5.0
Intermediate
28h
Updated this week
Mastering Algorithms for Problem Solving in Python
Gain insights into foundational and advanced algorithms, including recursion, dynamic programming, and graph algorithms, to enhance your problem-solving skills and coding proficiency in Python.
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As a developer, mastering the concepts of algorithms and being proficient in implementing them is essential to improving problem-solving skills. This course aims to equip you with an in-depth understanding of algorithms and how they can be utilized for problem-solving in Python.
Starting with the basics, you'll gain a foundational understanding of what algorithms are, with topics ranging from simple multiplication algorithms to analyzing algorithms. Then, you’ll delve into more advanced topics like recursion, backtracking, dynamic programming, and greedy algorithms. You'll also learn about basic graph algorithms, depth-first search, shortest paths, minimum spanning trees, and all-pairs shortest paths.
By the end of this course, you'll have acquired a wide range of skills that will significantly enhance your ability to solve problems efficiently in Python. Through this course, not only will you improve your coding skills, but you will also gain confidence in tackling complex problems.
As a developer, mastering the concepts of algorithms and being proficient in implementing them is essential to improving problem...Show More
WHAT YOU'LL LEARN
A comprehensive understanding of algorithms and their applications in problem solving
Proficiency in implementing recursion and backtracking in Python for complex tasks
An understanding of the concept of memoization and dynamic programming
Ability to apply memoization and dynamic programming for efficient computation in Python
Hands-on experience solving algorithmic challenges in Python
A comprehensive understanding of algorithms and their applications in problem solving
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Learning Roadmap
2.
Introduction to Algorithm
Introduction to Algorithm
Look at algorithms' historical roots, design principles, applications, and analysis for problem-solving.
3.
Recursion
Recursion
13 Lessons
13 Lessons
Examine techniques and algorithms for leveraging recursion in problem-solving and optimization.
4.
Backtracking
Backtracking
10 Lessons
10 Lessons
Break down complex ideas in backtracking algorithms for problems like N Queens, game trees, subset sum, and text segmentation.
5.
Dynamic Programming
Dynamic Programming
11 Lessons
11 Lessons
Take a closer look at dynamic programming techniques to solve complex problems efficiently in Python.
6.
Greedy Algorithms
Greedy Algorithms
7 Lessons
7 Lessons
See how it works to optimize with greedy algorithms for file storage, class scheduling, Huffman codes, stable matching, and book shelving.
7.
Basic Graph Algorithms
Basic Graph Algorithms
9 Lessons
9 Lessons
Piece together the parts of basic graph algorithms, representations, search methods, and complex challenges.
8.
Depth-First Search
Depth-First Search
11 Lessons
11 Lessons
Sharpen your skills in depth-first search (DFS) and its advanced applications in graphs.
9.
Minimum Spanning Trees
Minimum Spanning Trees
8 Lessons
8 Lessons
Solve challenges with improving minimum spanning trees' algorithms and updating strategies.
10.
Shortest Paths
Shortest Paths
10 Lessons
10 Lessons
Examine various algorithms for finding shortest paths in weighted graphs, addressing challenges and solutions.
11.
All-Pairs Shortest Paths
All-Pairs Shortest Paths
9 Lessons
9 Lessons
Grasp the fundamentals of shortest paths algorithms and their optimization techniques in graphs.
Certificate of Completion
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