章节大纲

  • 机器学习是人工智能 (AI) 的一个子领域。机器学习的目标通常是理解数据的结构并将这些数据拟合到可以被人理解和利用的模型中。

    尽管机器学习是计算机科学领域的一个分支，但它与传统的计算方法有所不同。在传统计算中，算法是计算机用于计算或解决问题的一系列明确编程的指令。而机器学习算法则允许计算机在数据输入上进行训练，并使用统计分析来输出落在特定范围内的值。正因为如此，机器学习促进计算机根据样本数据构建模型，从而基于数据输入实现决策过程的自动化。

    今天，任何技术用户都受益于机器学习。例如，面部识别技术让社交媒体平台能够帮助用户标记和分享朋友的照片；光学字符识别 (OCR) 技术能将文本图像转换为可编辑的文本；由机器学习驱动的推荐引擎会根据用户偏好推荐接下来观看的电影或电视节目；而依赖机器学习进行导航的自动驾驶汽车也可能很快就能面向消费者。

    机器学习是一个持续发展的领域。因此，在您使用机器学习方法或分析机器学习过程的影响时，需要牢记一些注意事项。

    在本教程中，我们将深入探讨机器学习中常见的监督学习和无监督学习方法，以及常见的算法方法，包括 K 近邻算法、决策树学习和深度学习。我们还将探讨机器学习中最常用的编程语言，并为您提供每种语言的优缺点。此外，我们还将讨论机器学习算法中存在的偏见，并思考在构建算法时如何避免这些偏见。

    Machine learning is a subfield of artificial intelligence (AI). The goal of
    machine learning generally is to understand the structure of data and fit
    that data into models that can be understood and utilized by people.
    Although machine learning is a field within computer science, it differs
    from traditional computational approaches. In traditional computing,
    algorithms are sets of explicitly programmed instructions used by
    computers to calculate or problem solve. Machine learning algorithms
    instead allow for computers to train on data inputs and use statistical
    analysis in order to output values that fall within a specific range.
    Because of this, machine learning facilitates computers in building
    models from sample data in order to automate decision-making
    processes based on data inputs.
    Any technology user today has benefitted from machine learning.
    Facial recognition technology allows social media platforms to help users
    t a g and share photos of friends. Optical character recognition (OCR)
    technology converts images of text into movable type. Recommendation
    engines, powered by machine learning, suggest what movies or
    television shows to watch next based on user preferences. Self-driving
    cars that rely on machine learning to navigate may soon be available to
    consumers.
    Machine learning is a continuously developing field. Because of this,
    there are some considerations to keep in mind as you work with machine
    learning methodologies, or analyze the impact of machine learning
    processes.
    In this tutorial, we’ll look into the common machine learning methods
    o f supervised and unsupervised learning, and common algorithmic
    approaches in machine learning, including the k-nearest neighbor
    algorithm, decision tree learning, and deep learning. We’ll explore which
    programming languages are most used in machine learning, providing
    y o u with some of the positive and negative attributes of each.
    Additionally, we’ll discuss biases that are perpetuated by machine
    learning algorithms, and consider what can be kept in mind to prevent
    these biases when building algorithms.