Published 11/2025
Created by Norbert Grover
MP4 | Video: h264, 1280×720 | Audio: AAC, 44.1 KHz, 2 Ch
Level: All | Genre: eLearning | Language: English | Duration: 19 Lectures ( 2h 19m ) | Size: 1.5 GB
Learn to build dynamic sliding window and two-pointer solutions for real-world coding problems and technical interviews.
What you’ll learn
Analyze and apply adaptive sliding window techniques to solve dynamic subarray and substring problems efficiently.
Identify when to use fixed-size versus adaptive sliding window patterns in coding challenges.
Construct algorithms using two-pointer strategies that don’t rely on traditional window structures.
Solve problems involving longest or shortest subarrays that meet complex conditions (e.g. sum ≥ target, exactly K distinct values).
Use frequency maps and hash-based counters to manage dynamic constraints within a moving window.
Implement in-place array and string manipulation using pointer movement and condition tracking.
Debug and visualize adaptive window logic using print tracing and incremental analysis.
Apply prefix sum and cumulative frequency techniques to count or optimize subarray conditions.
Build reusable templates for window-shrinking problems involving constraints like duplicates or target counts.
Prepare for technical interviews with confidence by mastering advanced pointer and loop control patterns.
Requirements
Basic understanding of variables, loops, and conditionals in any programming language.
Familiarity with Swift (or another modern language) and how functions, arrays, and dictionaries work.
Comfort with writing and running simple algorithms in an IDE or code editor.
Basic problem-solving experience on platforms like LeetCode, HackerRank, or CodeSignal.
Understanding of arrays, strings, and hash maps as data structures.
Ability to trace code using print statements or a debugger to follow variable states.
Familiarity with the sliding window pattern at a basic, fixed-size level.
Some exposure to two-pointer techniques, even if not fully mastered.
Experience reading or implementing simple algorithms with time and space complexity in mind.
Willingness to break down problems into smaller parts and iterate on a solution.
Description
If you’ve ever worked with the sliding window technique and felt like the “fixed-size” window explanations were just scratching the surface, you’re not alone — and you’re exactly who this course is for.Beyond Fixed Windows: Adaptive Sliding Algorithms is the first comprehensive course to focus specifically on the inner mechanics of adaptive sliding windows and pointer manipulation in iterative structures. While most resources only teach the basic fixed-size window template, this course takes you deeper — exploring how dynamic, condition-driven windows work in real coding interviews and algorithm design.What You’ll LearnThe fundamentals of adaptive (dynamic-size) sliding window problemsHow to manage window boundaries based on conditions (e.g. distinct elements, sum constraints, frequency maps)Mastery of two-pointer approaches that don’t involve explicit windowingReal-world patterns like:Longest/Shortest subarray that meets a conditionSubstring problems with at most or exactly K distinct elementsMinimum window problems (e.g. “smallest substring containing a pattern”)Counting and optimization strategies using prefix sums and hash mapsIn-place operations and performance optimization techniquesDebugging adaptive window logic through visual walkthroughs and print tracingHow interviewers expect you to adapt base patterns to new constraintsWhy This Course Is UniqueThis is not a survey course that glosses over templates. Instead, you’ll dive deeply into how adaptive windows actually work, why they break, how to fix them, and how to apply the logic to new problems. No other course provides such an intensive breakdown of pointer-driven loop behavior in the context of algorithmic problem solving.By the end of the course, you won’t just recognize the patterns — you’ll be able to build your own adaptive solutions from scratch with full control over correctness, efficiency, and clarity.
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