Machine Learning Interview Questions: 60+ Questions with Answers for 2026

You've built models, trained neural networks, and tuned hyperparameters. You know scikit-learn, TensorFlow, and PyTorch.

But then the interviewer asks: "How would you handle class imbalance in a production fraud detection system?" or "Explain the bias-variance tradeoff mathematically."

Suddenly, all that practical experience doesn't translate into clear, confident answers.

This guide gives you 60+ real machine learning interview questions asked at Google, Meta, Amazon, and top AI startups — with expert answers that demonstrate both theoretical understanding and practical experience.

What ML Interviews Actually Test

Machine learning interviews evaluate multiple dimensions:

• Fundamentals: Algorithms, math, statistics
• Practical experience: Real-world problem solving
• System design: Production ML systems at scale
• Coding: Implementing algorithms from scratch
• Communication: Explaining complex concepts simply

Companies want ML engineers who can both understand the theory AND ship production systems.

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ML Fundamentals Questions

1. Explain the bias-variance tradeoff

Answer:

Bias measures how far off predictions are from true values on average. High bias = underfitting.

Variance measures how much predictions change with different training data. High variance = overfitting.

Total Error = Bias² + Variance + Irreducible Error

High Bias, Low Variance    →  Simple model, consistent but wrong
Low Bias, High Variance    →  Complex model, fits training but not test
Optimal                    →  Balance that minimizes total error

In practice:

• Increase complexity (more features, deeper networks) → reduces bias, increases variance
• Add regularization (L1/L2, dropout) → reduces variance, may increase bias
• More training data → reduces variance without affecting bias

Interview tip: Give a concrete example — "A linear regression on non-linear data has high bias. A deep neural network on small data has high variance."

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2. What is regularization and why do we use it?

Answer:

Regularization adds a penalty term to the loss function to prevent overfitting by discouraging complex models.

L1 Regularization (Lasso):

Loss = Original Loss + λ Σ|wᵢ|

• Produces sparse solutions (some weights become exactly 0)
• Good for feature selection
• Creates a diamond constraint region

L2 Regularization (Ridge):

Loss = Original Loss + λ Σwᵢ²

• Shrinks weights toward zero but rarely exactly zero
• Handles correlated features better
• Creates a circular constraint region

Elastic Net: Combines L1 + L2

In neural networks:

• Dropout: Randomly zero out neurons during training
• Early stopping: Stop training when validation loss increases
• Data augmentation: Artificially increase training data

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3. Explain gradient descent and its variants

Answer:

Gradient descent finds minimum of a function by iteratively moving in the direction of steepest descent.

w = w - η * ∇L(w)

Variants:

Method	Update Frequency	Pros	Cons
Batch GD	After full dataset	Stable gradients	Slow, memory intensive
Stochastic GD	After each sample	Fast, can escape local minima	Noisy updates
Mini-batch GD	After batch (32-256)	Balance of both	Requires batch size tuning

Advanced optimizers:

Momentum: Accumulates gradient direction

v = βv + η∇L(w)
w = w - v

Adam: Adaptive learning rates + momentum

m = β₁m + (1-β₁)∇L        # First moment (mean)
v = β₂v + (1-β₂)∇L²       # Second moment (variance)
w = w - η * m / (√v + ε)

When to use what:

• Adam: Good default, works well in practice
• SGD + Momentum: Often better final accuracy with proper tuning
• AdamW: Adam with proper weight decay (recommended for transformers)

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4. How do you handle imbalanced datasets?

Answer:

Data-level approaches:

1. Oversampling minority class:

   • Random oversampling
   • SMOTE (Synthetic Minority Oversampling)
   • ADASYN (Adaptive Synthetic Sampling)

2. Undersampling majority class:

   • Random undersampling
   • Tomek links
   • NearMiss algorithm

3. Data augmentation: Generate synthetic minority samples

Algorithm-level approaches:

1. Class weights:

from sklearn.linear_model import LogisticRegression
model = LogisticRegression(class_weight='balanced')

2. Cost-sensitive learning: Different misclassification costs

3. Anomaly detection framing: Treat minority as anomalies

Evaluation:

• Don't use accuracy! Use:
  • Precision, Recall, F1-score
  • PR-AUC (better than ROC-AUC for imbalanced data)
  • Confusion matrix analysis

Production example: "For fraud detection at 0.1% fraud rate, I'd use SMOTE during training, class weights, and optimize for precision-recall AUC rather than accuracy."

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5. Explain cross-validation and when to use different types

Answer:

K-Fold CV:

• Split data into k folds
• Train on k-1, validate on 1, rotate
• Average performance across folds

from sklearn.model_selection import cross_val_score
scores = cross_val_score(model, X, y, cv=5)

When to use different types:

Type	Use Case
K-Fold (k=5 or 10)	Standard, balanced datasets
Stratified K-Fold	Imbalanced classification
Leave-One-Out (LOO)	Very small datasets
Time Series Split	Temporal data (prevent leakage)
Group K-Fold	Data with groups (e.g., multiple samples per user)

Common mistake: Using regular K-Fold on time series data causes data leakage (future information in training data).

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6. What's the difference between bagging and boosting?

Answer:

Bagging (Bootstrap Aggregating):

• Train models on random bootstrap samples in parallel
• Combine via averaging (regression) or voting (classification)
• Reduces variance
• Example: Random Forest

Data → [Sample 1] → Model 1 ↘
     → [Sample 2] → Model 2 → Average → Final Prediction
     → [Sample 3] → Model 3 ↗

Boosting:

• Train models sequentially, each correcting previous errors
• Weight samples by how poorly they were predicted
• Reduces bias
• Examples: AdaBoost, Gradient Boosting, XGBoost

Data → Model 1 → Errors → Weight → Model 2 → Errors → Model 3 → Sum

Key differences:

Aspect	Bagging	Boosting
Training	Parallel	Sequential
Focus	Reduce variance	Reduce bias
Overfitting	Resistant	Can overfit
Trees	Full depth	Shallow (stumps)

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7. Explain the ROC curve and AUC

Answer:

ROC (Receiver Operating Characteristic) curve plots:

• X-axis: False Positive Rate (FPR) = FP / (FP + TN)
• Y-axis: True Positive Rate (TPR) = TP / (TP + FN)

At different classification thresholds.

AUC (Area Under Curve):

• AUC = 0.5 → Random classifier
• AUC = 1.0 → Perfect classifier
• AUC = 0.8 → 80% chance a random positive is ranked higher than a random negative

When to use:

• ROC-AUC: Good for balanced datasets, comparing models
• PR-AUC: Better for imbalanced datasets (focuses on positive class)

Interview tip: "ROC-AUC can be misleading with severe class imbalance. A model that predicts all negatives might have high AUC but zero recall. I'd use precision-recall curves instead."

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Deep Learning Questions

8. Explain backpropagation mathematically

Answer:

Backpropagation computes gradients of the loss with respect to each weight using the chain rule.

For a network: Input → Hidden → Output

Forward pass:

z₁ = W₁x + b₁
a₁ = σ(z₁)
z₂ = W₂a₁ + b₂
ŷ = σ(z₂)
L = loss(y, ŷ)

Backward pass (chain rule):

∂L/∂W₂ = ∂L/∂ŷ * ∂ŷ/∂z₂ * ∂z₂/∂W₂
∂L/∂W₁ = ∂L/∂ŷ * ∂ŷ/∂z₂ * ∂z₂/∂a₁ * ∂a₁/∂z₁ * ∂z₁/∂W₁

Key insight: Gradients flow backward, multiplied at each layer. This is why:

• Vanishing gradients: Sigmoid/tanh squash gradients → use ReLU
• Exploding gradients: Gradients compound → use gradient clipping

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9. Why do we use activation functions and compare them

Answer:

Without activation functions: Neural network = linear transformation, no matter how deep.

Layer1: y = W₁x
Layer2: y = W₂(W₁x) = (W₂W₁)x = Wx  ← Still linear!

Common activations:

Function	Formula	Pros	Cons
ReLU	max(0, x)	Fast, no vanishing gradient	Dead neurons
Leaky ReLU	max(0.01x, x)	Prevents dead neurons	Small gradient for negative
ELU	x if x>0, α(eˣ-1)	Smooth, negative values	Expensive (exp)
GELU	x * Φ(x)	Best for transformers	Complex
Sigmoid	1/(1+e⁻ˣ)	Output [0,1]	Vanishing gradient
Tanh	(eˣ-e⁻ˣ)/(eˣ+e⁻ˣ)	Zero-centered	Vanishing gradient
Softmax	eˣⁱ/Σeˣʲ	Multi-class probabilities	Output layer only

When to use:

• Hidden layers: ReLU (default), GELU (transformers)
• Output, binary: Sigmoid
• Output, multi-class: Softmax

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10. Explain batch normalization and why it works

Answer:

Batch normalization normalizes layer inputs across the mini-batch:

μ = mean(x)
σ² = var(x)
x̂ = (x - μ) / √(σ² + ε)
y = γx̂ + β  ← Learnable scale and shift

Why it works (multiple theories):

1. Internal covariate shift: Reduces shift in layer input distributions (original paper)

2. Smoother loss landscape: Recent research suggests this is the main benefit — makes optimization easier

3. Regularization effect: Adds noise due to batch statistics, slight regularization

Practical benefits:

• Faster training (can use higher learning rates)
• Less sensitive to initialization
• Acts as slight regularizer (can remove dropout)

Alternatives:

• Layer Norm: Normalizes across features (better for transformers, RNNs)
• Group Norm: Normalizes across groups of channels (small batches)
• Instance Norm: Normalizes per sample per channel (style transfer)

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11. What is the vanishing gradient problem and how to address it?

Answer:

Problem: In deep networks, gradients become exponentially small when backpropagating through many layers, making early layers learn very slowly.

Cause: Multiplying many small gradients (sigmoid/tanh derivatives are ≤ 0.25)

Solutions:

1. Better activations:

   • ReLU (gradient = 1 for positive inputs)
   • Leaky ReLU, ELU

2. Architectural changes:

   • Skip/residual connections (ResNet)
   • Highway networks
   • Dense connections (DenseNet)

3. Better initialization:

   • Xavier/Glorot: Var(W) = 1/n_in (for tanh)
   • He initialization: Var(W) = 2/n_in (for ReLU)

4. Normalization:

   • Batch normalization
   • Layer normalization

5. LSTM/GRU for RNNs: Gates control gradient flow

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12. Explain attention mechanism and transformers

Answer:

Attention allows the model to focus on relevant parts of the input:

Attention(Q, K, V) = softmax(QKᵀ / √dₖ) V

• Q (Query): What we're looking for
• K (Key): What we match against
• V (Value): What we retrieve
• √dₖ: Scaling factor for stable gradients

Self-attention: Q, K, V all come from the same input

Multi-head attention: Run attention multiple times with different projections:

MultiHead(Q,K,V) = Concat(head₁, ..., headₕ) Wᴼ
headᵢ = Attention(QWᵢQ, KWᵢK, VWᵢV)

Transformer architecture:

1. Input embeddings + positional encoding
2. N layers of:
   • Multi-head self-attention
   • Add & Norm
   • Feed-forward network
   • Add & Norm
3. Output

Why transformers work:

• Parallel processing: No sequential dependency like RNNs
• Long-range dependencies: Attention connects any two positions directly
• Scalability: Can be trained on massive datasets

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13. Compare CNNs, RNNs, and Transformers

Answer:

Aspect	CNN	RNN/LSTM	Transformer
Best for	Images, local patterns	Sequential, time series	NLP, any sequence
Parallelization	High	Low (sequential)	High
Long-range deps	Limited by kernel	Vanishing gradients	Direct attention
Inductive bias	Translation invariance	Sequential order	Minimal
Memory	O(1) per layer	O(sequence length)	O(n²) for attention
Training	Fast	Slow	Very fast (parallel)

When to use:

• CNN: Images, audio spectrograms, any grid-like data
• RNN/LSTM: When sequential processing is required, small sequences
• Transformer: NLP, long sequences, large datasets

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NLP Specific Questions

14. Explain word embeddings (Word2Vec, GloVe)

Answer:

Word embeddings map words to dense vectors where semantic similarity = vector similarity.

Word2Vec:

• Skip-gram: Predict context words from center word
• CBOW: Predict center word from context words

"The cat sat on the mat"
Skip-gram: cat → [the, sat]
CBOW: [the, sat] → cat

Training: Use negative sampling (contrast true context with random words)

GloVe (Global Vectors):

• Uses global co-occurrence statistics
• Factorizes log co-occurrence matrix
• Combines local context (Word2Vec) + global statistics

Properties:

• Similar words have similar vectors
• Captures relationships: king - man + woman ≈ queen
• Fixed vocabulary (OOV problem)

Modern approach: Contextual embeddings (BERT) where word vectors depend on context

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15. Explain BERT and how it's pre-trained

Answer:

BERT (Bidirectional Encoder Representations from Transformers) uses:

Architecture: Transformer encoder (bidirectional attention)

Pre-training objectives:

1. Masked Language Model (MLM):
   • Randomly mask 15% of tokens
   • Predict masked tokens from context
   • Bidirectional (unlike GPT)

Input: "The [MASK] sat on the mat"
Output: Predict "cat"

2. Next Sentence Prediction (NSP):
   • Given two sentences, predict if B follows A
   • Helps with tasks needing sentence relationships

Fine-tuning:

• Add task-specific layer on top
• Fine-tune all parameters on task data

Variants:

• RoBERTa: No NSP, more data, dynamic masking
• ALBERT: Parameter sharing, factorized embeddings
• DistilBERT: Smaller, distilled from BERT

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System Design for ML

16. Design a recommendation system for an e-commerce platform

Answer:

Clarifying questions:

• Scale? (users, products, interactions)
• Latency requirements?
• What signals available? (purchases, views, ratings)

High-level architecture:

User → Feature Store → Candidate Generation → Ranking → Re-ranking → Results
                              ↓
                      Embedding Index

Components:

1. Candidate Generation (Recall):

   • Collaborative filtering (user-item embeddings)
   • Content-based (item features)
   • Popular items (cold start)

2. Ranking (Precision):

   • More complex model (GBM, DNN)
   • User features + item features + context
   • Optimize for click/purchase probability

3. Re-ranking:

   • Diversity (don't show all similar items)
   • Business rules (promoted items, freshness)
   • Fairness constraints

Handling challenges:

• Cold start: Use content features, popular items, ask preferences
• Scalability: Two-stage (fast recall + precise ranking)
• Real-time: Precompute embeddings, online feature store

Evaluation:

• Offline: Precision@K, Recall@K, NDCG
• Online: A/B test CTR, conversion rate, revenue

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17. Design a fraud detection system

Answer:

Requirements:

• Real-time (< 100ms latency)
• High precision (minimize false positives blocking users)
• Evolving fraud patterns (adversarial)

Architecture:

Transaction → Feature Engineering → Model Prediction → Rules Engine → Decision
     ↓              ↓                      ↓
   Kafka      Feature Store           Model Store

Features:

1. Transaction features: Amount, merchant, time, device
2. Aggregated features: User's avg spend, recent activity
3. Graph features: Connection to known fraudsters
4. Behavioral: Session patterns, typing speed

Model choices:

• Real-time: Gradient boosting (fast inference)
• Batch enrichment: Neural network for complex patterns
• Ensemble: Multiple models, different signals

Handling imbalance:

• Class weights
• Anomaly detection framing
• Cost-sensitive learning (different costs for FP vs FN)

Challenges:

• Concept drift: Fraud patterns change
  • Solution: Continuous monitoring, periodic retraining
• Adversarial: Fraudsters adapt
  • Solution: Multiple signals, graph analysis
• Feedback delay: Know fraud status later
  • Solution: Semi-supervised, anomaly detection

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18. How would you deploy and monitor an ML model in production?

Answer:

Deployment patterns:

1. Batch prediction:

   • Run daily/hourly
   • Store predictions in database
   • Good for: Recommendations, risk scores

2. Real-time inference:

   • API serving predictions
   • Low latency requirements
   • Good for: Search ranking, fraud detection

3. Embedded:

   • Model runs on device
   • Good for: Mobile apps, edge devices

MLOps pipeline:

Data → Validation → Training → Evaluation → Registry → Deployment → Monitoring
  ↑                                                                    ↓
  └──────────────────── Retraining trigger ←───────────────────────────┘

Monitoring:

1. Model performance:

   • Accuracy metrics (if labels available)
   • Prediction distribution shifts

2. Data quality:

   • Schema validation
   • Feature distributions (drift detection)

3. System health:

   • Latency, throughput, errors
   • Resource utilization

When to retrain:

• Scheduled (weekly, monthly)
• Performance degradation
• Significant data drift
• New training data threshold

---

Coding Challenges

19. Implement logistic regression from scratch

import numpy as np

class LogisticRegression:
    def __init__(self, lr=0.01, n_iters=1000):
        self.lr = lr
        self.n_iters = n_iters
        self.weights = None
        self.bias = None

    def _sigmoid(self, z):
        return 1 / (1 + np.exp(-np.clip(z, -500, 500)))

    def fit(self, X, y):
        n_samples, n_features = X.shape
        self.weights = np.zeros(n_features)
        self.bias = 0

        for _ in range(self.n_iters):
            # Forward pass
            z = np.dot(X, self.weights) + self.bias
            predictions = self._sigmoid(z)

            # Gradients
            dw = (1/n_samples) * np.dot(X.T, (predictions - y))
            db = (1/n_samples) * np.sum(predictions - y)

            # Update
            self.weights -= self.lr * dw
            self.bias -= self.lr * db

    def predict_proba(self, X):
        z = np.dot(X, self.weights) + self.bias
        return self._sigmoid(z)

    def predict(self, X, threshold=0.5):
        return (self.predict_proba(X) >= threshold).astype(int)

---

20. Implement K-Means clustering from scratch

import numpy as np

class KMeans:
    def __init__(self, n_clusters=3, max_iters=100, tol=1e-4):
        self.n_clusters = n_clusters
        self.max_iters = max_iters
        self.tol = tol
        self.centroids = None

    def fit(self, X):
        n_samples = X.shape[0]

        # Initialize centroids randomly
        idx = np.random.choice(n_samples, self.n_clusters, replace=False)
        self.centroids = X[idx].copy()

        for _ in range(self.max_iters):
            # Assign clusters
            distances = self._compute_distances(X)
            labels = np.argmin(distances, axis=1)

            # Update centroids
            new_centroids = np.array([
                X[labels == k].mean(axis=0) if np.sum(labels == k) > 0
                else self.centroids[k]
                for k in range(self.n_clusters)
            ])

            # Check convergence
            if np.all(np.abs(new_centroids - self.centroids) < self.tol):
                break

            self.centroids = new_centroids

        return labels

    def _compute_distances(self, X):
        # Euclidean distance to each centroid
        return np.sqrt(((X[:, np.newaxis] - self.centroids) ** 2).sum(axis=2))

    def predict(self, X):
        distances = self._compute_distances(X)
        return np.argmin(distances, axis=1)

---

21. Implement a simple neural network layer

import numpy as np

class DenseLayer:
    def __init__(self, input_size, output_size, activation='relu'):
        # He initialization
        self.W = np.random.randn(input_size, output_size) * np.sqrt(2/input_size)
        self.b = np.zeros((1, output_size))
        self.activation = activation

    def forward(self, X):
        self.X = X
        self.Z = np.dot(X, self.W) + self.b

        if self.activation == 'relu':
            self.A = np.maximum(0, self.Z)
        elif self.activation == 'sigmoid':
            self.A = 1 / (1 + np.exp(-self.Z))
        elif self.activation == 'none':
            self.A = self.Z

        return self.A

    def backward(self, dA, lr=0.01):
        m = self.X.shape[0]

        if self.activation == 'relu':
            dZ = dA * (self.Z > 0)
        elif self.activation == 'sigmoid':
            dZ = dA * self.A * (1 - self.A)
        else:
            dZ = dA

        dW = (1/m) * np.dot(self.X.T, dZ)
        db = (1/m) * np.sum(dZ, axis=0, keepdims=True)
        dX = np.dot(dZ, self.W.T)

        self.W -= lr * dW
        self.b -= lr * db

        return dX

---

Statistics & Probability Questions

22. Explain the central limit theorem and its importance in ML

Answer:

CLT states: The sampling distribution of the mean approaches a normal distribution as sample size increases, regardless of the population distribution.

Importance in ML:

1. Confidence intervals: We can estimate uncertainty in predictions
2. Hypothesis testing: A/B tests rely on CLT for significance testing
3. Batch gradients: Mean gradient over batch is approximately normal
4. Model evaluation: Mean performance across samples is normal

Example: If you measure model accuracy on random test samples:

• Each sample's accuracy varies
• Mean accuracy across many samples → Normal distribution
• Can compute confidence intervals for true accuracy

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23. Explain p-values and common misconceptions

Answer:

P-value: Probability of observing results as extreme as the data, assuming the null hypothesis is true.

Correct interpretation: "If there's no real effect, there's a 5% chance of seeing results this extreme or more extreme."

Common misconceptions:

Misconception	Reality
P-value = probability null is true	P-value assumes null is true
p < 0.05 means important effect	Statistical significance ≠ practical significance
p > 0.05 means no effect	Absence of evidence ≠ evidence of absence
Small p-value = large effect	P-value says nothing about effect size

In ML context:

• Always report effect size alongside significance
• Consider practical significance (is 0.1% accuracy improvement meaningful?)
• Multiple comparisons inflate false positives (use Bonferroni correction)

---

Quick-Fire Questions

24. What's the difference between supervised, unsupervised, and reinforcement learning?

• Supervised: Learn from labeled data (classification, regression)
• Unsupervised: Find patterns in unlabeled data (clustering, dimensionality reduction)
• Reinforcement: Learn from rewards/penalties (game playing, robotics)

25. What is feature scaling and when is it necessary?

Normalizing features to similar ranges. Necessary for: gradient descent optimization, distance-based algorithms (KNN, SVM), regularization (L1/L2 penalize large weights).

26. What's the difference between L1 and L2 loss?

• L1 (MAE): |y - ŷ|, robust to outliers, sparse gradients
• L2 (MSE): (y - ŷ)², sensitive to outliers, smooth gradients

27. What is dropout and how does it work?

Randomly sets neuron outputs to zero during training. Prevents co-adaptation, acts as ensemble of networks, regularizes the model.

28. What is transfer learning?

Using a model trained on one task as starting point for another. Pre-train on large dataset (ImageNet), fine-tune on specific task.

29. What's the curse of dimensionality?

As dimensions increase: data becomes sparse, distances become meaningless, more data needed. Solutions: dimensionality reduction, feature selection.

30. Explain precision vs recall vs F1

• Precision: TP / (TP + FP) — Of predicted positives, how many correct?
• Recall: TP / (TP + FN) — Of actual positives, how many found?
• F1: Harmonic mean = 2PR/(P+R)

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ML Interview Preparation Checklist

Fundamentals:

• Bias-variance tradeoff
• Regularization (L1, L2, dropout)
• Gradient descent and optimizers
• Cross-validation strategies
• Evaluation metrics (precision, recall, AUC)

Algorithms:

• Linear/logistic regression
• Decision trees, random forests, boosting
• SVM, KNN, naive Bayes
• Clustering (K-means, hierarchical)
• Dimensionality reduction (PCA, t-SNE)

Deep Learning:

• Backpropagation math
• CNN architectures
• RNN/LSTM for sequences
• Attention and transformers
• Regularization (dropout, batch norm)

System Design:

• Recommendation systems
• Search ranking
• Fraud detection
• MLOps and deployment

Coding:

• Implement basic algorithms from scratch
• scikit-learn, PyTorch/TensorFlow proficiency
• Feature engineering

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