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所在平台: Udemy |
课程主页: https://www.udemy.com/course/random-forest-adaboost-decision-trees-in-machine-learning/
课程评论：没有评论
课程名称：机器学习：随机森林、AdaBoost与决策树 课程概述：近年来，人工智能（AI）和机器学习（ML）经历了复兴，已经取得了一些惊人的成果，例如能够分析医学图像并预测疾病，达到了与人类专家相媲美的水平。谷歌的AlphaGo程序利用深度强化学习击败了围棋世界冠军，机器学习甚至被用于编程自动驾驶汽车，这将彻底改变汽车工业。想象一下通过消除人为错误来显著降低汽车事故发生率的世界。谷歌曾宣布他们将“以机器学习为先”，而像NVIDIA和亚马逊这样的公司也纷纷跟随，这将推动未来几年的创新。 机器学习在各行各业都有广泛应用，包括金融、在线广告、医学和机器人技术，是一种对任何行业都有益的工具，并将为你打开许多职业机会。机器学习还引发了一些哲学问题，比如我们是否在构建一个能够思考的机器？意识意味着什么？计算机是否会有一天统治世界？ 本课程专注于集成方法，特别将详细研究随机森林和AdaBoost算法。为激发讨论，我们将学习统计学习中的一个重要主题——偏差-方差权衡，然后研究自助法和装袋法作为同时减少偏差和方差的方法。所有课程材料均为免费提供，学员可以通过简单的命令在Windows、Linux或Mac上下载和安装Python、NumPy和SciPy。 本课程强调“如何构建和理解”，而不仅仅是“如何使用”。任何人只需阅读一些文档就可以在15分钟内学习使用API。本课程不仅仅是“记忆事实”，而是通过实验“自我见证”。它还将教你如何可视化模型内部发生的情况。如果你希望深入了解机器学习模型，而不仅仅是表面的了解，这门课程非常适合你。
In recent years, we've seen a resurgence in AI, or artificial intelligence, and machine learning.Machine learning has led to some amazing results, like being able to analyze medical images and predict diseases on-par with human experts.Google's AlphaGo program was able to beat a world champion in the strategy game go using deep reinforcement learning.Machine learning is even being used to program self driving cars, which is going to change the automotive industry forever. Imagine a world with drastically reduced car accidents, simply by removing the element of human error.Google famously announced that they are now "machine learning first", and companies like NVIDIA and Amazon have followed suit, and this is what's going to drive innovation in the coming years.Machine learning is embedded into all sorts of different products, and it's used in many industries, like finance, online advertising, medicine, and robotics.It is a widely applicable tool that will benefit you no matter what industry you're in, and it will also open up a ton of career opportunities once you get good.Machine learning also raises some philosophical questions. Are we building a machine that can think? What does it mean to be conscious? Will computers one day take over the world?This course is all about ensemble methods.In particular, we will study the Random Forest and AdaBoost algorithms in detail.To motivate our discussion, we will learn about an important topic in statistical learning, the bias-variance trade-off. We will then study the bootstrap technique and bagging as methods for reducing both bias and variance simultaneously.All the materials for this course are FREE. You can download and install Python, NumPy, and SciPy with simple commands on Windows, Linux, or Mac.This course focuses on "how to build and understand", not just "how to use". Anyone can learn to use an API in 15 minutes after reading some documentation. It's not about "remembering facts", it's about "seeing for yourself" via experimentation. It will teach you how to visualize what's happening in the model internally. If you want more than just a superficial look at machine learning models, this course is for you.
