Learn how fundamental data structures and algorithms are applied to build efficient and scalable machine learning models. This course covers the practical implementation and performance implications of trees, graphs, hashing, and core algorithmic techniques within the context of common machine learning tasks. Enhance your ability to select and implement appropriate structures for data representation, feature engineering, model training, and optimization.
Prerequisites Python programming, DSA concepts
Level:
Performance Analysis
Analyze the time and space complexity of algorithms and data structures commonly used in ML pipelines.
Data Representation
Select appropriate data structures (arrays, trees, graphs, hash tables) for efficient data storage and manipulation in ML.
Algorithm Implementation
Implement core algorithms (searching, sorting, graph traversal, hashing) relevant to ML problems.
ML Model Optimization
Understand how DSA choices impact the performance of ML model training and inference.
Feature Engineering Techniques
Apply hashing and other techniques for effective feature representation, especially with large or sparse datasets.
Nearest Neighbor Search
Implement and understand algorithms for efficient nearest neighbor searches in high-dimensional spaces.