01Coding Interviews for AI/MLDSA fundamentals plus NumPy, Pandas, and SQL - the coding skills that matter for ML interviews.02DSA Foundations for MLCore data structures and algorithms - arrays, hash maps, trees, graphs, sorting, and searching.03Arrays and StringsEssential array and string problems for ML coding interviews - sliding windows, two pointers, and more.04Hash Maps and SetsHash-based data structure problems - frequency counting, grouping, and lookup optimization.05Trees and GraphsTree traversals, graph algorithms, and topological sorting for ML pipeline problems.06Dynamic ProgrammingDP patterns that appear in ML coding interviews - sequence alignment, optimization, and memoization.07Sorting and SearchingSorting algorithms, binary search variants, and selection problems for data-heavy ML contexts.08NumPy for InterviewsMaster NumPy coding problems in AI interviews - vectorization, broadcasting, advanced indexing, linear algebra, and memory layout with 10 interview-style problems and full solutions.09Pandas for InterviewsMaster Pandas coding problems for data science and ML interviews - DataFrame operations, groupby, merge/join, window functions, missing data, performance optimization, and feature engineering.10SQL for ML RolesMaster SQL for ML and data science interviews - window functions, CTEs, self-joins, A/B test analysis, cohort analysis, funnel analysis, feature extraction queries, and query optimization.11Python ML CodingImplement ML algorithms from scratch in Python - linear regression, logistic regression, decision trees, KNN, K-means, PCA, gradient descent, neural networks, cross-validation, and train-test split.12Complexity AnalysisMaster time and space complexity for ML interviews - Big-O, amortized analysis, ML algorithm complexity, training vs inference costs, attention O(n^2), and common interview traps.13Mock Coding ProblemsPractice problems with solutions - curated for ML engineer and AI engineer interviews.
