Читать книгу Machine Learning For Dummies - John Paul Mueller, John Mueller Paul, Luca Massaron - Страница 22

Even though the movies make it sound like AI is going to make a huge splash, and you do sometimes see some incredible uses for AI in real life, the fact of the matter is that most uses for AI are mundane, even boring. For example, a recent article details how Verizon uses the R language to analyze security breach data (https://www.computerworld.com/article/3001832/data-analytics/how-verizon-analyzes-security-breach-data-with-r.html and https://softwarestrategiesblog.com/category/verizons-2020-data-breach-investigations-report-dbir/). Part 5 of this book provides you with real-world examples of this same sort of analysis. The act of performing this analysis is dull when compared to other sorts of AI activities, but the benefits are that Verizon saves money performing the analysis using R, and the results are better as well.

In addition, Python developers (see Chapters 4 and 5 for Python language details) have a huge array of libraries available to make machine learning easy. In fact, Kaggle (https://www.kaggle.com/competitions) provides competitions to allow Python developers and R practitioners to hone their machine learning skills in creating practical applications. The results of these competitions often appear later as part of products that people actually use. Although R still relies on strong support from the statistical community in academic research, the Python development community is particularly busy creating new libraries to make development of complex data science and machine learning applications easier (see https://www.globalsqa.com/top-20-open-source-python-libraries/ for the top 20 Python libraries in use today).