Lilly Tech Systems
Python DSA for AI Engineers
Master data structures and algorithms with real coding problems tailored for AI/ML engineering interviews. Every problem includes brute force and optimal solutions with full time/space complexity analysis, plus context on why each pattern matters for AI work.
Your Learning Path
Follow these lessons in order to build a strong DSA foundation for AI engineering interviews, or jump to any topic you need to practice.
1. DSA for ML Engineers
Why DSA matters for AI roles, what top companies test, Python-specific tips, and how to approach problems systematically during interviews.
2. Arrays & Strings
6 problems: two sum, max subarray, rotate array, string manipulation, anagram detection, and sliding window maximum — with brute force to optimal progression.
3. Hash Maps & Sets
6 problems: frequency counting, group anagrams, two sum variants, intersection, LRU cache, and word frequency in corpus — essential for data pipeline work.
4. Stacks & Queues
5 problems: valid parentheses, min stack, queue using stacks, monotonic stack, and next greater element — core patterns for expression parsing and BFS.
5. Linked Lists
5 problems: reverse list, detect cycle, merge sorted lists, remove nth from end, and intersection point — pointer manipulation mastery.
6. Binary Search Patterns
6 problems: classic binary search, rotated array, find peak, 2D matrix search, median of sorted arrays, and threshold finding for hyperparameter tuning.
7. Greedy Algorithms
5 problems: interval scheduling, task scheduler, jump game, minimum platforms, and activity selection — optimization patterns used in scheduling and resource allocation.
8. Practice Strategy & Tips
Time management strategies, common patterns cheat sheet, FAQ accordion, and a structured practice plan to maximize your interview readiness.
What You'll Learn
By the end of this course, you will be able to:
Solve DSA Problems in Python
Master arrays, hash maps, linked lists, stacks, binary search, and greedy algorithms with clean Pythonic solutions that interviewers love.
Analyze Time & Space Complexity
Determine Big-O for every solution. Understand brute force vs. optimal trade-offs and articulate why one approach is better during interviews.
Connect DSA to AI/ML Work
Understand how data structures power real ML systems — from hash maps in feature stores to binary search in hyperparameter tuning.
Ace Technical Interviews
Build muscle memory for the 33+ most common coding problems asked at Google, Meta, Amazon, and AI startups for ML engineering roles.