**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!