Mastering Algorithms: Practical Approach To Solving Problems

Algorithmic Symphony: Harmonizing Sorting, Dynamic Programming, Recursion, and Backtracking

What you'll learn

Understanding Fundamental Concepts: Develop a strong foundation in algorithmic principles

Problem-Solving Skills: Cultivate the ability to analyze complex problems and devise efficient algorithms to solve them.

Algorithm Design Techniques: Explore various algorithm design techniques, including brute force, recursion, and optimization strategies.

Data Structures Integration: Integrate algorithms with essential data structures such as arrays, linked lists, trees, and graphs

Practical Applications and Optimization: Apply algorithms to real-world scenarios and understand how to optimize code for better performance

Requirements

A solid understanding of at least one programming language is crucial

Description

Unlock the secrets of efficient problem-solving and algorithmic optimization with our "Mastering Algorithms" course. Whether you're a computer science student, a seasoned software engineer, or an aspiring coder, this course is designed to elevate your understanding of algorithm design, implementation, and analysis.Course Highlights:1. Foundational Concepts: Dive deep into the fundamental concepts of algorithmic design, data structures, and their practical applications. Understand time and space complexity, and develop a solid foundation in algorithmic thinking.2. Sorting and Searching Mastery: Explore a spectrum of sorting algorithms and searching techniques. From basic to advanced, learn how to choose the right approach for different scenarios and optimize your code for efficiency.3. Dynamic Programming Unveiled: Demystify the power of dynamic programming. Discover how to break down complex problems into simpler subproblems and optimize solutions for improved efficiency. Real-world applications and case studies will reinforce your understanding.4. Recursive Wisdom: Delve into the elegance of recursive algorithms. Master the art of problem decomposition, understand the divide-and-conquer approach, and apply recursion to solve a variety of problems efficiently.5. Backtracking Techniques: Learn systematic exploration with backtracking. Navigate complex problem spaces, make informed decisions, and discover how backtracking can be a powerful tool for solving problems with multiple solutions.6. Real-world Applications: Bridge the gap between theory and practice by applying algorithms to real-world scenarios. Work on hands-on projects and case studies that simulate industry challenges, preparing you for practical algorithmic problem-solving.7. Optimizing Code: Gain insights into optimizing code for better performance. Understand the importance of algorithmic efficiency in software development and learn best practices for writing scalable and maintainable code.8. Interactive Learning: Engage in a dynamic learning environment with interactive quizzes, coding exercises, and collaborative projects. Benefit from personalized feedback to enhance your problem-solving skills.By the end of this course, you will not only have a comprehensive understanding of various algorithms but also the ability to approach complex problems with confidence and devise efficient solutions. Join us on a journey to master the art and science of algorithms and elevate your coding skills to new heights.

Overview

Section 1: Recursion

Lecture 1 Introduction

Lecture 2 Pros & Cons

Lecture 3 Example

Lecture 4 Math Induction

Lecture 5 Math Function

Lecture 6 Iterative

Lecture 7 Coding: Factorial

Lecture 8 Coding: Fibonacci

Lecture 9 Tail Recursion

Lecture 10 Indirect Recusion

Section 2: Sorting

Lecture 11 Introduction

Lecture 12 Comparison based Sorting Algorithms

Lecture 13 Bubble Sort

Lecture 14 Coding: Bubble Sort

Lecture 15 Selection Sort

Lecture 16 Visual: Selection Sort

Lecture 17 Coding: Selection Sort

Lecture 18 Visual: Insertion Sort

Lecture 19 Coding: Insertion Sort

Section 3: Divide & Conquer

Lecture 20 Introduction

Lecture 21 Recursion

Lecture 22 Divide Phase

Lecture 23 Conquer Phase

Lecture 24 Merge Sort

Lecture 25 Visual: Merge Sort

Lecture 26 Coding: Merge Sort

Lecture 27 Quick Sort

Lecture 28 Coding: Quick Sort

Section 4: Backtracking

Lecture 29 Introduction

Lecture 30 Coding: N-Queens

Section 5: Dynamic Programming

Lecture 31 Introduction

Lecture 32 Memoization

Lecture 33 Coding: Memoization

Lecture 34 Tabulation

Lecture 35 Coding: Tabulation

Lecture 36 Patterns

Lecture 37 States and Transitions

Lecture 38 Problem: Maximum Deliciousness

Lecture 39 Coding: Maximum Deliciousness

Lecture 40 Complexity: Maximum Deliciousness

Lecture 41 Coding: Maximum Deliciousness (Top Down)

Lecture 0 Coding: Maximum Deliciousness (Bottom Up)

Section 6: Sliding Window

Lecture 42 Introduction

Lecture 43 Fixed Size Sliding Window

Lecture 44 Coding: Maximum Sum of Subarray

Lecture 45 Variable Size Sliding Window

Lecture 0 Visual: Minimum Length of Subarray

Lecture 0 Coding: Variable Size Sliding Window

Lecture 0 Introduction

Lecture 0 Coding: Two Pointers

Computer Science Students,Software Engineers and Developers,Coding Enthusiasts,Anyone Interested in Problem Solving