Mastering Algorithms for Problem Solving in Python
Gain insights into algorithms and their implementation for problem-solving in Python. Learn about recursion, dynamic programming, greedy algorithms, and graph algorithms to enhance coding proficiency and problem-solving skills.
Intermediate
96 Lessons
28h
Certificate of Completion
Gain insights into algorithms and their implementation for problem-solving in Python. Learn about recursion, dynamic programming, greedy algorithms, and graph algorithms to enhance coding proficiency and problem-solving skills.
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Course Overview
What You'll Learn
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Course Overview
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 recursi...Show More
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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Course Content
1.
Getting Started
1 Lessons
Get familiar with essential algorithms and optimize processes with practical examples in Python.
2.
Introduction to Algorithm
6 Lessons
Look at algorithms' historical roots, design principles, applications, and analysis for problem-solving.
3.
Recursion
13 Lessons
Examine techniques and algorithms for leveraging recursion in problem-solving and optimization.
4.
Backtracking
10 Lessons
Break down complex ideas in backtracking algorithms for problems like N Queens, game trees, subset sum, and text segmentation.
5.
Dynamic Programming
11 Lessons
Take a closer look at dynamic programming techniques to solve complex problems efficiently in Python.
Fibonacci Numbers—Recursive ApproachFibonacci Numbers—Iterative ApproachText SegmentationLongest Increasing SubsequenceEdit DistanceSubset Sum—Dynamic ProgrammingOptimal Binary Search Trees–Dynamic ProgrammingDynamic Programming on TreesChallenge: Dynamic ProgrammingSolution: Dynamic ProgrammingQuiz: Dynamic Programming
6.
Greedy Algorithms
7 Lessons
See how it works to optimize with greedy algorithms for file storage, class scheduling, Huffman codes, stable matching, and book shelving.
8.
Basic Graph Algorithms
9 Lessons
Piece together the parts of basic graph algorithms, representations, search methods, and complex challenges.
9.
Depth-First Search
11 Lessons
Sharpen your skills in depth-first search (DFS) and its advanced applications in graphs.
10.
Minimum Spanning Trees
8 Lessons
Solve challenges with improving minimum spanning trees' algorithms and updating strategies.
11.
Shortest Paths
10 Lessons
Examine various algorithms for finding shortest paths in weighted graphs, addressing challenges and solutions.
12.
All-Pairs Shortest Paths
9 Lessons
Grasp the fundamentals of shortest paths algorithms and their optimization techniques in graphs.
14.
Wrapping up
1 Lessons
Take a look at mastering algorithms' efficiency in problem-solving and their practical applications.
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