SkillSoft Explore Course – Lambers, Inc.

Aspire Pythonista to Python Master Python Master Track 5: Resource Optimization with Python

Final Exam: Resource Optimization with Python will test your knowledge and application of the topics presented throughout the Resource Optimization with Python track of the Aspire Pythonista to Python Master Journey.

Asset ID	it_feppm_05_enus
Course Type	Video Course
Course Category	Pythonista to Python Master

Objectives
Final Exam: Resource Optimization with Python add noise to an image add noise to an image and apply a blur add noise to an image and apply a blur which obscures minute details in an image aggregate data on a per-key, per-window basis apply cv2.resize to scale up an image along individual dimensions apply the Laplacian operator to detect the edges in an image apply the Laplacian, Sobel and Canny operators to detect the edges in an image compute aggregations on streaming data contrast tumbling windows and hopping windows create a workspace for the demos and install OpenCV from a Jupyter notebook create models with multiple fields and different data types draw a polygon and an arrow in an OpenCV image and introduce a text element forward messages to destination topics handle GET, PUT, POST, DELETE, HTTP requests with web views identify attributes of hopping tumbling identify attributes of tumbling windows identify the components that make up the architecture of a stream processing system identify the differences between event time, ingestion time, and processing time identify the different kinds of sinks that can be used with a Faust agent identify the results of bitwise AND, OR, NOT and XOR operations on images implement event time hopping windows implement gaussian and median blur operations in order to smooth an image implement processing time tumbling windows implement the cv2.resize method to reduce the size of a color image implement the "faust" command to run workers and send messages to agents implement the key index to iterate over keys, values, and items in windowed tables implement the subtract method in OpenCV to perform a subtract operation between two images implement trained classifiers to detect eyes, faces and people in images invoke the cast() method to await processing results from an agent list the components that make up the architecture of a stream processing system load images from your file system into an OpenCV array and then perform the reverse operation by saving an array into a local file perform a variety of translations and rotations in increments of 90 degrees in order to orient an image according to your specifications perform gaussian and median blur operations in order to smooth an image perform group-by operations on streams perform grouping operations and understand table sharding plot a circle, line, rectangle and ellipse in an image publish messages to a Kafka topic using the pykafka library read a color image into your Python source read a color image into your Python source as a grayscale image read a color image into your Python source as a grayscale image and view it using an interactive window recall the differences between event time, ingestion time, and processing time recall the different kinds of sinks that can be used with a Faust agent recall the important characteristics of the Faust stream processing applications recognize the results of bitwise AND, OR, NOT and XOR operations on images recognize the use of the BGR and RGB color spaces used by OpenCV and the Pillow libraries save table state to an embedded RocksDB database send and receive messages using channels separate a color image into blue, green and red channels use channels to send and receive messages use models to represent stream elements use the add and addWeighted methods in OpenCV to combine two images use the cv2.resize method to reduce the size of a color image use the "faust" command to run workers and send messages to agents use the key index to iterate over keys, values, and items in windowed tables use the pykafka library to publish messages to a Kafka topic use the subtract method in OpenCV to perform a subtract operation between two images use trained classifiers to detect eyes, faces and people in images use trained classifiers to detect faces and people in images use web views to handle GET, PUT, POST, DELETE, HTTP requests using trained classifiers to detect faces, eyes and people in images

Objectives

Final Exam: Resource Optimization with Python

add noise to an image
add noise to an image and apply a blur
add noise to an image and apply a blur which obscures minute details in an image
aggregate data on a per-key, per-window basis
apply cv2.resize to scale up an image along individual dimensions
apply the Laplacian operator to detect the edges in an image
apply the Laplacian, Sobel and Canny operators to detect the edges in an image
compute aggregations on streaming data
contrast tumbling windows and hopping windows
create a workspace for the demos and install OpenCV from a Jupyter notebook
create models with multiple fields and different data types
draw a polygon and an arrow in an OpenCV image and introduce a text element
forward messages to destination topics
handle GET, PUT, POST, DELETE, HTTP requests with web views
identify attributes of hopping tumbling
identify attributes of tumbling windows
identify the components that make up the architecture of a stream processing system
identify the differences between event time, ingestion time, and processing time
identify the different kinds of sinks that can be used with a Faust agent
identify the results of bitwise AND, OR, NOT and XOR operations on images
implement event time hopping windows
implement gaussian and median blur operations in order to smooth an image
implement processing time tumbling windows
implement the cv2.resize method to reduce the size of a color image
implement the "faust" command to run workers and send messages to agents
implement the key index to iterate over keys, values, and items in windowed tables
implement the subtract method in OpenCV to perform a subtract operation between two images
implement trained classifiers to detect eyes, faces and people in images
invoke the cast() method to await processing results from an agent
list the components that make up the architecture of a stream processing system
load images from your file system into an OpenCV array and then perform the reverse operation by saving an array into a local file
perform a variety of translations and rotations in increments of 90 degrees in order to orient an image according to your specifications
perform gaussian and median blur operations in order to smooth an image
perform group-by operations on streams
perform grouping operations and understand table sharding
plot a circle, line, rectangle and ellipse in an image
publish messages to a Kafka topic using the pykafka library
read a color image into your Python source
read a color image into your Python source as a grayscale image
read a color image into your Python source as a grayscale image and view it using an interactive window
recall the differences between event time, ingestion time, and processing time
recall the different kinds of sinks that can be used with a Faust agent
recall the important characteristics of the Faust stream processing applications
recognize the results of bitwise AND, OR, NOT and XOR operations on images
recognize the use of the BGR and RGB color spaces used by OpenCV and the Pillow libraries
save table state to an embedded RocksDB database
send and receive messages using channels
separate a color image into blue, green and red channels
use channels to send and receive messages
use models to represent stream elements
use the add and addWeighted methods in OpenCV to combine two images
use the cv2.resize method to reduce the size of a color image
use the "faust" command to run workers and send messages to agents
use the key index to iterate over keys, values, and items in windowed tables
use the pykafka library to publish messages to a Kafka topic
use the subtract method in OpenCV to perform a subtract operation between two images
use trained classifiers to detect eyes, faces and people in images
use trained classifiers to detect faces and people in images
use web views to handle GET, PUT, POST, DELETE, HTTP requests
using trained classifiers to detect faces, eyes and people in images