Udemy
    •  
    •  
    •  
    •  
    •  
    •  
    •  
    •  
Subscribe
Turn what you know into an opportunity and reach millions around the world.
Learn More
Your cart is empty.
Keep shopping
Numpy with Python
Rating: 4.2 out of 5(234 ratings)
17,047 students

Numpy with Python

NumPy is a library for the Python programming language
Created byDATAhill Solutions Srinivas Reddy
Last updated 1/2020
English

What you'll learn

  • 1. NumPy Introduction 2. Python Numpy Array 3. Indexing & Slicing - 1 4. Indexing & Slicing - 2 5. Statistical Functions, Operators & Random Numbers 6. What is Data Science 7. What is Machine Learning

Course content

1 section • 15 lectures • 3h 10m total length

  • Session 1. NumPy Introduction0:31

    Explore what numpy is, its supported operations and features, and learn how to install and use numpy with pip or anaconda.

  • NumPy Introduction34:09
  • Session 2. Python Numpy Array0:28

    Learn how to work with Numpy arrays in Python, including single dimension, multi dimension, and two dimensional arrays, and understand how they relate to matrices.

  • Python Numpy Array22:32
  • Session 3. Indexing & Slicing - 10:42

    Master numpy indexing and slicing basics, including zero-based indexing, positive step slices from left to right, and reversing sequences with negative steps.

  • Indexing & Slicing - 119:29

    Master numpy indexing and slicing to access specific values in arrays using start, end, and step. Learn how zero-based indexing and negative indices affect which elements you extract.

  • Session 4. Indexing & Slicing - 20:48

    Learn how to slice data using indexing in NumPy, from single-dimensional to two-dimensional arrays with rows and columns, and study the required syntax.

  • Indexing & Slicing - 230:21

    Master numpy indexing and slicing for 1d and 2d arrays, handling shapes and start–end–step selections. Learn broadcasting, transpose, and key statistics such as min, max, mean, median, and std.

  • Session 5. Statistical Functions, Operators & Random Numbers0:48

    Explore numpy's statistical functions, including finding minimum, maximum, standard deviation, and variance, and learn how to apply them alongside numpy's random module for generating values.

  • Statistical Functions, Operators & Random Numbers20:12

    Explore statistical functions and operators in numpy with Python, computing min, max, mean, median, variance, and standard deviation, while mastering linear algebra with matrices and generating random numbers with seeds.

  • Data Science1:43
  • 6. What is Data Science30:42
  • Machine Learning1:27

    Explore what machine learning is, contrast it with traditional programming, examine ML capabilities, its link to statistics, and types of ML, plus responsibilities from data collection to deployment and reporting.

  • 7. What is Machine Learning25:06
  • Summary1:32

Requirements

  • Previous programming experience. Familiarity with collection types in Python.

Description

In this series, we cover the basics of using NumPy for basic data analysis. Some of the things that are covered are as follows: installing NumPy using the Anaconda Python distribution, creating NumPy arrays in a variety of ways, gathering information about large datasets such as the mean, median and standard deviation, as well as utilizing Jupyter Notebooks for exploration using NumPy. If you are looking to get started with NumPy then join us!


1. NumPy Introduction

2. Python Numpy Array

3. Indexing & Slicing - 1

4. Indexing & Slicing - 2

5. Statistical Functions, Operators & Random Numbers

6. What is Data Science

7. What is Machine Learning

Who this course is for:

  • Beginner Python developers curious about Data Science

Report abuse