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Introduction To NumPy

Learn a good foundation of NumPy programming for use in machine learning and data science, or on its own for mathematical purposes. Read more.

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Course Skill Level
Beginner
Time Estimate
1h 2m

Hi, it's Mustafa Alawi, a Software engineer & entrepreneur. I've basically seven years of experience in web development & software development. I also work as a consultant helping small-businesses. And have a great passion for business & entrepreneurship and founded CalmAndCode. I like teaching people and providing them with the knowledge that I have as I'm talented at simplifying complex theories and matters. And one of my goals is to help my students find success and reach their goals.

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About This Course

Who this course is for:

  • Python developers (All levels)
  • Programmers who want to learn NumPy

What you’ll learn: 

  • How to install Python
  • Create Python files
  • Run Python files
  • How to use NumPy
  • How to create arrays in NumPy
  • How to apply mathematical functions to arrays and matrices
  • How to apply statistical functions to arrays and matrices

Requirements: 

  • Basic knowledge in Python (How to create simple python scripts)

This course will teach you how to get started with Numpy. Python was described as the language that is capable of doing anything and everything. The good news about this course is that you don’t need advanced knowledge in Python nor in any programming language. All you need is just simple knowledge of how to create simple Python functions/scripts.

Learning NumPy is very important if you want to either use it in machine learning and data science or on its own for mathematical purposes. And this course gives you the opportunity to learn that in a simple step-by-step approach. 

By the end of this course, you will have learned how to use NumPy, how to create array, matrices, call, mathematical, and statistical functions of NumPy.

Why should you take this course?

There are many reasons why this course is extremely important.

  1. You will learn one of the most important libraries that is used extensively in data science and machine learning.
  2. Secondly you will improve your Python skills by learning a new skill.
  3. You will be able to use NumPy for mathematical purposes.

Note: The project that will be created throughout this course will be provided so that you can download it and use it for commercial and non-commercial purposes.

Our Promise to You

By the end of this course, you will have learned a good foundation of NumPy.

10 Day Money Back Guarantee. If you are unsatisfied for any reason, simply contact us and we’ll give you a full refund. No questions asked.

Get started today and learn more about programming in NumPy.

Course Curriculum

Section 1 - Introduction
Introduction 00:00:00
Which IDE Should Be Used? 00:00:00
Installing Python – MAC Users 00:00:00
Installing Python – Windows Users 00:00:00
Installing NumPy 00:00:00
Important Functions 00:00:00
Section 2 - NumPy
Project Folder 00:00:00
Run File 00:00:00
Import NumPy 00:00:00
Array Info 00:00:00
Arrange 00:00:00
Reshape 00:00:00
Array Of Zeros 00:00:00
Array Of Ones 00:00:00
Steps 00:00:00
Big Arrays 00:00:00
Math Operations 00:00:00
2-Dimensional Math 00:00:00
More On Math 00:00:00
Math Functions 1 00:00:00
Math Functions 2 00:00:00
Comparison 00:00:00
Exponential 00:00:00
Sum 00:00:00
Root 00:00:00
Sub Array 00:00:00
Statistical Functions 00:00:00
For 00:00:00
NumPy Doc 00:00:00

About This Course

Who this course is for:

  • Python developers (All levels)
  • Programmers who want to learn NumPy

What you’ll learn: 

  • How to install Python
  • Create Python files
  • Run Python files
  • How to use NumPy
  • How to create arrays in NumPy
  • How to apply mathematical functions to arrays and matrices
  • How to apply statistical functions to arrays and matrices

Requirements: 

  • Basic knowledge in Python (How to create simple python scripts)

This course will teach you how to get started with Numpy. Python was described as the language that is capable of doing anything and everything. The good news about this course is that you don’t need advanced knowledge in Python nor in any programming language. All you need is just simple knowledge of how to create simple Python functions/scripts.

Learning NumPy is very important if you want to either use it in machine learning and data science or on its own for mathematical purposes. And this course gives you the opportunity to learn that in a simple step-by-step approach. 

By the end of this course, you will have learned how to use NumPy, how to create array, matrices, call, mathematical, and statistical functions of NumPy.

Why should you take this course?

There are many reasons why this course is extremely important.

  1. You will learn one of the most important libraries that is used extensively in data science and machine learning.
  2. Secondly you will improve your Python skills by learning a new skill.
  3. You will be able to use NumPy for mathematical purposes.

Note: The project that will be created throughout this course will be provided so that you can download it and use it for commercial and non-commercial purposes.

Our Promise to You

By the end of this course, you will have learned a good foundation of NumPy.

10 Day Money Back Guarantee. If you are unsatisfied for any reason, simply contact us and we’ll give you a full refund. No questions asked.

Get started today and learn more about programming in NumPy.

Course Curriculum

Section 1 - Introduction
Introduction 00:00:00
Which IDE Should Be Used? 00:00:00
Installing Python – MAC Users 00:00:00
Installing Python – Windows Users 00:00:00
Installing NumPy 00:00:00
Important Functions 00:00:00
Section 2 - NumPy
Project Folder 00:00:00
Run File 00:00:00
Import NumPy 00:00:00
Array Info 00:00:00
Arrange 00:00:00
Reshape 00:00:00
Array Of Zeros 00:00:00
Array Of Ones 00:00:00
Steps 00:00:00
Big Arrays 00:00:00
Math Operations 00:00:00
2-Dimensional Math 00:00:00
More On Math 00:00:00
Math Functions 1 00:00:00
Math Functions 2 00:00:00
Comparison 00:00:00
Exponential 00:00:00
Sum 00:00:00
Root 00:00:00
Sub Array 00:00:00
Statistical Functions 00:00:00
For 00:00:00
NumPy Doc 00:00:00
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