IMTx: Advanced Algorithmics and Graph Theory with Python

55,00 

Strengthen your skills in algorithmics and graph theory, and gain experience in programming in Python along the way.

Categories ,

55,00 

Strengthen your skills in algorithmics and graph theory, and gain experience in programming in Python along the way.

Categories ,

Discover the Course

About this course

Algorithmics and programming are fundamental skills for engineering students, data scientists and analysts, computer hobbyists or developers.

Learning how to program algorithms can be tedious if you aren’t given an opportunity to immediately practice what you learn. In this course, you won’t just focus on theory or study a simple catalog of methods, procedures, and concepts. Instead, you’ll be given a challenge wherein you’ll be asked to beat an algorithm we’ve written for you by coming up with your own clever solution.

To be specific, you’ll have to work out a route faster than your opponent through a maze while picking up objects.

Each week, you will learn new material to improve your artificial intelligence in order to beat your opponent. This structure means that as a learner, you’ll confront each abstract notion with a real-world problem.

We’ll go over data-structures, basic and advanced algorithms for graph theory, complexity/accuracy trade-offs, and even combinatorial game theory.

This course has received financial support from the Patrick and Lina Drahi Foundation.

At a glance

  • Institution: IMTx
  • Subject: Computer Science
  • Level: Intermediate
  • Prerequisites:

    Some familiarity with Python 3 and basic mathematics.

  • Language: English
  • Video Transcript: English
  • Associated skills:Data Structures, Artificial Intelligence, Graph Theory, Algorithms, Go (Programming Language), Game Theory, Python (Programming Language)

What you’ll learn

  • Ways to express a computational problem (such as pathfinding) using graph theory
  • How to choose the appropriate algorithm to solve the given computational problem
  • How to code the algorithmic solution in python
  • Methods for evaluating the proposed solution in terms of its complexity (amount of resources, scalability) or performance (accuracy, latency)

Course Overview

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Practical Impact

Gain immediately usable knowledge you can apply at work or life.

Progress Tracking

Track your growth every course, see how far you’ve come.

Career & Personal Growth

Advance professionally while growing personally.

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