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机器学习(Machine Learning, ML)是一种应用人工智能(AI)领域的科学技术,它使得计算机系统能够从数据中自动学习和改进,而无需显式编程。在机器学习的过程中,算法会通过识别和挖掘数据中的模式来构建一个模型,这个模型可以用于做出预测、分类、聚类、回归以及其他复杂的数据驱动决策。 机器学习的主要类型包括监督学习、无监督学习和半监督学习。在监督学习中,算法根据已知标签的数据集进行训练,以便对未知数据进行预测,例如分类任务(判断邮件是否为垃圾邮件)或回归任务(预测房价)。无监督学习则是在没有标签的情况下,仅凭数据本身的特征发现其中隐藏的结构或模式,如聚类分析(将用户分组到不同的客户细分群体中)。半监督学习介于两者之间,它部分数据有标签,部分数据无标签。 机器学习算法涵盖众多方法,包括但不限于逻辑回归、决策树、随机森林、支持向量机、K近邻算法、神经网络以及深度学习技术等。随着计算能力的增强和大数据时代的到来,机器学习已经在诸多领域展现出强大的功能,如图像识别、语音识别、自然语言处理、推荐系统、金融风控、医疗诊断等。 此外,机器学习的发展与统计学习、逼近论、凸优化、概率论等诸多数学和计算机科学领域紧密相关,且不断地推动着新算法和理论框架的创新与发展。同时,现代机器学习尤其是深度学习模型的内部工作机制有时难以完全解释,因此常被称为“黑箱”决策过程,这也是当前研究中的一个重要挑战。
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学习机器学习实战,主要参考apachecn的代码了解书中的思想。.zip (2000个子文件)
README.md 99B
KNN.py 12KB
svm-simple.py 10KB
datingTestSet.txt 34KB
testSet.txt 2KB
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