Data Structures: Arrays and Linked Lists

Data Structures

Arrays

• A collection of elements of the same data type stored in contiguous memory locations
• Each element is identified by an index or key
• Operations:
  • Access: O(1) time complexity
  • Search: O(n) time complexity
  • Insert/Delete: O(n) time complexity

Linked Lists

• A dynamic collection of elements, where each element points to the next element
• Each element is a separate object, and allocation/deallocation is dynamic
• Operations:
  • Access: O(n) time complexity
  • Search: O(n) time complexity
  • Insert/Delete: O(1) time complexity

Stacks and Queues

• Stacks:
  • Last-In-First-Out (LIFO) data structure
  • Operations:
    • Push: O(1) time complexity
    • Pop: O(1) time complexity
    • Peek: O(1) time complexity
• Queues:
  • First-In-First-Out (FIFO) data structure
  • Operations:
    • Enqueue: O(1) time complexity
    • Dequeue: O(1) time complexity
    • Peek: O(1) time complexity

Trees

• A hierarchical collection of nodes, where each node has a value and zero or more child nodes
• Operations:
  • Search: O(log n) time complexity (balanced trees)
  • Insert/Delete: O(log n) time complexity (balanced trees)

Machine Learning

Supervised Learning

• The algorithm is trained on labeled data to learn the relationship between input and output
• Types:
  • Regression: predicts continuous values
  • Classification: predicts categorical values
• Algorithms:
  • Linear Regression
  • Logistic Regression
  • Decision Trees
  • Random Forest

Unsupervised Learning

• The algorithm is trained on unlabeled data to discover patterns or structure
• Types:
  • Clustering: groups similar data points together
  • Dimensionality Reduction: reduces the number of features in the data
• Algorithms:
  • K-Means Clustering
  • Hierarchical Clustering
  • Principal Component Analysis (PCA)

Neural Networks

• A model composed of interconnected nodes (neurons) that process inputs to produce outputs
• Types:
  • Feedforward Networks
  • Recurrent Neural Networks (RNNs)
  • Convolutional Neural Networks (CNNs)
• Applications:
  • Image Recognition
  • Natural Language Processing
  • Game Playing

Data Structures

Arrays

• A collection of elements of the same data type stored in contiguous memory locations
• Each element is identified by an index or key
• Accessing an element takes O(1) time complexity
• Searching for an element takes O(n) time complexity
• Inserting or deleting an element takes O(n) time complexity

Linked Lists

• A dynamic collection of elements, where each element points to the next element
• Each element is a separate object, and allocation/deallocation is dynamic
• Accessing an element takes O(n) time complexity
• Searching for an element takes O(n) time complexity
• Inserting or deleting an element takes O(1) time complexity

Stacks and Queues

Stacks

• Last-In-First-Out (LIFO) data structure
• Push operation takes O(1) time complexity
• Pop operation takes O(1) time complexity
• Peek operation takes O(1) time complexity

Queues

• First-In-First-Out (FIFO) data structure
• Enqueue operation takes O(1) time complexity
• Dequeue operation takes O(1) time complexity
• Peek operation takes O(1) time complexity

Trees

• A hierarchical collection of nodes, where each node has a value and zero or more child nodes
• Searching for a node takes O(log n) time complexity (balanced trees)
• Inserting or deleting a node takes O(log n) time complexity (balanced trees)

Machine Learning

Supervised Learning

• The algorithm is trained on labeled data to learn the relationship between input and output
• Predicts continuous values using regression
• Predicts categorical values using classification
• Algorithms include Linear Regression, Logistic Regression, Decision Trees, and Random Forest

Unsupervised Learning

• The algorithm is trained on unlabeled data to discover patterns or structure
• Groups similar data points together using clustering
• Reduces the number of features in the data using dimensionality reduction
• Algorithms include K-Means Clustering, Hierarchical Clustering, and Principal Component Analysis (PCA)

Neural Networks

• A model composed of interconnected nodes (neurons) that process inputs to produce outputs
• Types include Feedforward Networks, Recurrent Neural Networks (RNNs), and Convolutional Neural Networks (CNNs)
• Applications include Image Recognition, Natural Language Processing, and Game Playing