scientist) and their skill requirements.

Outline the key components of a professional AI/ML project portfolio.

Develop a strategy for contributing to an open-source AI project to gain practical experience.