Summary of Machine Learning In Pure Perl - William N. Braswell, Jr.

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In this video, William N. Braswell, Jr. discusses machine learning in pure Perl. He explains that while there are other languages that can do machine learning, pure Perl is more readable and maintainable than Python. He also discusses the performance of Pearl compared to other languages.

  • 00:00:00 This video presents the history of machine learning, its different types, and its applications in business. William Braswell, Jr. explains that machine learning is a subset of artificial intelligence, which is itself a subset of mathematics. He goes on to say that, in order to be employable in the computer programming field, having knowledge of machine learning is a valuable asset.
  • 00:05:00 In this video, William N. Braswell, Jr. discusses machine learning in pure Perl. He says that while there are other languages that can do machine learning, pure Perl is more readable and maintainable than Python. He also discusses the performance of Pearl compared to other languages.
  • 00:10:00 The video discusses the differences between machine learning algorithms in Python and in other languages. It also points out that there is little pearl in machine learning, meaning that the algorithms are not as easy to use as they are in other languages.
  • 00:15:00 In this video, William N. Braswell, Jr. discusses how machine learning can be implemented in Perl using the k-nearest neighbors algorithm. He also notes that the algorithm can be generalized to any number of dimensions, and that there is no need to assign any classification to the data points before running the algorithm.
  • 00:20:00 In this video, William N. Braswell, Jr. discusses machine learning in Perl. He shows how to determine the three nearest neighbors of a data point using a distance function, and how this affects the classification of the data point.
  • 00:25:00 The three distance functions covered in this video are the Manhattan, Euclidean, and least squares distances. The Manhattan distance is the default distance function and is used for data that is two-dimensional. The Euclidean distance is used for data that is three-dimensional and the least squares distance is used for data that is two-dimensional and has non-zero distances between points.
  • 00:30:00 The video discusses the three distance metrics that can be used when analyzing data: Euclidean, Manhattan, and Minkowski. The Euclidean and Manhattan distances are the most common, but the Minkowski metric is more complicated but more accurate. The video shows how to calculate the runtime for different Python implementations of the Minkowski algorithm using the sklearn library.
  • 00:35:00 This video shows how the speed of two different programming languages, Python and Pearl, changes as the size and complexity of the training data increases. Python becomes faster as the data size and runtime increase, eventually becoming 50 times faster than Pearl.
  • 00:40:00 This video explains how compiled perl code runs faster than interpreted perl code. Python code eventually catches up to compiled perl code, but it takes a long time for Python to reach that point.
  • 00:45:00 This video introduces William N. Braswell, Jr., a Perl programmer, and discusses his experiences with machine learning. Braswell notes that Python is relatively fast, but Pearl is much slower, and Pearl's low speed is due to scientific notation. He asks for help from his viewers, and promises that the machine will eventually learn on its own.
  • 00:50:00 This video talks about how to use machine learning in pure Perl using the William N. Braswell, Jr. book, "Perl and Perl 6 for Machine Learning." The author discusses how to implement the algorithms needed, and how to make sure the data is correct. He also encourages others to try machine learning in Perl, and signs off with a farewell message.

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