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Data Science & ML track

NumPy

Array programming, broadcasting, vectorization and linear algebra ops.

6 concepts4 labs1 challenges
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Concepts

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Work through the concepts in order, or jump to what you need.

1

NumPy Introduction

Concept · Hands-on lab included

2

Array Creation and Shapes

Concept · Hands-on lab included

3

Vectorized Operations and Broadcasting

Concept · Hands-on lab included

4

Indexing and Slicing

Concept · Hands-on lab included

5

Random Numbers and Linear Algebra

Concept · Hands-on lab included

6

NumPy Performance

Concept · Hands-on lab included

Practice

Hands-on labs

Apply what you've learned in guided, production-style environments.

Browse all labs

Shape Gym: Build, Reshape, View

25 min · beginner · Array Creation and Shapes

Vectorize Everything

30 min · intermediate · Vectorized Operations and Broadcasting

Indexing Playground

25 min · intermediate · Indexing and Slicing

Simulate, Factor, Recover

30 min · intermediate · Random Numbers and Linear Algebra

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Next modules

Keep your learning streak going with related modules.

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Mathematics

Linear algebra, calculus and notation needed for machine learning.

0%

Statistics

Distributions, estimators, hypothesis testing and statistical inference.

0%

Python

Scientific Python stack: environments, typing, numpy-style thinking.

DevOpsForge - Data Science & ML · NumPy
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