Gain insights into utilizing NumPy for data manipulation and analytics. Learn about implementing concepts in both Python and NumPy through coding challenges and quizzes.

python.tar.gz

python3.6

Live3.6

If you're looking to grow your career in machine learning or data science in this day and age, adding a powerful library to your skill set is an important place to start. In that vein, Python has become one of the most widely used tools in the industry for serious data analytics, and NumPy is probably the most widely used data analytics library. With NumPy, you can manipulate data involving multi-dimensional arrays and matrices (think linear algebra).

Join us as we venture into the vast world of NumPy in this comprehensive course. Each lesson dive into the actual implementation of concepts in both pure Python and then NumPy, exploring how NumPy vectorization compares to traditional Python that uses a procedural and object-oriented approach.

Practice and test yourself along the way with in-browser coding challenges, quizzes, and more.

This course is intended for users who are already familiar with intermediate level Python.

From Python to Numpy

### Bellman-Ford Algorithm

The Bellman–Ford algorithm is an algorithm that is able to find the optimal path in a graph using a diffusion process. The optimal path is found by ascending the resulting gradient. This algorithm runs in quadratic time $O(|V||E|)$ (where $V$ is the number of vertices, and $E$ is the number of edges).


However, in our simple case, we won't hit the worst case scenario. Once this is done, we can ascend the gradient from the starting node. You can check on the figure that this leads to the shortest path.

# Bellman-Ford Algorithm

The Bellman–Ford algorithm is an algorithm that is able to find the optimal path in a graph using a diffusion process. The optimal path is found by ascending the resulting gradient. This algorithm runs in quadratic time $O(|V||E|)$ (where $V$ is the number of vertices, and $E$ is the number of edges).


However, in our simple case, we won't hit the worst case scenario. Once this is done, we can ascend the gradient from the starting node. You can check on the figure that this leads to the shortest path.

In this lesson, we will implement the solution of finding the shortest path in the maze using the Bellman-Ford approach. 

Introduction

Anatomy of an Array

Code Vectorization

Problem Vectorization

Custom Vectorization

Beyond NumPy

Conclusion

Coding Example: Find shortest path in a maze (Bellman-Ford approach)

Bellman-Ford Algorithm