Final Exam: Resource Optimization with Python

Python 3.6+    |    Intermediate
  • 1 Video | 30m 32s
  • Includes Assessment
  • Earns a Badge
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 Skillsoft Aspire Pythonista to Python Master Journey.

WHAT YOU WILL LEARN

  • 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

IN THIS COURSE

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    Resource Optimization with Python
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