AI Interview Preparation Guide

Table of Contents

  1. Machine Learning Fundamentals
  2. Deep Learning
  3. Natural Language Processing
  4. Computer Vision
  5. Reinforcement Learning
  6. Data Preprocessing & Feature Engineering
  7. Model Evaluation & Validation
  8. Optimization Techniques
  9. Practical Interview Questions

Machine Learning Fundamentals

Q1: What is Machine Learning and its types?

Answer: Machine Learning is a subset of Artificial Intelligence that enables systems to learn and improve from experience without being explicitly programmed.

Types:

  1. Supervised Learning: Learning from labeled data
    • Classification: Predicting discrete categories (Logistic Regression, Decision Trees, SVM, Random Forest)
    • Regression: Predicting continuous values (Linear Regression, Ridge, Lasso)
  2. Unsupervised Learning: Learning from unlabeled data
    • Clustering: K-means, DBSCAN, Hierarchical Clustering
    • Dimensionality Reduction: PCA, t-SNE, Autoencoders
  3. Semi-Supervised Learning: Learning from partially labeled data
    • Self-training, Co-training, Graph-based methods
  4. Reinforcement Learning: Learning through interaction and rewards
    • Q-Learning, Policy Gradient, Actor-Critic

Q2: What is the difference between supervised and unsupervised learning?

Answer:

Feature Supervised Unsupervised
Data Labeled data Unlabeled data
Goal Predict target variable Find patterns/structure
Examples Classification, Regression Clustering, Dimensionality Reduction
Performance Metric Accuracy, Precision, Recall, F1 Silhouette Score, Inertia
Computational Cost Lower (known targets) Higher (exploration needed)

Q3: What is overfitting and underfitting?

Answer:

Overfitting:

Solutions:

Underfitting:

Solutions:

Q4: What is the bias-variance tradeoff?

Answer:

Bias: Error from incorrect assumptions in learning algorithm

Variance: Sensitivity to fluctuations in training data

Total Error = Bias² + Variance + Irreducible Error

Tradeoff:

Q5: Explain Linear Regression

Answer:

Definition: Predicts continuous target variable using linear relationship

Formula: y = β₀ + β₁x₁ + β₂x₂ + … + βₙxₙ

Key Concepts:

Advantages:

Disadvantages:

Variants:

Q6: What is Logistic Regression?

Answer:

Definition: Classification algorithm for binary (and multiclass) problems

Formula:

Key Concepts:

Advantages:

Disadvantages:

Extensions:

Q7: Explain Decision Trees

Answer:

Definition: Tree-based model that recursively splits data based on feature values

Key Concepts:

Gini Impurity: Gini = 1 - Σ(p_i)² where p_i is proportion of class i

Information Gain: IG = Entropy(parent) - Σ[(N_child/N_parent) × Entropy(child)]

Advantages:

Disadvantages:

Regularization:

Q8: What is a Random Forest?

Answer:

Definition: Ensemble method combining multiple decision trees with bagging

How It Works:

  1. Create multiple bootstrap samples from training data
  2. Train decision tree on each sample
  3. For classification: Majority voting
  4. For regression: Average predictions

Key Hyperparameters:

Advantages:

Disadvantages:

Feature Importance: Calculated as average decrease in impurity across all trees

Q9: Explain Support Vector Machine (SVM)

Answer:

Definition: Algorithm that finds optimal hyperplane maximizing margin between classes

Key Concepts:

Mathematical Formulation:

Kernel Tricks: Used for non-linear classification

Advantages:

Disadvantages:

Q10: What is K-Nearest Neighbors (KNN)?

Answer:

Definition: Non-parametric algorithm classifying/predicting based on K nearest neighbors

How It Works:

  1. Calculate distance from query point to all training points
  2. Select K nearest points
  3. For classification: Majority class
  4. For regression: Average value

Distance Metrics:

Choosing K:

Advantages:

Disadvantages:

Deep Learning

Q11: What is a Neural Network?

Answer:

Definition: Computational model inspired by biological neural networks

Structure:

Neuron (Perceptron):

Output = Activation(Σ(weight × input) + bias)

Activation Functions:

Training Process:

  1. Forward propagation: Input → Hidden layers → Output
  2. Calculate loss (MSE, Cross-entropy, etc.)
  3. Backward propagation: Compute gradients
  4. Update weights using gradient descent

Loss Functions:

Advantages:

Disadvantages:

Q12: Explain Convolutional Neural Networks (CNN)

Answer:

Definition: Neural network designed for processing grid-like data (images, time series)

Key Components:

  1. Convolutional Layer: Applies filters to extract local features
    • Filter size: Typically 3×3 or 5×5
    • Stride: How much filter moves
    • Padding: Adding zeros around edges
  2. Pooling Layer: Reduces spatial dimensions
    • Max Pooling: Takes maximum value
    • Average Pooling: Takes average value
    • Typical size: 2×2

  3. Fully Connected Layer: Connects all neurons (classification)

  4. Activation Functions: ReLU for hidden layers, Softmax for output

Why CNNs Work:

Popular Architectures:

Applications:

Q13: Explain Recurrent Neural Networks (RNN)

Answer:

Definition: Neural networks with connections forming cycles, suitable for sequential data

Key Concept:

Variants:

Basic RNN:

LSTM (Long Short-Term Memory):

GRU (Gated Recurrent Unit):

Applications:

Challenges:

Q14: What is Transformer Architecture?

Answer:

Definition: Neural network architecture based on self-attention mechanism, replacing RNNs

Key Innovation: Self-Attention Mechanism

Attention(Q, K, V) = softmax(Q·K^T / √d_k)·V

Architecture Components:

  1. Multi-Head Attention:
    • Multiple parallel attention heads
    • Each head attends to different representation subspaces
    • Outputs concatenated and projected
  2. Feed-Forward Network:
    • Two linear transformations with activation
    • FFN(x) = ReLU(x·W_1 + b_1)·W_2 + b_2
  3. Layer Normalization:
    • Normalizes inputs before each sub-layer
    • Stabilizes training
  4. Positional Encoding:
    • Encodes position information (RNNs have inherent ordering)
    • PE(pos, 2i) = sin(pos/10000^(2i/d_model))
    • PE(pos, 2i+1) = cos(pos/10000^(2i/d_model))
  5. Residual Connections:
    • Each sub-layer: output = LayerNorm(x + SubLayer(x))
    • Aids gradient flow in deep networks

Advantages:

Disadvantages:

Popular Transformer Models:

Natural Language Processing

Q15: What is Word Embedding?

Answer:

Definition: Represents words as dense vectors capturing semantic meaning

Importance:

Common Approaches:

1. Word2Vec:

2. GloVe (Global Vectors):

3. FastText:

4. BERT/Contextual Embeddings:

Dimensions:

Evaluation:

Q16: Explain Tokenization and Text Preprocessing

Answer:

Tokenization: Breaking text into smaller units (words, subwords, characters)

Types:

  1. Word Tokenization: Split by whitespace/punctuation
    • Issue: “don’t” becomes separate tokens
  2. Subword Tokenization:
    • Byte Pair Encoding (BPE): Merges frequent character pairs
    • WordPiece: Similar to BPE, used in BERT
    • SentencePiece: Language-independent
  3. Character Tokenization: Each character is token
    • Handles unknown words
    • More computation needed

Text Preprocessing Steps:

  1. Lowercasing: Convert to lowercase
    • Treats “USA” and “usa” same
    • May lose information (proper nouns)
  2. Removing Punctuation: Remove special characters
    • “Hello!” → “Hello”
  3. Removing Stopwords: Remove common words (the, is, at)
    • Reduces noise
    • May lose context for some tasks
  4. Stemming: Reduce words to root form
    • “running”, “runs”, “ran” → “run”
    • Fast but crude (overstemming)
  5. Lemmatization: Convert to dictionary form
    • “better” → “good”
    • More accurate but slower
  6. Handling Special Tokens:

Sequence Padding/Truncation:

Q17: What is Attention Mechanism?

Answer:

Definition: Mechanism allowing models to focus on relevant parts of input

Problem It Solves:

Mechanism:

  1. Alignment Score: Compute similarity between decoder state and encoder outputs
    • Multiplicative: score = s·e / √d_k
    • Additive: score = v·tanh(W_s·[s, e])
  2. Normalization: Apply softmax to get attention weights
    • Weights sum to 1
    • Focus on most relevant parts
  3. Context Vector: Weighted sum of encoder outputs
    • context = Σ(attention_weight × encoder_output)

Attention Types:

  1. Self-Attention: Query, Key, Value from same sequence
    • Captures dependencies within sequence
  2. Cross-Attention: Query from one sequence, Key/Value from another
    • Encoder-decoder architecture
  3. Multi-Head Attention: Multiple attention heads in parallel
    • Different representation subspaces
    • Concatenate and project outputs

Advantages:

Applications:

Q18: Explain BERT and its Training

Answer:

BERT (Bidirectional Encoder Representations from Transformers)

Key Features:

Training Objectives:

1. Masked Language Modeling (MLM):

2. Next Sentence Prediction (NSP):

Architecture:

Fine-tuning for Downstream Tasks:

  1. Classification:
    • Add linear layer on top of CLS token
    • Fine-tune on task-specific data
  2. Token Labeling (NER, POS tagging):
    • Add linear layer on top of each token
    • Classify each token independently
  3. Question Answering:
    • Two linear layers: Start and End position prediction
    • Span containing answer is [start, end]

Fine-tuning Hyperparameters:

Advantages:

Disadvantages:

Computer Vision

Q19: How does Object Detection work? (YOLO, Faster R-CNN)

Answer:

Object Detection Task: Localize and classify objects in images Output: Bounding boxes + class labels + confidence scores

Approaches:

1. Two-Stage Detectors (Faster R-CNN):

Process:

  1. Feature Extraction: CNN (VGG, ResNet) extracts features
  2. Region Proposal Network (RPN): Generates candidate bounding boxes
  3. RPN Output: Class scores (object/background) + bounding box adjustments
  4. RoI Pooling: Extract fixed-size feature maps from proposals
  5. Classification & Regression: Classify objects and refine boxes

Advantages:

Disadvantages:

2. One-Stage Detectors (YOLO - You Only Look Once):

Process:

  1. Divide image into S×S grid
  2. Each grid cell predicts:
    • B bounding boxes with confidence scores
    • C class probabilities
  3. Non-Maximum Suppression: Remove overlapping boxes

Loss Function:

Advantages:

Disadvantages:

Performance Metrics:

Other Detectors:

Q20: Explain Image Segmentation

Answer:

Definition: Assigning class label to each pixel in image

Types:

1. Semantic Segmentation:

2. Instance Segmentation:

3. Panoptic Segmentation:

Architecture: U-Net

Structure:

Why Skip Connections:

Loss Functions:

Popular Architectures:

Applications:

Reinforcement Learning

Q21: What is Reinforcement Learning?

Answer:

Definition: Agent learns to make decisions by interacting with environment, receiving rewards/penalties

Key Components:

  1. Agent: Entity making decisions
  2. Environment: System the agent interacts with
  3. State (s): Current situation
  4. Action (a): Choice the agent makes
  5. Reward (r): Feedback from environment
  6. Policy (π): Mapping from state to action
  7. Value Function (V): Expected cumulative reward from state

Objective: Maximize cumulative reward over time

Markov Decision Process (MDP):

Approaches:

1. Value-Based Methods:

2. Policy-Based Methods:

3. Model-Based Methods:

Exploration vs Exploitation:

Applications:

Data Preprocessing & Feature Engineering

Q22: What is Feature Scaling and Normalization?

Answer:

Why Scale Features:

Normalization (Min-Max Scaling):

X_normalized = (X - X_min) / (X_max - X_min)

Standardization (Z-score):

X_standardized = (X - mean) / std_dev

Robust Scaling:

X_robust = (X - median) / IQR

Log Scaling:

X_log = log(X)

When to Use: | Algorithm | Scaling Needed | |———–|—————| | Linear/Logistic Regression | Yes | | Decision Trees | No | | SVM | Yes | | Neural Networks | Yes | | KNN | Yes | | Naive Bayes | Generally No | | Gradient Boosting | No |

Q23: How to Handle Missing Data?

Answer:

Causes:

Strategies:

1. Deletion:

2. Imputation (Fill Missing Values):

3. Using Algorithm-Specific Approaches:

Missing Data Mechanisms:

Practical Guidelines:

Q24: Explain Dimensionality Reduction

Answer:

Why Reduce Dimensions:

Principal Component Analysis (PCA):

Process:

  1. Standardize features to mean 0, std 1
  2. Compute covariance matrix
  3. Calculate eigenvalues and eigenvectors
  4. Sort by eigenvalues (descending)
  5. Select top K eigenvectors
  6. Project data onto selected components

Key Concepts:

Advantages:

Disadvantages:

t-SNE (t-Distributed Stochastic Neighbor Embedding):

UMAP (Uniform Manifold Approximation and Projection):

Feature Selection vs Reduction:

Curse of Dimensionality:

Model Evaluation & Validation

Q25: Explain Cross-Validation

Answer:

Problem: Using test set multiple times causes overfitting to test set

Solution: Cross-Validation - Multiple train/test splits

Types:

1. K-Fold Cross-Validation: Process:

  1. Divide data into K equal parts
  2. For each fold:
    • Use fold as test set
    • Use remaining K-1 as training set
    • Train model and evaluate
  3. Average metrics across all folds

Example (5-fold):

Advantages:

Disadvantages:

Typical K: 5 or 10 Stratified K-fold: For imbalanced data, maintain class ratios

2. Leave-One-Out Cross-Validation (LOOCV):

3. Time Series Cross-Validation:

4. Stratified Cross-Validation:

Metrics to Report:

Q26: Classification Metrics - Precision, Recall, F1

Answer:

Confusion Matrix:

                Predicted Positive    Predicted Negative
Actual Positive      TP                   FN
Actual Negative      FP                   TN

Metrics:

Accuracy:

Accuracy = (TP + TN) / (TP + TN + FP + FN)

Precision:

Precision = TP / (TP + FP)

Recall (Sensitivity, True Positive Rate):

Recall = TP / (TP + FN)

F1 Score (Harmonic Mean):

F1 = 2 × (Precision × Recall) / (Precision + Recall)

Precision-Recall Tradeoff:

ROC-AUC:

ROC: Receiver Operating Characteristic
X-axis: False Positive Rate = FP / (FP + TN)
Y-axis: True Positive Rate = TP / (TP + FN)
AUC: Area Under the Curve

When to Use Which:

Q27: Regression Metrics

Answer:

Mean Squared Error (MSE):

MSE = (1/n) Σ(y_actual - y_predicted)²

Root Mean Squared Error (RMSE):

RMSE = √MSE

Mean Absolute Error (MAE):

MAE = (1/n) Σ|y_actual - y_predicted|

R² (Coefficient of Determination):

R² = 1 - (SS_res / SS_tot)

Where:

Interpretation:

Adjusted R²:

Adjusted R² = 1 - [(1 - R²) × (n-1) / (n-p-1)]

MSE vs MAE: | Metric | Outlier Sensitivity | Interpretability | |——–|——————-|—————–| | MSE/RMSE | High | Squared units | | MAE | Low | Same units as target |

Optimization Techniques

Q28: Explain Gradient Descent Variants

Answer:

Gradient Descent: Iteratively move in direction of negative gradient

θ = θ - α × ∇J(θ)

Where:

Variants:

1. Batch Gradient Descent:

2. Stochastic Gradient Descent (SGD):

3. Mini-Batch Gradient Descent:

4. Momentum:

v = β × v + (1-β) × ∇J(θ)
θ = θ - α × v

5. Nesterov Accelerated Gradient (NAG):

v = β × v + (1-β) × ∇J(θ - α × v)
θ = θ - α × v

6. Adaptive Learning Rate Methods:

AdaGrad (Adaptive Gradient):

g_t² = g_t² + (∇J(θ))²
θ = θ - (α / √(g_t² + ε)) × ∇J(θ)

RMSprop:

g_t² = β × g_t² + (1-β) × (∇J(θ))²
θ = θ - (α / √(g_t² + ε)) × ∇J(θ)

Adam (Adaptive Moment Estimation):

m = β₁ × m + (1-β₁) × ∇J(θ)          [First moment estimate]
v = β₂ × v + (1-β₂) × (∇J(θ))²       [Second moment estimate]
m̂ = m / (1 - β₁^t)                    [Bias correction]
v̂ = v / (1 - β₂^t)
θ = θ - α × m̂ / (√v̂ + ε)

Comparison: | Method | Convergence | Stability | Memory | Best For | |——–|————-|———–|——–|———-| | SGD | Slow | Moderate | Low | Simple problems | | Momentum | Fast | Good | Low | General purpose | | AdaGrad | Medium | Good | Medium | Sparse data | | Adam | Fast | Excellent | Medium | Most problems |

Q29: What is Regularization?

Answer:

Problem: Overfitting - Model learns training data too well

Solution: Add penalty term to loss function

Loss = Data Loss + Regularization Term

Types:

L1 Regularization (Lasso):

Loss = MSE + λ × Σ|θ|

L2 Regularization (Ridge):

Loss = MSE + λ × Σ(θ²)

L1 vs L2: | Aspect | L1 | L2 | |——–|—-|—-| | Penalty | Absolute | Squared | | Sparsity | Yes (zero weights) | No | | Multicollinearity | No | Yes | | Feature Selection | Yes | No | | Solution | Corner (sparse) | Ridge (smooth) |

Elastic Net:

Loss = MSE + λ₁ × Σ|θ| + λ₂ × Σ(θ²)

Other Regularization Techniques:

Dropout (Neural Networks):

Early Stopping:

Data Augmentation:

Hyperparameter λ (regularization strength):

Practical Interview Questions

Q30: How would you approach building a recommendation system?

Answer:

Types of Recommendation Systems:

1. Content-Based Filtering: Process:

  1. Extract features of items (movies: genre, director, actors)
  2. Create user preference profile (weighted feature vectors)
  3. Recommend items similar to user’s past interactions
  4. Similarity metric: Cosine similarity

Advantages:

Disadvantages:

2. Collaborative Filtering:

User-Based:

Item-Based:

Advantages:

Disadvantages:

3. Matrix Factorization:

Advantages:

Disadvantages:

4. Deep Learning Approaches:

Combining Approaches (Hybrid):

Challenges & Solutions:

Challenge Solution
Cold-start (new user) Use content features, user demographics
Cold-start (new item) Use content features, item metadata
Sparsity Matrix factorization, deep learning
Diversity Re-rank recommendations, diversity loss
Popularity bias Down-weight popular items, calibration
Exploitation vs Exploration Bandit algorithms, Thompson sampling

Evaluation Metrics:

Q31: Explain how you would detect and handle outliers

Answer:

Detection Methods:

1. Statistical Methods:

Z-Score:

z = (x - mean) / std_dev

IQR (Interquartile Range):

Lower bound = Q1 - 1.5 × IQR
Upper bound = Q3 + 1.5 × IQR

2. Visualization:

3. Machine Learning Methods:

Isolation Forest:

Local Outlier Factor (LOF):

Mahalanobis Distance:

4. Domain Knowledge:

Handling Outliers:

1. Deletion:

2. Transformation:

3. Capping (Winsorization):

4. Robust Methods:

5. Separate Model:

Guidelines:

Q32: Walk me through your Machine Learning project

Answer:

Project Example: Predicting house prices

1. Problem Definition:

2. Data Collection & Exploration:

3. Data Preprocessing:

4. Feature Engineering:

5. Train/Test Split:

6. Model Selection:

7. Model Training:

8. Model Evaluation:

9. Results Interpretation:

10. Model Deployment:

11. Documentation:

Common Challenges & Solutions:

Issue Solution
High RMSE Check data quality, engineer features, try complex model
Train/test gap Regularization, more training data, cross-validation
Predictions biased Feature engineering, different model
Slow predictions Feature selection, model simplification, caching
Unstable model Ensemble methods, regularization, more data

Q33: How would you handle imbalanced data?

Answer:

Problem: Class distribution skewed (e.g., 95% negative, 5% positive)

Evaluation Metrics (First!):

Solutions:

1. Data Level Approaches:

Oversampling (Increase minority):

Original: 95% negative, 5% positive
After: 50% negative, 50% positive

Undersampling (Decrease majority):

Original: 95% negative, 5% positive
After: 50% negative, 50% positive

Hybrid: Combine over + undersampling (SMOTE + ENN)

2. Algorithm Level Approaches:

Class Weights:

Threshold Adjustment:

Ensemble Methods:

3. Hybrid Approaches:

4. Cost-Sensitive Learning:

Best Practices:

  1. SMOTE BEFORE train-test split: 
    X_train, X_test, y_train, y_test = train_test_split(X, y)
smote = SMOTE()
X_train_balanced, y_train_balanced = smote.fit_resample(X_train, y_train)
# Then train model on balanced training data
    • Avoid data leakage
    • Test set remains imbalanced (real distribution)
  2. Use appropriate metrics:
    • Precision/Recall/F1 over Accuracy
    • Confusion matrix analysis
    • Cost-based metrics if available
  3. Experiment with combinations:
    • SMOTE + Ensemble often works best
    • Try different ratios (oversample to what level?)
    • Cross-validation to avoid overfitting
  4. Domain context:
    • Cost of false positives vs false negatives
    • Business requirements (tolerable error rates)

Q34: Explain Hyperparameter Tuning

Answer:

Hyperparameters: Settings configured before training (not learned from data)

Examples:

Tuning Methods:

1. Grid Search:

param_grid = {
    'learning_rate': [0.001, 0.01, 0.1],
    'max_depth': [3, 5, 7],
    'n_estimators': [50, 100, 200]
}
# Test all 3×3×3 = 27 combinations

2. Random Search:

param_dist = {
    'learning_rate': uniform(0.001, 0.1),
    'max_depth': randint(3, 10),
    'n_estimators': randint(50, 300)
}
# Sample N random combinations

3. Bayesian Optimization:

4. Evolutionary Algorithms:

Workflow:

  1. Identify important hyperparameters:
    • Focus on sensitive parameters first
    • Less important: Learning rate vs activation
  2. Understand parameter effects:
    • Increasing max_depth: Overfit risk ↑
    • Increasing λ: Underfitting risk ↑
    • Decreasing learning_rate: Slower convergence
  3. Define search space:
    • Grid: When you know approximate range
    • Random/Bayesian: Larger continuous spaces
  4. Choose CV strategy:
    • K-fold (typically 5)
    • Stratified for classification
    • Time-series for temporal data
  5. Optimize metric:
    • Regression: RMSE or R²
    • Classification: F1, ROC-AUC (not Accuracy if imbalanced)
  6. Monitor overfitting:
    • Validation curve: Training vs validation score
    • If validation plateaus, stop
    • Learning curve: Score vs dataset size

Example: Learning Curves

X-axis: Training set size
Y-axis: Model score

High Bias (Underfitting):
Training error: High
Validation error: High (gap small)
Solution: More complex model

High Variance (Overfitting):
Training error: Low
Validation error: High (gap large)
Solution: More data or regularization

Tips:

  1. Coarse-to-fine: Start broad, narrow down
  2. Parallelization: Use multiple processors
  3. Early stopping: Stop unpromising combinations
  4. Combine with feature engineering:
    • Features matter more than hyperparameters
  5. Avoid multiple comparisons: Use hold-out test set
  6. Document results: For reproducibility and understanding

Q35: How would you explain model predictions to stakeholders?

Answer:

Importance:

Methods:

1. Feature Importance:

Example:

Top 3 important features for house price prediction:
1. Square footage (importance: 0.45)
2. Location (importance: 0.35)
3. Age (importance: 0.20)

Pros: Simple, global view Cons: Only global importance, not individual predictions

2. SHAP (SHapley Additive exPlanations):

SHAP Value Interpretation:

Types:

Pros: Theoretically sound, individual explanations, global insights Cons: Computationally expensive for large datasets

3. LIME (Local Interpretable Model-Agnostic Explanations):

Example:

This email is classified as SPAM because:
- Contains "free" (weight: +0.3)
- Unknown sender (weight: +0.25)
- Multiple links (weight: +0.2)

Pros: Model-agnostic, easy to understand Cons: Local approximation, less theoretically rigorous

4. Decision Rules:

5. Partial Dependence Plots:

6. Counterfactual Explanations:

7. Confidence/Uncertainty:

Communication Strategy:

1. Know Your Audience:

2. Tailor Explanation:

3. Visualization:

4. Validation:

5. Document Limitations:

Example Explanation Narrative:

Our recommendation system predicts which customers will churn with 87% accuracy.

For customer #12345:
- Predicted to churn with probability 0.75
- Key reasons:
  1. No purchase in last 3 months (contribution: +0.35)
  2. Older customer segment (contribution: +0.25)
  3. Below average satisfaction (contribution: +0.15)

Confidence: Medium (similar customers have 73% actual churn rate)

Recommendation: Proactive outreach with discount offer

Summary Table: When to Use Different Techniques

Task Best Approach
Classification Logistic Regression (baseline), SVM, Random Forest, Gradient Boosting
Regression Linear Regression (baseline), Ridge/Lasso, Random Forest, Gradient Boosting
Clustering K-means (simple), DBSCAN (arbitrary shape), Hierarchical (dendrograms)
Dimensionality Reduction PCA (linear), t-SNE (visualization), UMAP (preservation)
Text Classification BERT, Logistic Regression with TF-IDF
Sequence Modeling LSTM/GRU for short-range, Transformer for long-range
Image Classification CNN (ResNet, EfficientNet)
Object Detection YOLO (speed), Faster R-CNN (accuracy)
Imbalanced Data SMOTE + Ensemble, cost-weighted learning
Outlier Detection IQR, Isolation Forest, LOF
Recommendation Collaborative Filtering, Content-based, Matrix Factorization

Key Takeaways

  1. Always start with simple baselines before complex models
  2. Data quality matters more than model complexity
  3. Use appropriate evaluation metrics for your problem
  4. Cross-validation for reliable estimates of generalization
  5. Feature engineering is crucial for model performance
  6. Interpretability important for stakeholder trust
  7. Hyperparameter tuning requires systematic approach
  8. Imbalanced data needs special handling
  9. Monitor train-test gap to detect overfitting
  10. Document assumptions and limitations clearly

Recommended Reading & Resources

Good luck with your AI interviews! Remember to practice, stay updated with recent developments, and understand the fundamentals deeply.

https://github.com/ktwillcode/AI-Agents-Interview

https://github.com/Devinterview-io/llms-interview-questions

https://github.com/KalyanKS-NLP/LLM-Interview-Questions-and-Answers-Hub/blob/main/Interview_QA/QA_1-3.md https://skphd.medium.com/interview-questions-and-answers-on-agentic-ai-9069ed148f3d