Prerequisites: Requires proficiency in Python programming and familiarity with fundamental machine learning concepts (e.g., supervised/unsupervised learning, basic model types).

Level: Intermediate

What You'll Learn

• 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.