Deep Dive into Algorithms from Udemy

What's inside

Learning objectives

Students will learn various backtracking problems along with implementation using c language
Students will learn various dynamic programming problems along with implementation using c language
Students will learn various graph algorithms along with implementation using c language
Branch and bound

Divide and conquer
Greedy algorithm
Pattern matching
Searching and sorting

Students will learn various backtracking problems along with implementation using c language
Students will learn various dynamic programming problems along with implementation using c language
Students will learn various graph algorithms along with implementation using c language
Branch and bound
Divide and conquer
Greedy algorithm
Pattern matching
Searching and sorting

Syllabus

Backtracking

Introduction

Concept of N Queen Problem

Implementation of N Queen Problem

Time Complexity Analysis of N Queen Problem

Concept of Knight's Tour Problem

Implementation of Knight's Tour Problem

Time Complexity Analysis of Knight's Tour Problem

Concept Explanation of Rat in a Maze Problem

Implementation of Rat in a Maze

Time Complexity Analysis of Rat in a Maze

Concept Explanation of Subset Sum

Implementation of Subset Sum Problem

Time and Space Complexity Analysis of Subset Sum Problem

Concept Explanation of M-Coloring Problem

Implementation of M Coloring Problem

Time and Space Complexity Analysis of M Coloring Problem

Concept Explanation of Hamiltonian Cycle Problem

Implementation of Hamiltonian Cycle

Time and Space Complexity Analysis of Hamiltonian Cycle

Concept Explanation of Sudoku Solver

Implementation of Sudoku Solver

Time and Space Complexity Analysis of Sudoku Solver

Sieve of Eratosthene

Implementation of Sieve of Eratosthene

Concept Explanation of Sieve of Sundaram

Implementation of Sieve of Sundaram

Time and Space Complexity Analysis of Sieve of Eratosthene and Sieve of Sundaram

Sieve of Eratosthene in O(N) Time Complexity

Implementation of Sieve of Eratosthene in O(N) Time Complexity

Prime Numbers after P with Sum S

Implementation of Prime Numbers after P with Sum S

Time and Space Complexity Analysis of Prime Numbers after P with Sum S

Dynamic Programming

Introduction to Dynamic Programming - Part 1

Introduction to Dynamic Programming - Part 2

0/1 Kanpsack Problem

Implementation of 0/1 Knapsack Problem

Printing items in 0/1 Knapsack Problem

Implementation of printing items in 0/1 Knapsack Problem

Minimum Cost Path

Implementation of Minimum Cost Path

Tracing the path of Minimum Cost Path

Implementation of Tracing the Path of Minimum Cost Path

Subset Sum Problem

Printing items in Subset Sum Problem

Implementation of printing items in Subset Sum

Maximum Size Square Sub Matrix with all 1s

Implementation of Maximum Size Square Sub Matrix with all 1s

Longest Increasing Subsequence

Implementation of Longest Increasing Subsequence

Printing items in Longest Increasing Subsequence

Implementation of Printing items in Longest Increasing Subsequence

Longest Common Subsequence

Implementation of Longest Common Subsequence

Tracing the String in Longest Common Subsequence

Implementation of Tracing the String in Longest Common Subsequence

Range Query Algorithms

Introduction and Brute Force Approach of Range Minimum Query

Implementation of Range Minimum Query ( Brute Force Approach )

Dynamic Programming Approach for Range Minimum Query

Implementation of Range Minimum Query ( Dynamic Programming Approach )

Introduction to Segment Tree

Constructing Segment Tree

Implementation of Constructing Segment Tree

Range Minimum Query on the Constructed Segment Tree

Implementation of Range Minimum Query on Constructed Segment Tree

Range Minimum Query Using Sparse Table

Performing RMQ on Constructed Sparse Table

How to efficiently fill Sparse Table ?

Implementation of RMQ using Sparse Table

Graph Algorithms

How to represent a adjacency list for an directed and undirected graph ?

Implementation of Adjacency List for Directed and Undirected Graph

HierHolzer's Algorithm

Implementation of HierHolzer's Algorithm

Union Find Algorithm

Implementation of Union Find Algorithm

Topological Sorting

Dijkstra's Algorithm

Bellman Ford Algorithm

Ford Fulkerson Method Edmond Karg Maximum Flow Algorithm

Kargers Algorithm for Minimum Cut

Kruskal's Algorithm for Minimum Spanning Tree

Prim's Algorithm for Minimum Spanning Tree

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Covers backtracking, dynamic programming, and graph algorithms, which are fundamental concepts in computer science curricula

Teaches dynamic programming, which is essential for solving optimization problems in software development and system design

Explores graph algorithms like Dijkstra's, Bellman-Ford, and Kruskal's, which are crucial for network optimization and pathfinding

Requires familiarity with the C language, which may pose a barrier for learners unfamiliar with this language

Includes Hierholzer's Algorithm, Ford Fulkerson Method, and Karger's Algorithm, which are relevant to advanced algorithm design

Focuses on implementation using the C language, which may not be the preferred language for all software development projects

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Deep Dive into Algorithms with these activities:

Review Data Structures

Show steps

Solidify your understanding of fundamental data structures before diving into algorithm design paradigms. This will make it easier to understand the complexities and trade-offs of different algorithms.

Browse courses on Data Structures

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Review the definitions and properties of common data structures.
Implement basic operations (insertion, deletion, search) for each data structure.
Solve simple problems using each data structure.

Review Introduction to Algorithms

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Reference a comprehensive algorithms textbook to supplement the course material. This will provide a deeper understanding of the concepts and techniques covered in the course.

View Introduction to Algorithms, fourth edition on Amazon

Show steps

Read the relevant chapters on backtracking, dynamic programming, and graph algorithms.
Work through the examples and exercises in the book.
Compare the book's explanations with the course material.

Review Algorithm Design

Show steps

Reference a comprehensive algorithms textbook to supplement the course material. This will provide a deeper understanding of the concepts and techniques covered in the course.

View Algorithm Design on Amazon

Show steps

Read the relevant chapters on backtracking, dynamic programming, and graph algorithms.
Work through the examples and exercises in the book.
Compare the book's explanations with the course material.

Three other activities

Expand to see all activities and additional details

Show all six activities

LeetCode Backtracking Problems

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Practice backtracking problems on LeetCode to improve your problem-solving skills and coding proficiency. This will help you master the backtracking concepts covered in the course.

Show steps

Attempt to solve each problem independently.
Select a set of backtracking problems on LeetCode.
Analyze the solutions and identify areas for improvement.

Create a Blog Post on Dynamic Programming

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Write a blog post explaining the concept of dynamic programming and its applications. This will help you solidify your understanding of dynamic programming and improve your communication skills.

Show steps

Research and gather information on dynamic programming.
Write a clear and concise explanation of dynamic programming.
Provide examples of dynamic programming problems and their solutions.
Publish the blog post on a platform like Medium or your personal website.

Implement a Graph Visualization Tool

Show steps

Build a tool that visualizes graph algorithms in action. This will deepen your understanding of graph algorithms and improve your coding skills.

Show steps

Choose a programming language and a graph visualization library.
Implement the visualization for several graph algorithms (e.g., Dijkstra's, Kruskal's).
Add features to interact with the visualization (e.g., step-by-step execution).

Career center

Learners who complete Deep Dive into Algorithms will develop knowledge and skills that may be useful to these careers:

Algorithm Developer

An Algorithm Developer designs and implements new algorithms or optimizes existing ones for various applications. The core content of this course directly aligns with the responsibilities of this role. The detailed exploration of algorithmic paradigms, combined with practical implementation examples in C, makes this course highly relevant. An aspiring Algorithm Developer may benefit from the study of backtracking, dynamic programming, graph algorithms, and pattern matching techniques, enabling them to create efficient and innovative solutions.

See salaries and explore the career path for Algorithm Developer

Software Engineer

A Software Engineer designs, develops, and tests software applications. This course, with its deep dive into algorithmic paradigms, helps build a strong foundation for writing efficient and performant code. The sections on backtracking, dynamic programming, and graph algorithms directly translate to problem-solving skills needed daily by Software Engineers. Understanding algorithmic time complexity and different algorithm design paradigms greatly assists in creating scalable and robust software solutions.

See salaries and explore the career path for Software Engineer

Computer Scientist

A Computer Scientist conducts research and develops new theories and algorithms in computer science. This course, focusing on the theoretical foundations of algorithms, is highly beneficial. The in-depth coverage of algorithmic paradigms, time complexity analysis, and various algorithm design techniques provides a solid foundation for conducting research and developing innovative solutions to complex computational problems. Exposure to backtracking, dynamic programming, and graph algorithms ensures a broad understanding of fundamental algorithmic concepts.

See salaries and explore the career path for Computer Scientist

Research Scientist

A Research Scientist conducts scientific research in various fields, often involving the development and implementation of new algorithms and computational methods. This course, with its deep dive into different algorithmic paradigms, provides a solid foundation for research in computer science, mathematics, and related fields. The detailed exploration of specific algorithms, such as backtracking, dynamic programming, and graph algorithms, are essential for conducting research and developing innovative solutions to complex problems.

See salaries and explore the career path for Research Scientist

Full-Stack Developer

A Full Stack Developer works on both the front-end and back-end of web applications. This course directly correlates to Full Stack Development tasks, especially on the Back End where efficient algorithms are critical for server-side logic, database interactions, and API design. The focus on time complexity with the use of graph algorithms, dynamic programming, and searching/sorting provides a strong foundation for optimizing application performance and scalability.

See salaries and explore the career path for Full-Stack Developer

Machine Learning Engineer

A Machine Learning Engineer develops and deploys machine learning models. This course may be useful, as the exploration of algorithms, particularly dynamic programming and graph algorithms, helps optimize machine learning algorithms and data processing pipelines. Furthermore, a Machine Learning Engineer can leverage the knowledge gained from this course to understand the computational complexity of different machine learning algorithms and choose the most efficient ones for specific tasks.

See salaries and explore the career path for Machine Learning Engineer

Data Scientist

A Data Scientist analyzes large datasets to extract meaningful insights and develop predictive models. This course may be useful in this path, as a Data Scientist often needs to implement different algorithms to process and analyze data effectively. The focus on searching and sorting algorithms, along with dynamic programming techniques, help optimize data processing pipelines and improve model performance. Understanding the trade-offs between different algorithms, as covered in the course, is critical for making informed decisions in data analysis and model development.

See salaries and explore the career path for Data Scientist

Game Developer

A Game Developer creates video games, often requiring proficiency in algorithms for AI, pathfinding, and game logic. This course may be useful, as the graph algorithms and dynamic programming techniques covered in the course are highly relevant. Pathfinding algorithms like Dijkstra's and A*, which are related to the material on graph algorithms, are crucial for character movement and AI decision-making. Dynamic programming can be applied to optimize game state calculations and resource management.

See salaries and explore the career path for Game Developer

Mobile Application Developer

A Mobile Application Developer creates applications for mobile devices. This course may be useful, as understanding of algorithms can lead to writing efficient code. The study of algorithms such as Backtracking, Dynamic Programming, Divide and Conquer, and Greedy Algorithm helps improve efficiency of application development. Applying searching and sorting techniques can help to optimize the applications.

See salaries and explore the career path for Mobile Application Developer

Database Administrator

A Database Administrator is responsible for maintaining and optimizing databases to ensure data integrity and performance. This course may be useful, as knowledge of searching and sorting algorithms, as well as range query algorithms like segment trees and sparse tables, aids in optimizing database queries and indexing strategies. Understanding algorithmic complexity also helps Database Administrators make informed decisions about database design and query optimization, ensuring efficient data retrieval and storage.

See salaries and explore the career path for Database Administrator

Cybersecurity Analyst

A Cybersecurity Analyst protects computer systems and networks from cyber threats. This course may be useful, as understanding algorithms can aid in identifying and mitigating security vulnerabilities. Pattern matching algorithms, such as those used in intrusion detection systems, help detect malicious code and network traffic. Knowledge of graph algorithms can also be applied to analyze network topologies and identify potential points of attack. The course's coverage of various algorithms helps improve Cybersecurity Analysts' ability to detect and respond to cyber threats effectively.

See salaries and explore the career path for Cybersecurity Analyst

Robotics Engineer

A Robotics Engineer designs, builds, and programs robots. This course may be useful, as algorithms play a crucial role in robot navigation, path planning, and control systems. Graph algorithms, such as Dijkstra's and A*, are used for path planning, while dynamic programming can be applied to optimize robot movements and energy consumption. The knowledge gained from this course helps Robotics Engineers develop efficient and robust control systems for robots operating in complex environments.

See salaries and explore the career path for Robotics Engineer

Systems Architect

A Systems Architect designs and implements complex computer systems. This course may be useful, as understanding algorithm design paradigms helps in making informed decisions about system architecture and performance optimization. The knowledge of different algorithm design techniques assists in selecting the most appropriate algorithms for various system components. An aspiring Systems Architect leverages this knowledge to build scalable, reliable, and efficient computer systems.

See salaries and explore the career path for Systems Architect

Bioinformatician

A Bioinformatician analyzes biological data using computational tools and algorithms. This course may be useful, as many bioinformatics tasks, such as sequence alignment and phylogenetic analysis, rely on efficient algorithms. Dynamic programming is used extensively in sequence alignment, while graph algorithms are used in network analysis and phylogenetic tree construction. Understanding the principles of algorithm design and analysis helps Bioinformaticians develop and optimize computational methods for analyzing large biological datasets.

See salaries and explore the career path for Bioinformatician

Quantitative Analyst

A Quantitative Analyst develops and implements mathematical models for financial analysis and risk management. This course may be useful, as the algorithm design paradigms and optimization techniques covered are applicable to financial modeling. Dynamic programming, in particular, is used in option pricing and portfolio optimization. Moreover, the understanding of algorithmic complexity helps Quantitative Analysts develop efficient and scalable models for large financial datasets.

See salaries and explore the career path for Quantitative Analyst

Deep Dive into Algorithms

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Syllabus

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