Data Science Prerequisites - Numpy, Matplotlib, and Pandas in Python - Plotting Images

The video tutorial covers the basics of handling image data sets in machine learning, focusing on downloading images using wget, loading them with the Pillow library, and converting them into numpy arrays. It explains the structure of image data, including dimensions and color channels, and demonstrates how to plot images using Matplotlib, both in color and grayscale. The tutorial also touches on the concept of heat maps and how to adjust color mappings in plots.

What is a common characteristic of machine learning image datasets like MNIST?

• They are stored as JPEG files.

• They are stored in a single file format.

• They are always in color.

• They are only used for training neural networks.

Which library is used to load images in the tutorial?

• Pillow

• Matplotlib

• Scikit-learn

• OpenCV

What type of data structure is used to represent images in computers?

• Graph

• Array

• Binary Tree

• Linked List

What do the three dimensions of an image array represent?

• Height, Width, and Depth

• Height, Depth, and Time

• Width, Depth, and Time

• Height, Width, and Color Channels

How are colors represented in an image?

• Using hexadecimal codes

• Using CMYK values

• Using RGB values

• Using a single grayscale value

What function is used to display images in matplotlib?

• plt.show()

• plt.display()

• plt.imshow()

• plt.plot()

How can a color image be converted to grayscale in numpy?

• By taking the minimum value across color channels

• By converting to binary

• By taking the maximum value across color channels

• By taking the mean across color channels