Introduction to Algorithms

Introduction to Algorithms

by Cuantum Technologies LLC
Released June 2024
Publisher(s): Packt Publishing
ISBN: 9781836203872

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Book description

Discover the fundamentals and advanced concepts of algorithms with this comprehensive course. Learn about efficiency, types, design techniques, and real-world applications, and enhance your algorithmic knowledge.

Key Features

  • Basics to advanced algorithm design and applications, along with real-world applications
  • Engaging exercises & case studies from the latest industry trends & practices for reinforcement
  • Clear, step-by-step instructions for complex and advanced topics

Book Description

Begin your journey into the fascinating world of algorithms with this comprehensive course. Starting with an introduction to the basics, you will learn about pseudocode and flowcharts, the fundamental tools for representing algorithms. As you progress, you'll delve into the efficiency of algorithms, understanding how to evaluate and optimize them for better performance. The course will also cover various basic algorithm types, providing a solid foundation for further exploration.

You will explore specific categories of algorithms, including search and sort algorithms, which are crucial for managing and retrieving data efficiently. You will also learn about graph algorithms, which are essential for solving problems related to networks and relationships. Additionally, the course will introduce you to the data structures commonly used in algorithms.

Towards the end, the focus shifts to algorithm design techniques and their real-world applications. You will discover various strategies for creating efficient and effective algorithms and see how these techniques are applied in real-world scenarios. By the end of the course, you will have a thorough understanding of algorithmic principles and be equipped with the skills to apply them in your technical career.

What you will learn

  • Understand the basics of algorithms and their significance
  • Evaluate the efficiency of different algorithms
  • Apply various types of algorithms to solve complex problems
  • Utilize graph algorithms for network-related issues
  • Implement appropriate data structures for algorithm optimization
  • Design efficient algorithms for real-world applications

Who this book is for

This course is designed for a wide range of learners, including technical professionals looking to enhance their algorithmic knowledge, computer science students seeking a deeper understanding of algorithm principles, and software developers aiming to improve their coding efficiency. Additionally, it is suitable for data scientists and analysts who need to apply algorithms to data management and analysis tasks, educators looking for comprehensive teaching material on algorithms, and hobbyists interested in expanding their technical skill set.

Table of contents

  1. Code Blocks Resource
  2. Premium Customer Support
  3. Who we are
    1. Our Philosophy
    2. Our Expertise
  4. Introduction
  5. Preface
    1. Introduction to the Book
    2. Who Should Read This Book
    3. How to Use This Book
  6. Chapter 1: Introduction to Algorithms
    1. 1.1 What is an Algorithm?
      1. 1.1.1 Characteristics of a Good Algorithm
      2. 1.1.2 How Algorithms are Used
      3. 1.1.3 Brief Summary and Some Points for Further Reflection
    2. 1.2 Importance of Algorithms in Computer Science
      1. 1.2.1 Algorithms Power Our Digital World
      2. 1.2.2 Algorithms Drive Efficiency
      3. 1.2.3 Algorithms Form the Basis of Advanced Fields
      4. 1.2.4 The Future Implications and Advancements in Algorithms
    3. 1.3 Fundamentals of Computational Thinking
      1. 1.3.1 Decomposition
      2. 1.3.2 Pattern Recognition
      3. 1.3.3 Abstraction
      4. 1.3.4 Algorithmic Thinking
      5. 1.3.5 Debugging and Iteration
    4. 1.4 Practice Problems
      1. Problem 1: Password Generator
      2. Problem 2: Calendar Events
      3. Problem 3: Building a Pyramid
      4. Problem 4: Text Compression
    5. 1.5 Chapter Summary
  7. Chapter 2: Pseudocode and Flowcharts
    1. 2.1 Understanding Pseudocode
      1. 2.1.1 Flexibility of Pseudocode
    2. 2.2 Understanding Flowcharts
    3. 2.3 Translating Real-World Problems into Pseudocode
    4. 2.4 Practice Problems
      1. Problem 1
      2. Problem 2
      3. Problem 3
    5. Chapter 2 Summary
  8. Chapter 3: Algorithm Efficiency
    1. 3.1 Understanding Time Complexity
      1. 3.1.1 Big O notation
      2. 3.1.2 Difference between the best-case, average-case, and worst-case time complexity
    2. 3.2 Understanding Space Complexity
      1. 3.2.1 Caching/Memoization
    3. 3.3 Introduction to Big O Notation
      1. 3.3.1 What is Big O Notation?
      2. 3.3.2 Common Types of Time Complexities
      3. 3.3.3 Asymptotic Analysis
    4. 3.4 Practice Problems
      1. Linear Search:
      2. Sum of Elements:
      3. Find Duplicate:
      4. Bubble Sort:
    5. Chapter 3 Summary
  9. Chapter 4: Basic Algorithm Types
    1. 4.1 Divide and Conquer Algorithms
    2. 4.2 Greedy Algorithms
      1. 4.2.1 What is a Greedy Algorithm?
      2. 4.2.2 Coin Change Problem
    3. 4.3 Dynamic Programming Algorithms
    4. 4.4 Recursive Algorithms
      1. 4.4.1 Tail Recursion
    5. 4.5 Practice Problems
      1. Problem 1: Binary Search (Divide and Conquer)
      2. Problem 2: Coin Change (Greedy Algorithm)
      3. Problem 3: Fibonacci Series (Dynamic Programming)
      4. Problem 4: Sum of Natural Numbers (Recursive Algorithm)
      5. Problem 5: QuickSort (Divide and Conquer)
      6. Problem 6: Implementing a Stack using Recursion (Recursive Algorithm)
    6. Chapter 4 Summary
  10. Chapter 5: Search Algorithms
    1. 5.1 Linear Search
      1. 5.1.1 Limitations of Linear Search
    2. 5.2: Binary Search
    3. 5.3 Hashing and Hash Tables
      1. 5.3.1 Collisions
    4. 5.4 Practice Problems
      1. Problem 1: Linear Search
      2. Problem 2: Binary Search
      3. Problem 3: Hashing
      4. Problem 4: Binary Search vs Linear Search
    5. Chapter 5 Summary
  11. Chapter 6: Sort Algorithms
    1. 6.1 Bubble Sort
      1. 6.1.1 When and Why to Use or Not to Use Bubble Sort
    2. 6.2 Selection Sort
    3. 6.3 Insertion Sort
    4. 6.4 Quick Sort
    5. 6.5 Merge Sort
    6. 6.6 Heap Sort
    7. 6.7 Practice Problems
      1. 1. Implement Bubble Sort
      2. 2. Analyze Quick Sort's Worst Case
      3. 3. Merge Sort with Linked Lists
      4. 4. Heapify an Array
      5. 5. Stability in Sorting
    8. Chapter 6 Summary
  12. Chapter 7: Graph Algorithms
    1. 7.1 Introduction to Graph Theory
    2. 7.2 Depth-First Search
    3. 7.3 Breadth-First Search
      1. 7.3.1 BFS Time Complexity
    4. 7.4 Dijkstra's Algorithm
    5. 7.5 A* Search
      1. 7.5.1 Heuristics in A*
      2. 7.5.2 Time and Space Complexity
      3. 7.5.3 Optimality of A Search
      4. 7.5.4 Real-world Applications of A Search*
      5. 7.5.5 Variations of A Search*
      6. 7.5.6 Complexity of A Search*
    6. 7.6 Practice Problems
      1. Problem 1: DFS in Maze Solving
      2. Problem 2: Shortest Path in a Grid with BFS
    7. Chapter 7 Summary
  13. Chapter 8: Data Structures Used in Algorithms
    1. 8.1 Arrays
      1. 8.1.1 Properties and Common Uses of Arrays
    2. 8.2 Linked Lists
      1. 8.2.1 Other Types of Linked Lists
    3. 8.3 Stacks and Queues
      1. 8.3.1. Stacks
      2. 8.3.2 Queues
      3. 8.3.3 Priority Queues and Dequeues
    4. 8.4 Trees and Graphs
      1. 8.4.1 Trees
      2. 8.4.2 Graphs
      3. 8.4.3 Going Deeper
    5. 8.5 Practice Problems
      1. Problem 1: Arrays - Maximum Subarray Sum
      2. Problem 2: Linked Lists - Reverse a Linked List
      3. Problem 3: Stacks - Valid Parentheses
      4. Problem 4: Trees - Maximum Depth of Binary Tree
    6. Chapter 8 Summary
  14. Chapter 9: Algorithm Design Techniques
    1. 9.1 Recursion
    2. 9.2 Iterative Approaches
      1. 9.2.1 Iterative Factorial Calculation
      2. 9.2.2 The Tail Recursion Optimization
    3. 9.3 Backtracking
    4. 9.4 Branch and Bound
      1. 9.4.1 Working Principle of Branch and Bound
      2. 9.4.2 The Travelling Salesman Problem
    5. 9.5 Practical Problems
      1. 1. Recursion: Fibonacci Series
      2. 2. Iterative Approach: Factorial
      3. 3. Backtracking: N-Queens Problem
      4. 4. Branch and Bound: Travelling Salesman Problem
    6. Chapter 9 Summary
  15. Chapter 10: Real World Applications of Algorithms
    1. 10.1 Algorithms in Databases
    2. 10.2 Algorithms in Artificial Intelligence
      1. 10.2.1 Machine Learning Algorithms
      2. 10.2.2 Search Algorithms
      3. 10.2.3 How Algorithms Can Power NLP in AI
      4. 10.2.4 Role of Algorithms in Machine Learning
    3. 10.3 Algorithms in Network Routing
      1. 10.3.1 Dijkstra’s Algorithm
      2. 10.3.2 Bellman-Ford Algorithm
      3. 10.3.3 Link State Routing Protocol (LSRP)
    4. 10.4 Practice Problems
      1. Problem 1: Algorithms in Databases
      2. Problem 2: Algorithms in Artificial Intelligence
      3. Problem 3: Algorithms in Network Routing
    5. Chapter 10 Summary
  16. Conclusion
  17. Where to continue?
  18. Know more about us

Product information

  • Title: Introduction to Algorithms
  • Author(s): Cuantum Technologies LLC
  • Release date: June 2024
  • Publisher(s): Packt Publishing
  • ISBN: 9781836203872