Numpy Python Tutorial for Machine Learning

In this video, you’ll learn about Numpy Python for Machine Learning and the basics of the Numpy Python Library. This library is essential to Machine Learning. You’ll learn about how to convert Python lists and matrices into Numpy arrays; how to utilize built-in functions unique to the Numpy library such as np.zeros(), np.ones(), np.eye(), and np.arange(); what the methods and attributes of the Numpy Library are including reshape(), max(), min(), argmax(), and argmin(); and how to use np.random with specific commands such as randint(), rand(), and randn() to generate random arrays and matrices.

After watching this video and learning the basics of Numpy, there’s an activity right below this description for you to try out on your own using the skills you learned here. Enjoy!

NUMPY ACTIVITY:

Create a random 1 dimensional numpy array with some values larger than 1 and some smaller than -1 and reshape this array to be 2 or more rows.

TIMESTAMP:

00:00 Introduction & Recap of Last Video
00:34 Lesson Structure for Numpy Array Basics Video
01:02 Numpy Array using Python List and Matrix
02:24 Numpy Array using Built-In Numpy Functions
06:28 Numpy Array Methods and Attributes
08:55 Numpy Array using Random
10:28 Call-to-action: Numpy Array Basics

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