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Study Plan & Tips

A structured 4-week plan to master all 50 problems, a spaced repetition schedule to ensure long-term retention, and answers to the most common questions about AI/ML coding interviews.

The 4-Week Study Plan

This plan assumes 1.5-2 hours of daily practice. Adjust the pace based on your schedule, but maintain the order and review days.

Week 1: Foundation (Easy + Array/String Medium)

Day	Problems	Focus
Day 1	1-3 (Two Sum, Valid Parentheses, Merge Sorted Lists)	Hash map, stack, two pointers basics
Day 2	4-6 (Buy/Sell Stock, Valid Palindrome, Linked List Cycle)	Track min, two pointers, fast/slow
Day 3	7-10 (Climbing Stairs, Inorder, Max Subarray, Contains Dup)	DP intro, tree traversal, Kadane's
Day 4	11-13 (3Sum, Container Water, Longest Substring)	Sort + two pointers, sliding window
Day 5	14-16 (Product Array, Group Anagrams, Merge Intervals)	Prefix/suffix, hash map grouping, sort + scan
Day 6	17-20 (Sort Colors, Subarray Sum K, Rotate, Spiral)	Partition, prefix sum, in-place tricks
Day 7	Review Day 1-3 problems	Rewrite from memory; time yourself

Week 2: Trees, Graphs, and DP

Day	Problems	Focus
Day 8	21-23 (Level Order, Validate BST, Islands)	BFS, DFS with range, flood fill
Day 9	24-26 (Course Schedule, Word Search, Clone Graph)	Topological sort, backtracking, graph copy
Day 10	27-30 (Tree Paths, Kth Smallest, Serialize, LCA)	Tree recursion patterns
Day 11	31-33 (Coin Change, Word Break, House Robber)	1D DP fundamentals
Day 12	34-35 (Unique Paths, Decode Ways)	2D DP and Fibonacci variants
Day 13	36-37 (LRU Cache, Min Stack)	Design with composite data structures
Day 14	Review Day 4-10 problems	Spaced repetition review

Week 3: Advanced Problems

Day	Problems	Focus
Day 15	38-40 (Top K, Meeting Rooms II, Task Scheduler)	Heap, greedy, scheduling
Day 16	41-42 (Median Two Arrays, Merge K Lists)	Binary search, heap with k elements
Day 17	43-44 (Trapping Rain Water, Longest Palindrome)	Two pointers, expand around center
Day 18	45-46 (Edit Distance, Word Ladder)	2D DP, BFS on implicit graph
Day 19	47-48 (Sliding Window Max, Largest Rectangle)	Monotonic deque and stack
Day 20	49-50 (Regex Matching, Min Window Substring)	2D DP, advanced sliding window
Day 21	Review Day 11-20 problems	Spaced repetition review

Week 4: Review and Mock Interviews

Day	Activity	Focus
Day 22	Full review: Easy problems (1-10)	Solve all 10 in under 2 hours timed
Day 23	Full review: Medium Arrays/Strings (11-20)	Focus on problems you struggled with
Day 24	Full review: Medium Trees/Graphs (21-30)	Whiteboard practice (no IDE)
Day 25	Full review: Medium DP/Design (31-40)	Practice explaining your approach aloud
Day 26	Full review: Hard problems (41-50)	Focus on problem-solving process, not perfection
Day 27	Mock interview: 2 random problems, 45 min each	Simulate real interview conditions
Day 28	Final review of weakest patterns	Fill gaps; rest before real interviews

Spaced Repetition Schedule

Research shows that reviewing material at increasing intervals maximizes long-term retention. After solving a problem, review it at these intervals:

Review	When	What to Do
1st review	Next day	Rewrite the solution from memory. If you cannot, study it again.
2nd review	3 days later	Write the solution without looking. Should take under target time.
3rd review	1 week later	Solve with just the problem statement. Explain the approach aloud.
4th review	2 weeks later	Quick review: can you identify the pattern and write the key lines in 2 minutes?

Interview Day Tips

1. Clarify before coding — Ask about edge cases, input constraints, and expected output format. This shows thoroughness and prevents mistakes.
2. State the pattern — Say "This looks like a sliding window problem because..." Interviewers want to see your thought process.
3. Start with brute force — Describe the naive approach and its complexity first, then optimize. Never jump to the optimal solution without acknowledging alternatives.
4. Talk while coding — Narrate what you are doing. Silent coding makes it impossible for the interviewer to help you or assess your thinking.
5. Test with examples — Walk through your code with the given example before claiming it works. Catch bugs during dry run, not after submission.
6. Discuss complexity — Always state time and space complexity without being asked. This demonstrates completeness.

AI/ML-Specific Interview Tips

• Connect to ML systems — When discussing your solution, mention where the pattern appears in ML. "This hash map approach is similar to how feature stores provide O(1) lookup during inference."
• Know Python idioms — AI/ML interviews are almost always in Python. Know collections (Counter, defaultdict, deque), heapq, itertools, and functools.
• Expect follow-ups about scale — "What if the data does not fit in memory?" "How would you parallelize this?" These are common at AI companies.
• Be ready for ML + coding combos — Some interviews mix coding with ML questions: "Implement a function to compute precision@k" or "Write an efficient nearest neighbor search."

Frequently Asked Questions

How many problems should I solve per day?

Quality over quantity. Solve 2-3 new problems per day and review 2-3 old ones. A deep understanding of 50 problems is far more valuable than a shallow exposure to 500. Each problem should be solved, understood, and reviewed multiple times.

Should I solve problems on LeetCode or just read the solutions here?

Both. Use this course to learn the pattern and study the solution. Then go to LeetCode and solve the problem yourself in their coding environment. The act of typing the solution, handling edge cases, and getting it accepted is an essential part of learning.

What language should I use for AI/ML interviews?

Python is the standard for AI/ML roles. All solutions in this course are in Python. Interviewers expect you to know Python's standard library (collections, heapq, itertools). If you are interviewing for an ML infrastructure role, C++ may also be tested, but Python is always acceptable.

How are AI/ML coding interviews different from general SWE interviews?

The core coding questions are similar, but AI/ML interviews may also include: (1) ML-specific coding (implement gradient descent, write a custom loss function), (2) data processing (pandas, SQL), (3) system design for ML systems, and (4) probability and statistics questions. This course covers the coding portion, which is typically 1-2 of the 4-5 interview rounds.

What if I cannot solve a problem after 20 minutes?

Look at the hint (pattern name) first, then try again for 10 minutes. If you still cannot solve it, study the solution thoroughly, then close it and rewrite from memory. There is no shame in studying solutions — the goal is to learn the pattern, not to reinvent algorithms under time pressure. Come back to the problem in 2-3 days.

Should I memorize solutions?

No. Memorize patterns, not solutions. If you understand that "Trapping Rain Water" is a two-pointer problem where you track left_max and right_max, you can derive the solution. If you memorize the code without understanding why, a slight variation will stump you. Focus on the "why" behind each approach.

How important are hard problems?

Medium problems are the most important — they account for about 60-70% of interview questions. Easy problems should be warmups you can solve quickly. Hard problems are asked less often, and interviewers usually do not expect perfect solutions. What matters most for hard problems is demonstrating clear problem-solving process: breaking the problem down, considering multiple approaches, and making progress even if you do not reach the optimal solution.

What if I am short on time and cannot do the full 4-week plan?

If you only have 2 weeks, focus on: (1) All easy problems (Day 1-3), (2) Medium arrays and trees (Day 4-10), (3) The most important mediums: Two Sum, 3Sum, Number of Islands, Course Schedule, Coin Change, LRU Cache, Top K Frequent, and (4) Review. Skip hard problems if necessary — medium mastery is more valuable than partial hard problem coverage.

Key Takeaways

• Follow the 4-week plan for structured preparation. Adjust the pace but maintain the review days.
• Use spaced repetition: review at 1 day, 3 days, 1 week, and 2 weeks after first solving each problem.
• Focus on medium problems — they account for 60-70% of interview questions.
• Quality over quantity: deep understanding of 50 problems beats shallow exposure to 500.
• On interview day: clarify, state the pattern, start with brute force, talk while coding, test with examples, discuss complexity.
• Connect solutions to ML systems to stand out in AI/ML interviews.