Numpy Mastery

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• Course Syllabus and Details Start
• How to Memorize Numpy Syntax (16:31) Start
• How to Become "Fluent" in Numpy, as Fast as Possible (11:01) Start

  Setup

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• Install Anaconda (5:06) Start
• A Quick Tour of Anaconda Navigator (2:22) Start
• Check Package Installation (3:35) Start
• A Quick Tour of the Spyder IDE (8:00) Start
• Install Practice Software (2:56) Start

  Numpy Basics

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• Numpy Basics (5:16) Start
• Array Attributes (size, shape, dtype, etc) (9:35) Start
• Numpy Axes Explained (8:36) Start
• The Numpy Array function (8:55) Start
• Practice exercises (43:33) Start

  Important Numpy Array Creation and Manipulation Methods

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• Numpy Arange (22:02) Start
• Numpy Linspace (22:35) Start
• Practice Exercises (2:22) Start

  Rearranging numpy arrays

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• Numpy Reshape (23:48) Start
• Sort (17:47) Start
• Flatten (13:25) Start
• Practice exercises (16:57) Start

  Zeros, Ones, Empty, and Full

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• Numpy Ones, Zeros, Empty, and Full Start
• Numpy Zeros (10:49) Start
• Numpy Ones (7:44) Start
• Numpy Empty (11:54) Start
• Numpy Full (9:44) Start
• Practice Exercises (18:29) Start

  Probability Distributions and Random Numbers

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• How to Understand Numpy Random Seed (20:53) Start
• Randint (12:29) Start
• Rand and Random Uniform (12:22) Start
• Random Choice (24:30) Start
• Random Normal (20:37) Start
• Practice Exercises (18:20) Start

  Combining Numpy Arrays

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• VStack (11:27) Start
• Hstack (10:10) Start
• Concatenate (16:26) Start
• Append (16:55) Start
• Repeat (12:19) Start
• Tile (16:46) Start
• Practice exercises (8:42) Start

  Splitting arrays

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• Vsplit (12:24) Start
• Hsplit (12:16) Start
• Split (18:03) Start
• Practice exercises (5:44) Start

  Numpy Statistical Functions

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• Min (13:37) Start
• Max (11:25) Start
• Mean (13:50) Start
• Median (15:13) Start
• Sum (10:44) Start
• Standard Deviation (16:09) Start
• Practice exercises (5:52) Start

  Numpy Math Functions

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• Log (9:27) Start
• Exponential (9:31) Start
• Square Root (7:21) Start
• Power (13:43) Start
• Practice exercises (4:19) Start

  We'd love to get your feedback

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  BONUS: Numpy Cheat Sheet

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• Numpy Cheat Sheet Preview