Python NumPy Module Full Crash Course for Machine Learning
Python NumPy Module Full Crash Course for Machine Learning
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Chapters:
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0:00 - Introduction to NumPy in Python
🟥 Understanding, Creating and Working with Arrays
0:44 - 1D arrays
2:42 - Type Promotion
5:38 - 2D arrays
7:45 - 3D and n-dimensional arrays
12:12 - astype function
🟥 Reshaping the Arrays
13:45 - reshape
16:41 - reshape(-1,1) & reshape(1,-1)
🟥 Indexing and Slicing
20:46 - indexing and slicing
30:01 - Boolean Mask Indexing
🟥 IMPORTANT Functions to Build Arrays
35:42 - arange function
39:48 - linspace function
41:12 - ones and zeros function
43:25 - eye function
46:32 - empty function
🟥 Axis Meaning and How to Use it
48:41 - axis meaning
🟥 Some Useful Functions You NEED to Know
56:27 - min, max, argmin and argmax
58:48 - sort, argsort
01:01:50 - unique
🟥 Statistics
01:04:18 - mean and median
01:08:20 - standard deviation
🟥 Linear Algebra
01:14:11 - How to work with matrices in python (a lot of matrix-related functions are studied in this section)
🟥 View Vs Deep Copy
01:25:15 - view VS deep copy
🟥 Manipulating the Arrays
01:30:08 - ravel VS flatten
01:36:07 - insert
01:39:24 - delete
01:41:29 - concatenate
01:44:33 - append
🟥 Working with Polynomials
01:48:20 - poly1d
01:52:59 - polyfit
🟥 Norms and Distances
01:58:05 - L1 and L2 norm (+ Manhattan, Euclidean and Minkowski Distance)
02:04:39 - Frobenius norm
🟥 NumPy Random
02:06:34 - NumPy random module (a lot of functions are studied in this section)
02:17:09 - NumPy random seed
To my opinion this is a very good video tutorial for learning numpy module in Python for machine learning and data science.
Auf dieser Seite können Sie das Online-Video Python NumPy Module Full Crash Course for Machine Learning mit der Dauer stunde minuten sekunde in guter Qualität ansehen, das der Benutzer Koolac 27 Oktober 2022 hochgeladen hat, den Link mit Freunden und Bekannten teilen, dieses Video wurde auf Youtube bereits 610 Mal angesehen und es wurde von 22 den Zuschauern gefallen. Viel Spaß beim Betrachtenden Zuschauern gefallen!
