1.1.1 Algorithms & Problems
Develop strong problem-solving skills and learn the fundamental data structures and algorithms that power AI systems. This section focuses on practical applications in machine learning and AI development.
What You'll Learn
- Array & String Algorithms: Manipulation and optimization techniques
- Linked Lists: Implementation and applications in AI data processing
- Stacks & Queues: Essential for parsing and graph algorithms
- Trees & Graphs: Critical for decision trees, neural networks, and search algorithms
- Hash Tables: Fast lookups for feature engineering and caching
- Sorting & Searching: Optimization techniques for large datasets
- Dynamic Programming: Optimization problems in AI and ML
- Graph Algorithms: Pathfinding, clustering, and network analysis
AI-Focused Applications
- Decision Trees: Tree data structures in machine learning
- Graph Neural Networks: Graph algorithms for deep learning
- Feature Hashing: Hash tables for high-dimensional data
- Optimization: Dynamic programming in neural network training
- Search Algorithms: A*, beam search for AI applications
Prerequisites
- Python programming fundamentals
- Basic mathematics (algebra and logic)
Learning Path
- Complexity Analysis - Big O notation and performance analysis
- Arrays & Strings - Fundamental data manipulation
- Linked Lists - Dynamic data structures
- Stacks & Queues - LIFO and FIFO data structures
- Trees - Binary trees, BSTs, and tree traversals
- Graphs - Representation and traversal algorithms
- Sorting & Searching - Efficient algorithms and optimization
- Dynamic Programming - Advanced optimization techniques
- AI Applications - Applying DSA concepts to machine learning
Build the algorithmic thinking skills essential for AI engineering!