Lilly Tech Systems
Sorting & Searching for ML
Sorting and searching are not just textbook algorithms — they are the backbone of ranking systems, retrieval pipelines, and data processing in machine learning. This course covers every algorithm you need with full Python implementations, complexity analysis, and real ML applications.
Your Learning Path
Follow these lessons in order for a complete understanding of sorting and searching for ML, or jump to any topic that interests you.
1. Sorting & Searching in ML Context
Why sorting and searching matter for ranking, retrieval, recommendation systems, and data processing pipelines. Complexity refresher and the algorithms that power ML at scale.
2. Comparison Sorts
Full Python implementations of QuickSort, MergeSort, and HeapSort. Stability analysis, when to use each, and how they apply to sorting ML model predictions and feature arrays.
3. Non-Comparison Sorts
Counting sort, radix sort, and bucket sort with implementations. Beat O(n log n) for integer data — critical for feature binning, histogram construction, and discretization.
4. Binary Search Variants
Six problems: first/last occurrence, search insert position, peak element, rotated array search, matrix search, and threshold finding for ML calibration.
5. Top-K Problems
Five problems: kth largest element, top K frequent elements, K closest points, merge K sorted lists, and median from data stream — the core of ranking and retrieval systems.
6. Custom Sorting & Comparators
Five problems: custom sort with comparators, sort by multiple keys, merge intervals, meeting rooms scheduling, and rearrange operations for ML feature engineering.
7. Patterns & Tips
Algorithm selection guide, complexity cheat sheet, common mistakes, and frequently asked questions about sorting and searching in ML coding interviews.
What You'll Learn
By the end of this course, you will be able to:
Implement Every Sorting Algorithm
Write QuickSort, MergeSort, HeapSort, counting sort, radix sort, and bucket sort from scratch in Python with full complexity analysis.
Solve Binary Search Problems
Handle all variants: first/last occurrence, rotated arrays, matrix search, peak finding, and threshold-based search for ML calibration.
Master Top-K and Ranking
Use heaps, quickselect, and streaming algorithms to solve the top-K problems that power recommendation engines and retrieval pipelines.
Choose the Right Algorithm
Know exactly when to use which sorting or searching algorithm based on data characteristics, stability requirements, and performance constraints.