NumPy, SciPy, and Matplotlib Recipes

Learn to code Scientific Python recipes and Image Processing with NumPy, SciPy, Matplotlib, and Jupyter Notebook

4.10 (34 reviews)
Udemy
platform
English
language
Programming Languages
category
238
students
11 hours
content
Jul 2022
last update
$69.99
regular price

What you will learn

Understand and explain the Scientific Ecosystem

Work with Ndarrays in NumPy

Mathematical and Statistical Functions

Image Processing with NumPy and Matplotlib

Basic and Advanced Visualizations using Matplotlib

SciPy, NumPy, and Matplotlib Recipes

K-Means Clustering

Description

Become a Master in Scientific Python and acquire employers' one of the most requested skills of 21st Century! A great Scientific Python programmer earns more than $150000 per year.

This is the most comprehensive, yet straight-forward course for the Scientific Python on Udemy! Whether you have never used SciPy before, already know basics of Python, or want to learn the advanced features of NumPy with Python 3, this course is for you! In this course we will teach you NumPy, SciPy, Matplotlib, and Jupyter Notebook. 

With over 100 lectures and more than 10 hours of video this comprehensive course leaves no stone unturned in teaching you Scientific Python!

This course will teach you Scientific Python in a very practical manner, with every lecture comes a full Python 3 programming video and a corresponding Jupyter notebook that has Python 3 code! Learn in whatever manner is the best for you!

We will start by helping you get Python3, NumPy, Matplotlib, Jupyter, and SciPy installed on your Windows computer and Raspberry Pi.

We cover a wide variety of topics, including:

  • Basics of Scientific Python Ecosystem

  • Basics of SciPy, NumPy, and Matplotlib

  • Installation of Python 3 on Windows

  • Setting up Raspberry Pi

  • Tour of Python 3 environment on Raspberry Pi

  • Jupyter installation and basics

  • Ndarrays

  • Array Creation Routines

  • Basic Visualization with Matplotlib

  • Ndarray Manipulation

  • Installation of SciPy

  • Image Processing with NumPy and Matplotlib

  • NumPy and SciPy

  • Scientific and Business Visualizations

  • K-Means clustering with SciPy

You will get lifetime access to over 100 lectures plus corresponding PDFs and the Jupyter notebooks for the lectures! 

So what are you waiting for? Learn SciPy, NumPy, and Matplotlib in a way that will advance your career and increase your knowledge, all in a fun and practical way!

Content

Introduction

Objectives, Prerequisites, and Audiences
Contents and Topics Overview
Please leave your feedback
Scientific Python Ecosystem
Introduction to NumPy
Important Projects in scientific Python Ecosystem

Python 3 on Windows

Installation
Verification

Raspberry Pi and Python

What is Raspberry Pi
Unboxing
URLs to all the softwares used in this section
Raspbian OS Setup Part 1
Raspbian OS Setup Part 2
Remote connection with VNC
Linux commands used in the section
Install IDLE3 on Raspberry Pi Raspbian
Python on Raspberry Pi

Python 3 Basics

"Hello World" on Windows!
"Hello World" on Raspberry Pi!
Interpreter vs Script Mode
IDLE
RPi vs PC

Python Package Index and pip

Python Package Index
pip on Windows
pip3 on Raspberry Pi

Install NumPy and Matplotlib

Install NumPy and Matplotlib on Windows
Install NumPy and Matplotlib on Raspberry Pi

IPython and Jupyter Basics

IPython and Jupyter
Install Jupyter on Windows
Install Jupyter on Raspberry Pi
PuTTY
Connect to a remote Jupyter Notebook
A brief Tour of Jupyter
Commands used in this Section

Getting Started with NumPy

Ndarray, Indexing, and Slicing
Ndarray Properties
NumPy constants
NumPy Datatypes

Creation of arrays and Matplotlib

Ones and Zeros
Matrices
Introduction to Matplotlib
Visualize Numerical Ranges

NumPy and Random

NumPy and Random

Array manipulation

Array manipulation

Bitwise Operations

Bitwise Operations

Statistical Routines

Statistical Routines

FFTs

FFTs
Advanced FFTs

Linear Algebra

Dot Products
Vector Dot product
Inner Product
QR Decomposition
Determinants and Solving Linear Equations

Mathematical and Trigonometric Function

Trigonometric Functions
Hyperbolic Functions
Exponential Functions
Logarithmic Functions
Convolution

Image Processing and Matplotlib

What is Digital Image Processing?
Image Datasets
Pillow Installation
Read, display, and save images with Matplotlib
NumPy for Images
Image Statistics
Image Masks
Image Channels
Arithmetic Operations
Logical Operations
Histogram with NumPy and Matplotlib

File Operations with NumPy

NumPy File Format
Reading CSV File

Set Operations

Set Operations

Sorting and Counting

Sorting Routines
Counting Non Zero Elements

Plotting in detail

Single Line Plot
Multiline Plot
Grid, Axes, and Labels
Colors, Lines, and Markers

Introduction to SciPy

Introduction to SciPy
Install SciPy on Windows
Install SciPy on Raspberry Pi

NumPy and SciPy Recipes

Advanced Indexing
Images and Memory Maps
Work with Audio
More Array Creation routines
SciPy Stats
NumPy Testing Routines
Brownian Motion
Set Operations

A bit of machine Learning

K Means Clustering

Visualization Recipes with Matplotlib

Simple Plots
Time Plots
Multiplying Two Signals
Different Strokes
Filled Plots
Dashed Plots
Colored Plots
Dotted Plots
Polar Plots
2D Histograms

Downloadable Contents

Downloadable Code Bundle
BONUS LECTURE

Screenshots

NumPy, SciPy, and Matplotlib Recipes - Screenshot_01NumPy, SciPy, and Matplotlib Recipes - Screenshot_02NumPy, SciPy, and Matplotlib Recipes - Screenshot_03NumPy, SciPy, and Matplotlib Recipes - Screenshot_04

Reviews

Kirk
September 11, 2023
The information presented is fine. However reading what you are typing is distracting and doesn't help comprehension. Perhaps a quick explanation of what is there would be better.
Emily
September 5, 2023
There is a lot of content where it is not really relevant to using numpy, scipy, or matplotlib. It is also quite long winded. This means I am having to listen to it at 1.5 speed. For example, lesson 33 on Ndarray, Indexing and Slicing has 5 mins of irrelevant unix admin. It might be useful for someone who isn't used to unix or raspberry pi, but not a python programmer who wants to learn numpy and scipy quickly.
Abdullah
August 15, 2021
Alright being a beginner in raspberry pi, I was just awestruck. The things that he is teaching is out there in the internet scattered. But you can actually save a load of your time by purchasing this course.
Matheus
March 10, 2020
O curso cumpre o que promete. Tenho aprendido muito com os vídeos e minha base nem era tão boa assim de Python. Super recomendável àqueles que precisam trabalhar com visualização de dados!

Charts

Price

NumPy, SciPy, and Matplotlib Recipes - Price chart

Rating

NumPy, SciPy, and Matplotlib Recipes - Ratings chart

Enrollment distribution

NumPy, SciPy, and Matplotlib Recipes - Distribution chart
2548109
udemy ID
9/7/2019
course created date
11/21/2019
course indexed date
Bot
course submited by