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From Python to Numpy

Name: From Python to Numpy
Price: 59 USD
Author: Nicolas P. Rougier

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

45 lessons

Created by Nicolas P. Rougier

What's included

45 lessons across 7 modules
17 quizzes with instant feedback
AI Tutor on every lesson

Join 3.1 million developers at

What You'll Learn

Identify NumPy’s core role, array types, and dtypes for numerical and scientific computing
Create and reshape NumPy arrays using zeros/ones, random, linspace, and meshgrid
Apply NumPy indexing (slicing, fancy, boolean) to extract and modify multidimensional data
Use broadcasting rules to perform vectorized arithmetic on arrays of different shapes
Analyze memory layout, strides, views vs copies, and ravel/flatten impacts on performance
Implement uniform, temporal, and spatial vectorization in simulations (Life, Mandelbrot, Boids)
Evaluate when to use pure Python, NumPy, or libraries like Numba/SciPy based on speed and effort

Learning Roadmap

45 Lessons17 Quizzes1 Challenge

Introduction

Get familiar with NumPy fundamentals, including array creation, reshaping, indexing, and vectorization.

Broadcasting in NumPy

NumPy Vectorization

Readability vs. Speed

Anatomy of an Array

Look at the foundational elements of NumPy arrays, memory layout, views, copies, and optimization.

Introduction

Memory layout

Views and Copies

Coding Example: How to find if one vector is view of the other?

Solution Review

Code Vectorization

13 Lessons

Examine methods to enhance Python code efficiency using NumPy for various simulations.

Problem Vectorization

9 Lessons

Find out about problem vectorization to enhance algorithmic efficiency in Python and NumPy.

Custom Vectorization

5 Lessons

Dig into custom vectorization techniques using Typed Lists and memory-aware NumPy arrays.

Beyond NumPy

4 Lessons

Implement optimized Python techniques and explore advanced libraries beyond NumPy for enhanced performance.

Conclusion

2 Lessons

Master the steps to effectively leverage NumPy for creative and efficient problem-solving.

Certificate of Completion

Showcase your accomplishment by sharing your certificate of completion.

About this course

WHY I WROTE THIS COURSE

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.

ABOUT THE AUTHOR

Nicolas P. Rougier

Researcher at Inria (French national institute for research in computer science and control) interested in Computational Neurosciences, Open Science, Scientific Visualization and Computer Graphics.

Learn more about Nicolas

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