Why DS + ML?
Efficient ML Code
Strong data structures knowledge helps you write optimized, production-grade ML pipelines that scale to large datasets without bottlenecks.
ML Engineer Interviews
Top companies test both DSA rounds and ML system design — this track covers both so you walk into interviews fully prepared.
Better Algorithms
Understand the algorithmic foundations behind ML algorithms like KNN, decision trees, and graph-based recommendation models.
Core Topics
Arrays, Strings & Hashing
Core building blocks used in feature engineering and data preprocessing pipelines, including hash maps for fast lookups in feature stores.
FoundationExplore
Trees & Graphs
Binary trees, BSTs, and graph traversal techniques — essential for decision trees, random forests, and recommendation systems.
IntermediateExplore
Regression & Classification
Linear/logistic regression, decision trees, random forests, and evaluation metrics like precision, recall, and F1-score.
ML CoreExplore
Clustering & Dimensionality Reduction
K-Means, hierarchical clustering, and PCA — unsupervised learning techniques for grouping data and reducing feature space.
ML CoreExplore
Dynamic Programming
Optimization techniques applicable to sequence models, reinforcement learning, and efficient algorithm design.
AdvancedExplore
Model Evaluation & Tuning
Cross-validation, hyperparameter tuning, bias-variance tradeoff, and choosing the right metric for your problem.
ML CoreExplore
12-Week Practice Plan
| Week | Focus | Goal |
|---|---|---|
| 1-2 | Arrays, Strings, Hashing | 50 problems on platforms like LeetCode |
| 3-4 | Trees & Graphs | 30 problems + 1 ML graph-based project |
| 5-6 | Regression & Classification | Build 2 end-to-end ML models |
| 7-8 | Clustering & PCA | 1 unsupervised learning project |
| 9-10 | Dynamic Programming | 30 problems + revision |
| 11-12 | Mock Interviews | 2 DSA + 2 ML system design mocks |