AI Coding Exam Prep
Master coding challenges for AI/ML interviews. From DSA fundamentals to ML algorithm implementation, PyTorch challenges to timed mock exams.
All Courses
20 comprehensive courses covering coding challenges for AI/ML interviews.
DSA Fundamentals
Python DSA for AI Engineers
Master Python data structures and algorithms tailored for AI engineering roles and coding interviews.
8 LessonsTrees & Graphs for AI
Binary trees, BSTs, graph traversals, and tree-based algorithms essential for AI coding rounds.
8 LessonsDynamic Programming Patterns
Memoization, tabulation, and classic DP patterns including knapsack, LCS, LIS, and matrix chain.
8 LessonsSorting & Searching for ML
Binary search variations, merge sort, quicksort, and search algorithms applied to ML data problems.
7 LessonsPattern-Based Problems
Sliding Window & Two Pointers
Fixed and variable window techniques, two-pointer patterns for sorted arrays and substring problems.
7 LessonsBacktracking & Recursion
Permutations, combinations, N-Queens, Sudoku solver, and pruning strategies for efficient solutions.
7 LessonsHeap & Priority Queue
Min/max heaps, priority queues, top-K problems, and median-finding algorithms for interviews.
7 LessonsLinkedList & Stack
Linked list manipulation, stack-based problems, monotonic stacks, and classic interview patterns.
7 LessonsML-Specific Coding
NumPy Coding Challenges
Array operations, broadcasting, vectorization, and numerical computing challenges with NumPy.
8 LessonsPandas Coding Challenges
Data manipulation, aggregation, merging, and real-world data wrangling challenges with pandas.
8 LessonsSQL Coding Challenges
Complex queries, window functions, CTEs, and data analysis SQL problems for ML interviews.
8 LessonsPyTorch Coding Challenges
Tensor operations, model building, training loops, and deep learning implementation challenges.
8 LessonsAlgorithm Implementation
ML Algorithm Implementation
Implement ML algorithms from scratch: linear regression, decision trees, KNN, SVM, and more.
8 LessonsMath & Linear Algebra Coding
Matrix operations, eigenvalues, SVD, and numerical methods implemented in Python for ML.
7 LessonsProbability & Statistics Coding
Implement probability distributions, hypothesis tests, Bayesian methods, and statistical algorithms.
7 LessonsPractice & Mock Exams
LeetCode Top 50 for AI
Curated top 50 LeetCode problems most frequently asked in AI/ML engineering interviews.
8 LessonsData Pipeline Challenges
Build ETL pipelines, streaming processors, and data transformation challenges for ML systems.
7 LessonsAPI & System Challenges
Design and implement REST APIs, microservices, and system-level coding challenges.
7 LessonsCompetitive Programming
Contest strategies, speed coding, problem classification, and optimization for AI competitions.
7 LessonsTimed Coding Practice
Simulate real interview conditions with timed coding sessions and mock exam scenarios.
8 LessonsWhat You'll Learn
Skills you will gain across these 20 courses.
Master DSA for AI
Build a strong foundation in data structures and algorithms with Python, tailored for AI/ML engineering interviews and coding rounds.
Implement ML Algorithms
Code machine learning algorithms from scratch, including linear models, trees, clustering, and neural networks with NumPy and PyTorch.
Solve Data Challenges
Tackle real-world data manipulation problems using pandas, SQL, and NumPy that mirror actual interview coding assessments.
Excel Under Pressure
Practice with timed mock exams and competitive programming challenges to build speed, accuracy, and confidence for interview day.
AI Coding Exam is built around the specific pattern library that AI-leaning coding interviews pull from. Classical data structures and algorithms still come up, but alongside them you now see problems that probe for tensor operations, sliding-window attention math, vectorized NumPy thinking, and the kind of pragmatic optimization that shows whether a candidate can actually ship an AI system rather than just talk about one.
Each topic in the track includes a progression from easy to hard, a discussion of the pattern being tested, and a written trace through how a strong candidate would approach the problem out loud. We emphasize speaking-your-work practice because that is what most candidates lose points on, not the final answer. The goal is that after working through this track, a reader walks into the whiteboard calmer, faster, and with a clearer sense of what the interviewer is measuring.