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Numpy Complete Tutorial For Machine Learning

Machine Learning Prerequisite


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Description

There are  various library functions available in python for building up machine learning models and end up writing code for pre-existing functions using sheer logic which is a waste of both time and energy, in such times it becomes essential if one understands the nuances of the Library being used efficiently. So Numpy being one of the essential libraries for Machine Learning requires deep understanding ,this is the course where we have discussed everything on numpy and this will help you to develop your skills required for machine learning.

  • Fast and versatile, the NumPy vectorization, indexing, and broadcasting concepts are the de-facto standards of array computing today.

  • NumPy offers comprehensive mathematical functions, random number generators, linear algebra routines, Fourier transforms, and more.

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

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

  • NumPy’s high level syntax makes it accessible and productive for programmers from any background or experience level.

    This is a quick overview of arrays in NumPy. It demonstrates how n-dimensional (n>=2) arrays are represented and can be manipulated. In particular, if you don’t know how to apply common functions to n-dimensional arrays (without using for-loops), or if you want to understand axis and shape properties for n-dimensional arrays, this article might be of help.

    Learning Objectives

    After reading, you should be able to:

    • Understand the difference between one-, two- and n-dimensional arrays in NumPy;

    • Understand how to apply some linear algebra operations to n-dimensional arrays without using for-loops;

    • Understand axis and shape properties for n-dimensional arrays.

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