Beginner

How to Use This Course

This course covers the 50 LeetCode problems most frequently asked at AI/ML companies. Before you start solving problems, read this lesson to understand the study strategy, time targets, and pattern recognition approach that will maximize your preparation efficiency.

Why These 50 Problems?

We analyzed thousands of interview reports from Google AI, Meta AI, OpenAI, DeepMind, Amazon ML, Apple ML, and other top AI/ML companies. These 50 problems represent the core patterns that appear over and over. Mastering them gives you coverage of roughly 80% of coding questions you will face in AI/ML interviews.

AI/ML companies test the same data structures and algorithms as other tech companies, but they weight certain topics more heavily. Graph algorithms matter more (think knowledge graphs, GNNs), array/matrix operations are central (tensors are multi-dimensional arrays), and design problems often involve caching and scheduling (model serving, training job orchestration).

The Pattern Recognition Approach

Memorizing solutions does not work. There are thousands of LeetCode problems, and interviewers frequently modify problems to test understanding. Instead, learn to recognize the underlying pattern:

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The Pattern Method: When you see a new problem, ask: (1) What data structure fits the constraints? (2) What algorithmic pattern does this match? (3) Have I solved a problem with the same structure before? This three-question framework lets you solve problems you have never seen.

The 8 Core Patterns

Every problem in this course maps to one of these patterns:

  1. Hash Map / Set — O(1) lookups to avoid nested loops. Used in: Two Sum, Group Anagrams, Contains Duplicate, Subarray Sum K.
  2. Two Pointers — Scan from both ends or use fast/slow pointers. Used in: 3Sum, Container With Most Water, Valid Palindrome, Linked List Cycle.
  3. Sliding Window — Maintain a window over a sequence. Used in: Longest Substring Without Repeating, Minimum Window Substring, Sliding Window Maximum.
  4. Stack / Queue — LIFO/FIFO ordering for nested structures. Used in: Valid Parentheses, Min Stack, Trapping Rain Water, Largest Rectangle.
  5. Tree / Graph Traversal — BFS and DFS on trees and graphs. Used in: Level Order, Number of Islands, Course Schedule, Word Ladder.
  6. Dynamic Programming — Optimal substructure with overlapping subproblems. Used in: Climbing Stairs, Coin Change, Word Break, Edit Distance.
  7. Binary Search — Divide search space in half. Used in: Median of Two Sorted Arrays, Kth Smallest in BST.
  8. Heap / Greedy — Maintain order or make locally optimal choices. Used in: Merge K Lists, Top K Frequent, Meeting Rooms II, Task Scheduler.

Time Targets by Difficulty

In a real interview, you have 45 minutes per problem (including discussion). Here are practice targets:

DifficultyTarget TimeWhat Interviewers Expect
Easy10-15 minutesSolve quickly and cleanly. Discuss edge cases. These are warmups.
Medium20-25 minutesIdentify the pattern, code the solution, handle edge cases. Most interview questions are medium.
Hard30-40 minutesDemonstrate problem-solving process. Partial solutions are acceptable. Show your thinking.

How to Study Each Problem

For every problem in this course, follow this five-step process:

  1. Read the problem — Understand inputs, outputs, and constraints. Identify the pattern before writing any code.
  2. Attempt it yourself — Spend 15-20 minutes trying before looking at the solution. Struggle is where learning happens.
  3. Study the solution — Read the solution line by line. Make sure you understand why each line exists, not just what it does.
  4. Rewrite from memory — Close the solution and rewrite it. If you cannot, you do not understand it yet.
  5. Review after 2-3 days — Spaced repetition is the key to long-term retention. Revisit problems at increasing intervals.

ML Context: Why AI Engineers Need DSA

You might wonder: "I build models, why do I need to know Two Sum?" Here is why these patterns matter in real AI/ML work:

  • Hash maps power feature stores and embedding lookups in recommendation systems.
  • Graph traversal is the foundation of knowledge graphs, GNNs, and computational graphs (PyTorch autograd).
  • Dynamic programming appears in sequence alignment (bioinformatics), Viterbi decoding (HMMs), and CTC loss computation.
  • Heaps and scheduling manage GPU job queues, batch processing priority, and model serving request routing.
  • Sliding windows are used in time-series analysis, attention mechanisms, and streaming data processing.
  • Binary search optimizes hyperparameter tuning and threshold selection in classification.

Course Structure

The course is organized by difficulty and topic:

  • Lesson 2: Easy (1-10) — Build confidence with foundational problems. Every AI engineer should solve these in under 15 minutes.
  • Lessons 3-5: Medium (11-40) — The core of the course. Grouped by topic: arrays/strings, trees/graphs, DP/design.
  • Lesson 6: Hard (41-50) — Advanced problems that test deep understanding. Focus on the problem-solving process.
  • Lesson 7: Pattern Guide — A reference mapping every problem to its pattern with quick identification tips.
  • Lesson 8: Study Plan — A complete 4-week plan with daily targets and spaced repetition schedule.

Key Takeaways

  • Focus on pattern recognition over memorization. Learn 8 patterns, solve 50 problems, handle thousands of variations.
  • Practice with time targets: 15 minutes for easy, 25 for medium, 40 for hard.
  • Follow the five-step process for each problem: read, attempt, study, rewrite, review.
  • These problems were selected specifically for AI/ML company interviews based on real interview data.
  • Every algorithm in this course has a direct connection to real AI/ML systems.