Recursion, Backtracking and Dynamic Programming in Python

🚀 Course Title: Recursion, Backtracking and Dynamic Programming in Python 🎓 Course Headline: Master Competitive Programming with Python!

Welcome to the World of Algorithms!

This comprehensive course dives deep into the core concepts of algorithmic problem-solving through Python, focusing on Recursion, Backtracking, Dynamic Programming, and Divide and Conquer methods. These techniques are pivotal in fields such as software engineering, investment banking, and R&D, making them essential skills for anyone looking to excel in competitive programming or enhance their coding prowess.

🔄 Section 1 - RECURSION

• What is Recursion and Recursive Methods?
• Understanding Stack Memory and Heap Memory.
• Preventing Stack Overflow: Learning what it is and how to avoid it.
• Efficiently Calculating Fibonacci Numbers.
• Exploring the Factorial Function.
• Solving the Tower of Hanoi Problem.

⚔️ Section 2 - SEARCH ALGORITHMS

• Linear Search: Basic approach to searching in a list.
• Binary Search: Optimizing search with sorted data.

🏆 Section 3 - SELECTION ALGORITHMS

• Deep Dive into Selection Algorithms.
• Implementing Hoare's Algorithm.
• Finding the k-th Order Statistics in Linear Time (Quickselect Algorithm).
• Solving the Median of Medians Problem.
• Exploring the Secretary Problem.

🖱️ Section 4 - BIT MANIPULATION PROBLEMS

• Binary Numbers and Logical Operations.
• Efficient Number Checking (Even and Odd).
• Solving Bit Length Problems.
• Russian Peasant Multiplication: A fast multiplication technique.

✋️ Section 5 - BACKTRACKING

• Understanding Backtracking Algorithms.
• N-Queens Problem: Placing queens on a chessboard without them threatening each other.
• Hamiltonian Cycle Problem and Colorings.
• Knight's Tour Problem: Finding the shortest possible route for a knight to visit all squares on a chessboard.
• Solving Maze Problems.
• Mastering Sudoku Puzzles.

💲 Section 6 - DYNAMIC PROGRAMMING

• Introduction to Dynamic Programming.
• The Knapsack Problem and its variations.
• Rod Cutting and Edit Distance Problems.
• Understanding State Transition Diagrams and Memoization.

🔍 Section 7 - SUBSTRING SEARCH ALGORITHMS

• Brute-Force Substring Search.
• Z Algorithm for efficient substring search.
• Rabin-Karp Algorithm: Using hashing to speed up searches.
• Knuth-Morris-Pratt (KMP) Algorithm for partial matches.

🎫 Section 8 - COMMON INTERVIEW QUESTIONS

• Tackling Top Questions from Google, Facebook, and Amazon.
• Solving Anagram, Palindrome, and Integer Reversion Problems.
• The Dutch National Flag Problem: Separating red, white, and blue pebbles.
• Trapping Rain Water: A two-pointer problem with geometric interpretation.

📈 Section 9 - Algorithms Analysis

• Measuring the Running Time of Algorithms.
• Understanding Big O (ordo), Big Omega (omega), and Big Theta (theta) Notations.
• Exploring Complexity Classes: P vs NP.
• The Importance of Efficiency in Algorithmic Solutions.

In this course, we will not only cover the theoretical aspects of each algorithm but also implement them from scratch in Python. By the end of this journey, you'll have a robust understanding of these crucial concepts and how to apply them effectively.

Let's embark on this coding adventure together! 🤝