NumPy for Data Science Beginners: 2021

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What you"ll learn:

• Different Numpy function applied as Matrix/Array Operations
• Different Numpy function for Linear Algebra, Statistics, Math
• Numpy for Matplotlib visualization
• Numpy Quizzes

Description

Wanna learn NumPy?

Look no further. This course covers everything from how to install and import NumPy to how to solve complex problems involving array creation, transformations, and random sampling.

Course Structure

The course is presented as a series of on-demand lecture style videos with lots of animated examples, code walkthroughs, and challenge problems to test your knowledge. Go as fast or as slow as you want.

It's difficult to describe everything around us with just one number. The world is multidimensional. The data we are consuming, product we use on daily basis, from non living organism to living organism require many feature to fully characterise and quantify it.

So if you want to learn about fastest python based numerical multi dimensional data processing framework, which is the foundation for many data science package like pandas for data analysis, sklearn scikit-learn for machine learning algorithm, you are at right place.

This course introduce with all majority of concept of NumPy - numerical python library.

I will teach from what and why of NumPy to all important concept of N dimension data processing

This course covers following topics.

• Why and What NumPy is
• NumPy installation
• Creating NumPy array
• Array indexing and slicing
• Array manipulation
• Mathematical & statistical function
• Linear algebra function
• How to persist NumPy array
• Numpy practical application on Images
• RGB Image to Gray scale conversion
• Apply average and edge detection filter on images

Go to my other course needed for Data Scientists. See you inside course.

Happy learning

Abbosjon Madiev

Who this course is for:

• Data Science Beginners
• Anyone who want to learn how to process N dimensional Data
• Anyone who want to learn Numpy - Numerical Python Library.

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