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

Created by Oak Academy, Ali CAVDAR | 4 hours on-demand video course

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.

What you’ll 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

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