NumPy Python Programming Language Library from Scratch A-Z™

Hello there,

Welcome to “NumPy Python Programming Language Library from Scratch A-Z™” Course

NumPy Library for Data Science, Machine Learning,Pandas, Deep Learning using Python from A-Z with the NumPy stack course

Numpy is a library for the Python programming language, adding support for large, multi-dimensional arrays and matrices, along with a large collection of high-level mathematical functions to operate on these arrays. Moreover, Numpy forms the foundation of the Machine Learning stack.

POWERFUL N-DIMENSIONAL ARRAYS: Fast and versatile, the NumPy vectorization, indexing, and broadcasting concepts are the de-facto standards of array computing today.

NUMERICAL COMPUTING TOOLS: NumPy offers comprehensive mathematical functions, random number generators, linear algebra routines, Fourier transforms, and more.

INTEROPERABLE: NumPy supports a wide range of hardware and computing platforms, and plays well with distributed, GPU, and sparse array libraries.

PERFORMANT: The core of NumPy is well-optimized C code. Enjoy the flexibility of Python with the speed of compiled code.

EASY TO USE: NumPy’s high level syntax makes it accessible and productive for programmers from any background or experience level.

OPEN SOURCE: Distributed under a liberal BSD license, NumPy is developed and maintained publicly on GitHub by a vibrant, responsive, and diverse community.

Nearly every scientist working in Python draws on the power of NumPy.

OAK Academy offers highly-rated data science courses that will help you learn how to visualize and respond to new data, as well as develop innovative new technologies. Whether you’re interested in machine learning, data mining, or data analysis, has a course for you.

Data science is everywhere. Better data science practices are allowing corporations to cut unnecessary costs, automate computing, and analyze markets. Essentially, data science is the key to getting ahead in a competitive global climate.

Python Numpy, Python instructors on OAK Academy specialize in everything from software development to data analysis, and are known for their effective, friendly instruction for students of all levels.

The core programming language is quite small and the standard library is also large. In fact, Python's large library is one of its greatest benefits, providing a variety of different tools for programmers suited for many different tasks.

• Are you ready for a Data Science career?

• Do you want to learn the Python Numpy from Scratch? or

• Are you an experienced Data scientist and looking to improve your skills with Numpy!

In both cases, you are at the right place! The number of companies and enterprises using Python is increasing day by day. The world we are in is experiencing the age of informatics. Python and its Numpy library will be the right choice for you to take part in this world and create your own opportunities,

In this course, we will open the door of the Data Science world and will move deeper. You will learn the fundamentals of Python and its beautiful library Numpy step by step with hands-on examples. Most importantly in Data Science, you should know how to use effectively the Numpy library. Because this library is limitless.

In this course you will learn;

• Installing Anaconda Distribution for Windows

• Installing Anaconda Distribution for MacOs

• Installing Anaconda Distribution for Linux

• Introduction to NumPy Library

• The Power of NumPy

• Creating NumPy Array with The Array() Function

• Creating NumPy Array with Zeros() Function

• Creating NumPy Array with Ones() Function

• Creating NumPy Array with Full() Function

• Creating NumPy Array with Arange() Function

• Creating NumPy Array with Eye() Function

• Creating NumPy Array with Linspace() Function

• Creating NumPy Array with Random() Function

• Properties of NumPy Array

• Reshaping a NumPy Array: Reshape() Function

• Identifying the Largest Element of a Numpy Array: Max(), Argmax() Functions

• Detecting Least Element of Numpy Array: Min(), Argmin() Functions

• Concatenating Numpy Arrays: Concatenate() Function

• Splitting One-Dimensional Numpy Arrays: The Split() Function

• Splitting Two-Dimensional Numpy Arrays: Split(), Vsplit, Hsplit() Function

• Sorting Numpy Arrays: Sort() Function

• Indexing Numpy Arrays

• Slicing One-Dimensional Numpy Arrays

• Slicing Two-Dimensional Numpy Arrays

• Assigning Value to One-Dimensional Arrays

• Assigning Value to Two-Dimensional Array

• Fancy Indexing of One-Dimensional Arrrays

• Fancy Indexing of Two-Dimensional Arrrays

• Combining Fancy Index with Normal Indexing

• Combining Fancy Index with Normal Slicing

• Fancy Indexing of One-Dimensional Arrrays

• Fancy Indexing of Two-Dimensional Arrrays

• Combining Fancy Index with Normal Indexing

• Combining Fancy Index with Normal Slicing

Reed courses certificate of completion

Digital certificate - Included

• Course Intro 01:10

  • 1: NumPy Python Programming Language Library from Scratch A-Z™ Preview 01:10

• Installations 33:35

  • 2: Installing Anaconda Distribution For Windows 10:35
  • 3: Installing Anaconda Distribution For Mac 06:17
  • 4: Installing Anaconda Distribution For Linux 14:43
  • 5: Notebook Project Files Link regarding NumPy Python Programming Language Library 01:00
  • 6: 6 Article Advice And Links about Numpy, Numpy Pyhon 01:00

• NumPy Library Introduction 23:28

  • 7: Intro to Numpy Library 06:24
  • 8: The Power of Numpy 16:04
  • 9: quiz 01:00

• Creating NumPy Array in Python 41:43

  • 10: Create a Numpy Array with the Array () Function 08:16
  • 11: Create A Numpy Array with The zeros() function 05:05
  • 12: Create A Numpy Array with The ones() function 03:06
  • 13: Create A Numpy Array with The full() function 02:49
  • 14: Creating NumPy Array with Arrange() Function 02:55
  • 15: Create A Numpy Array with The Eye() function 03:08
  • 16: Creating NumPy Array with Linspace() Function 01:31
  • 17: Creating NumPy Array with Random() Function 08:29
  • 18: Properties of NumPy Array 05:24
  • 19: quiz 01:00

• Functions in the NumPy Library 41:32

  • 20: Reshaping a Numpy Array Reshape() Function 05:57
  • 21: Identifying the Largest Element of a Numpy Array Max(), Argmax() Functions 03:45
  • 22: Detecting Least Element of Numpy Array Min(), Argmin() Functions 02:35
  • 23: Concatenating Numpy Arrays Concatenate() Function 09:40
  • 24: Splitting One-Dimensional Numpy Arrays The Split() Function 05:46
  • 25: Splitting Two-Dimensional Numpy Arrays Split(), Vsplit, Hsplit() Function 09:33
  • 26: Sorting Numpy Arrays Sort() Function 04:16

• Indexing, Slicing, and Assigning NumPy Arrays 1:04:58

  • 27: Indexing Numpy Arrays 07:39
  • 28: Slicing One-Dimensional Numpy Arrays 06:08
  • 29: Slicing Two-Dimensional Numpy Arrays 09:30
  • 30: Assigning Value to One-Dimensional Arrays 05:02
  • 31: Assigning Value to two-Dimensional Arrays 09:57
  • 32: Fancy Indexing of One-Dimensional Arrrays 06:09
  • 33: Fancy Indexing of Two-Dimensional Arrrays 12:32
  • 34: Combining Fancy Index with Normal Indexing 03:25
  • 35: Combining Fancy Index with Normal Slicing 04:36

• Operations in Numpy Library 34:54

  • 36: Operations with Comparison Operators 06:09
  • 37: Arithmetic Operations in Numpy 15:10
  • 38: Statistical Operations in Numpy 06:35
  • 39: Solving second-degree equations with NumPy 07:00

• Extra 01:00

  • 40: NumPy Python Programming Language Library from Scratch A-Z™ 01:00

Hello there,

Welcome to “NumPy Python Programming Language Library from Scratch A-Z™” Course

NumPy Library for Data Science, Machine Learning,Pandas, Deep Learning using Python from A-Z with the NumPy stack course

Numpy is a library for the Python programming language, adding support for large, multi-dimensional arrays and matrices, along with a large collection of high-level mathematical functions to operate on these arrays. Moreover, Numpy forms the foundation of the Machine Learning stack.

NumPy aims to provide an array object that is up to 50x faster than traditional Python lists. The array object in NumPy is called ndarray , it provides a lot of supporting functions that make working with ndarray very easy. Arrays are very frequently used in data science, where speed and resources are very important. numpy, numpy stack, numpy python, scipy, Python numpy, deep learning, artificial intelligence, lazy programmer, pandas, machine learning, Data Science, Pandas, Deep Learning, machine learning python, numpy course

POWERFUL N-DIMENSIONAL ARRAYS: Fast and versatile, the NumPy vectorization, indexing, and broadcasting concepts are the de-facto standards of array computing today.

NUMERICAL COMPUTING TOOLS: NumPy offers comprehensive mathematical functions, random number generators, linear algebra routines, Fourier transforms, and more.

INTEROPERABLE: NumPy supports a wide range of hardware and computing platforms, and plays well with distributed, GPU, and sparse array libraries.

PERFORMANT: The core of NumPy is well-optimized C code. Enjoy the flexibility of Python with the speed of compiled code.

EASY TO USE: NumPy’s high level syntax makes it accessible and productive for programmers from any background or experience level.

OPEN SOURCE: Distributed under a liberal BSD license, NumPy is developed and maintained publicly on GitHub by a vibrant, responsive, and diverse community.

Nearly every scientist working in Python draws on the power of NumPy.

NumPy brings the computational power of languages like C and Fortran to Python, a language much easier to learn and use. With this power comes simplicity: a solution in NumPy is often clear and elegant.

OAK Academy offers highly-rated data science courses that will help you learn how to visualize and respond to new data, as well as develop innovative new technologies. Whether you’re interested in machine learning, data mining, or data analysis, has a course for you.

Data science is everywhere. Better data science practices are allowing corporations to cut unnecessary costs, automate computing, and analyze markets. Essentially, data science is the key to getting ahead in a competitive global climate.

Python Numpy, Python instructors on OAK Academy specialize in everything from software development to data analysis, and are known for their effective, friendly instruction for students of all levels.

Whether you work in machine learning or finance, or are pursuing a career in web development or data science, Python is one of the most important skills you can learn. Python's simple syntax is especially suited for desktop, web, and business applications. Python's design philosophy emphasizes readability and usability. Python was developed upon the premise that there should be only one way (and preferably one obvious way) to do things, a philosophy that has resulted in a strict level of code standardization.

The core programming language is quite small and the standard library is also large. In fact, Python's large library is one of its greatest benefits, providing a variety of different tools for programmers suited for many different tasks.

• Are you ready for a Data Science career?

• Do you want to learn the Python Numpy from Scratch? or

• Are you an experienced Data scientist and looking to improve your skills with Numpy!

In both cases, you are at the right place! The number of companies and enterprises using Python is increasing day by day. The world we are in is experiencing the age of informatics. Python and its Numpy library will be the right choice for you to take part in this world and create your own opportunities,

In this course, we will open the door of the Data Science world and will move deeper. You will learn the fundamentals of Python and its beautiful library Numpy step by step with hands-on examples. Most importantly in Data Science, you should know how to use effectively the Numpy library. Because this library is limitless.

Throughout the course, we will teach you how to use Python in Linear Algebra and we will also do a variety of exercises to reinforce what we have learned in this Data Science Using Python Programming Language: NumPy Library | A-Z™ course.

In this course you will learn;

• Installing Anaconda Distribution for Windows

• Installing Anaconda Distribution for MacOs

• Installing Anaconda Distribution for Linux

• Introduction to NumPy Library

• The Power of NumPy

• Creating NumPy Array with The Array() Function

• Creating NumPy Array with Zeros() Function

• Creating NumPy Array with Ones() Function

• Creating NumPy Array with Full() Function

• Creating NumPy Array with Arange() Function

• Creating NumPy Array with Eye() Function

• Creating NumPy Array with Linspace() Function

• Creating NumPy Array with Random() Function

• Properties of NumPy Array

• Reshaping a NumPy Array: Reshape() Function

• Identifying the Largest Element of a Numpy Array: Max(), Argmax() Functions

• Detecting Least Element of Numpy Array: Min(), Argmin() Functions

• Concatenating Numpy Arrays: Concatenate() Function

• Splitting One-Dimensional Numpy Arrays: The Split() Function

• Splitting Two-Dimensional Numpy Arrays: Split(), Vsplit, Hsplit() Function

• Sorting Numpy Arrays: Sort() Function

• Indexing Numpy Arrays

• Slicing One-Dimensional Numpy Arrays

• Slicing Two-Dimensional Numpy Arrays

• Assigning Value to One-Dimensional Arrays

• Assigning Value to Two-Dimensional Array

• Fancy Indexing of One-Dimensional Arrrays

• Fancy Indexing of Two-Dimensional Arrrays

• Combining Fancy Index with Normal Indexing

• Combining Fancy Index with Normal Slicing

• Fancy Indexing of One-Dimensional Arrrays

• Fancy Indexing of Two-Dimensional Arrrays

• Combining Fancy Index with Normal Indexing

• Combining Fancy Index with Normal Slicing

Why would you want to take this course?

We have prepared this course in the simplest way for beginners and have prepared many different exercises to help them understand better.

No prior knowledge is needed!

In this course, you need no previous knowledge about Python or Numpy.

This course will take you from a beginner to a more experienced level.

If you are new to data science or have no idea about what data science is, no problem, you will learn anything from scratch you need to start data science.

If you are a software developer or familiar with other programming languages and you want to start a new world, you are also in the right place. You will learn step by step with hands-on examples.

You'll also get:

· Lifetime Access to The Course

· Fast & Friendly Support in the Q&A section

Dive in now NumPy Python Programming Language Library from Scratch A-Z™

NumPy Library for Data Science, Machine Learning,Pandas, Deep Learning using Python from A-Z with the NumPy stack course

We offer full support, answering any questions.

See you in the course!

• Anyone who wants to learn Numpy
• Anyone who want to use effectively linear algebra,
• Software developer whom want to learn the Neural Network’s math,
• Data scientist whom want to use effectively Numpy array
• Anyone interested in data sciences
• Anyone who plans a career in data scientist,
• Anyone eager to learn python with no coding background
• Anyone who is particularly interested in big data, machine learning
• Anyone eager to learn Python with no coding background
• Anyone who wants to learn Numpy

• No prior knowledge of Numpy is required

• Free software and tools used during the course

• Basic computer knowledge

• Desire to learn Python and Numpy library

• Nothing else! It’s just you, your computer and your ambition to get started today

• Desire to learn data science

• Desire to learn Python

• Desire to work on machine learning

• Desire to learn python machine learning A-Z

Currently there are no Q&As for this course. Be the first to ask a question.