- Chapter 1 – Introduction to Algorithms: Learn what algorithms are, their broad utilities, and the basics of algorithm complexity.
- Chapter 2 – 4 Analysis of Algorithms: Including chapters on Time Complexity, Space Complexity, and Asymptotic Notation.
- Chapter 5 – Recursive Algorithms: Understand the concept of recursion in algorithms, and its pros and cons.
- Chapter 6 – 8 – Sorting Algorithms: Delve into popular sorting algorithms like Bubble Sort, Merge Sort, and Quick Sort with code examples.
- Chapter 9 – 11 – Searching Algorithms: Explore conventional searching algorithms including Linear Search, Binary Search, and Hashing.
- Chapter 12 – 14 – Graph Algorithms: Learn the concept of graphs and basics of graph traversal techniques: Depth-First Search and Breadth-First Search.
- Chapter 15 – 17 – Shortest Path Algorithms: Understand the algorithms used for finding shortest paths in graphs, including Dijkstra’s algorithm and Bellman-Ford algorithm.
- Chapter 18 – 20 – Dynamic Programming: Discuss the principles of dynamic programming, with common examples like the knapsack problem, fib sequence, minimum coin change problem.
- Chapter 21 – 23 – String Matching Algorithms: Explain how string matching is done using algorithms like the Knuth-Morris-Pratt algorithm and Rabin-Karp algorithm.
- Chapter 24 – 26 – Tree Algorithms: Learn about trees and tree algorithms, including Binary Trees, Tree Traversal, and Binary Search Trees.
- Chapter 27 – Algorithm Design Techniques: Get introduced to popular algorithm design techniques such as Divide and Conquer, Greedy Algorithms, and Backtracking.
- Chapter 28 – Real-world Applications of Algorithms: Look at real-world applications of algorithms across various sectors, and consider the ethical implications.
By the end of this course, you will be well-versed in the language of algorithms, enhancing your ability to decode our digital experiences’ invisible drivers. Get ready to make the invisible visible!