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Numpy Mastery
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Course Syllabus and Details
How to Memorize Numpy Syntax (16:31)
How to Become "Fluent" in Numpy, as Fast as Possible (11:01)
Setup
Install Anaconda (5:06)
A Quick Tour of Anaconda Navigator (2:22)
Check Package Installation (3:35)
A Quick Tour of the Spyder IDE (8:00)
Install Practice Software (2:56)
Numpy Basics
Numpy Basics (5:16)
Array Attributes (size, shape, dtype, etc) (9:35)
Numpy Axes Explained (8:36)
The Numpy Array function (8:55)
Practice exercises (43:33)
Important Numpy Array Creation and Manipulation Methods
Numpy Arange (22:02)
Numpy Linspace (22:35)
Practice Exercises (2:22)
Rearranging numpy arrays
Numpy Reshape (23:48)
Sort (17:47)
Flatten (13:25)
Practice exercises (16:57)
Zeros, Ones, Empty, and Full
Numpy Ones, Zeros, Empty, and Full
Numpy Zeros (10:49)
Numpy Ones (7:44)
Numpy Empty (11:54)
Numpy Full (9:44)
Practice Exercises (18:29)
Probability Distributions and Random Numbers
How to Understand Numpy Random Seed (20:53)
Randint (12:29)
Rand and Random Uniform (12:22)
Random Choice (24:30)
Random Normal (20:37)
Practice Exercises (18:20)
Combining Numpy Arrays
VStack (11:27)
Hstack (10:10)
Concatenate (16:26)
Append (16:55)
Repeat (12:19)
Tile (16:46)
Practice exercises (8:42)
Splitting arrays
Vsplit (12:24)
Hsplit (12:16)
Split (18:03)
Practice exercises (5:44)
Numpy Statistical Functions
Min (13:37)
Max (11:25)
Mean (13:50)
Median (15:13)
Sum (10:44)
Standard Deviation (16:09)
Practice exercises (5:52)
Numpy Math Functions
Log (9:27)
Exponential (9:31)
Square Root (7:21)
Power (13:43)
Practice exercises (4:19)
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BONUS: Numpy Cheat Sheet
Numpy Cheat Sheet
How to Understand Numpy Random Seed
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