About this course
This course empowers you to discover the building blocks of modern computing. Through guided problem-solving and relatable analogies, you’ll learn why arrays, trees, and dynamic programming matter—from powering search engines to enabling AI. Ask yourself, “How could I have developed this solution myself?” and build a deep, intuitive understanding of algorithms.
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Learn the fundamentals of Big-O notation and its practical implications.
Compare linear, logarithmic, and exponential growth with practical examples.
Examine how different cases affect algorithm performance in real applications.
Apply complexity concepts to measure and compare algorithm efficiency.
Understand why efficiency matters and get introduced to performance measurement.
Analyze case studies to see how efficiency impacts large-scale systems.
Learn the basics of static arrays and their applications.
Understand dynamic resizing and performance trade-offs.
Explore the design and use of singly linked lists.
Compare the structures and performance of different linked lists.
Learn stack operations and applications in function calls.
Study FIFO structures and their use in scheduling.
Understand how heaps underpin efficient priority queues
Learn hash table design and strategies to manage collisions.
Recap key data structures and compare their performance characteristics.
Description: Introduction to recursive thinking and its advantages.
Description: Learn to follow recursive calls and understand call stacks
Description: Understand the importance of base cases in recursion.
Description: Use recursion trees to analyze algorithm behaviour
Description: Learn the key idea of breaking problems into subproblems.
Apply divide and conquer with merge sort.
Study quick sort and its partitioning strategy.
Evaluate time complexity using recursion trees and the Master Theorem
Address common pitfalls and limitations of recursive solutions.
Description: Review case studies where recursion is effectively applied.
Understand the basics of tree data structures and their applications.
Learn preorder, inorder, and postorder traversals.
Study AVL and Red-Black Trees for efficient searching.
Introduce graph representations and basic properties.
Learn BFS for level-order graph traversal.
Explore DFS and its variations for graph traversal.
Study Dijkstra’s and Bellman-Ford algorithms.
Analyze how graphs power social networks and navigation systems.
Explore the principles behind making locally optimal choices.
Examine real-world examples where greedy strategies succeed.
Understand the need for memorization in complex problems.
Learn how to store intermediate results for efficiency.
Discover bottom-up dynamic programming approaches.
Analyze the strengths and weaknesses of both strategies.
Solve the knapsack problem using dynamic programming.
Implement the LCS problem and discuss its applications.
Delve into optimization techniques within dynamic programming.
Description: Review case studies highlighting dynamic programming in industry.
Overview of recent innovations and complex algorithmic techniques.
Study new trends and methodologies in algorithm design.
Learn algorithms behind data compression and efficient storage.
Analyze how search engines process and rank data efficiency
Explore how advanced algorithms are applied in AI and ML.
Study practical methods for optimizing algorithm performance.
Review how major companies implement advanced algorithms in real systems.
Summarize key learnings and discuss emerging trends in algorithms.
