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所在平台: Udemy |
课程主页: https://www.udemy.com/course/machine-learning-in-python-random-forest-adaboost/
课程评论：没有评论
课程名称: Python中的集成机器学习：随机森林与AdaBoost 课程概述: 近年来，人工智能（AI）和机器学习（ML）经历了复兴，带来了显著的成果，例如在医学图像分析和疾病预测方面取得与人类专家相当的效果。谷歌的AlphaGo程序使用深度增强学习战胜世界围棋冠军，机器学习正在改变汽车行业，促进无人驾驶汽车的发展，从而显著减少交通事故。这一趋势推动了各大公司（如谷歌、NVIDIA和亚马逊）以“机器学习优先”的方式推动创新，机器学习在金融、在线广告、医学和机器人等多个行业得到了广泛应用，为各个行业的从业者开辟了新的职业机遇。 本课程集中于集成方法。我们将学习如何结合经典机器学习模型，如k-近邻和决策树，克服它们的局限性，从而构建更强大的分类器或回归器。课程将详细介绍随机森林和AdaBoost算法，并探讨统计学习中的偏差-方差权衡重要主题。我们还将学习自助法和集成技术，作为同时减少偏差和方差的方法。 课程注重实验和实操，让学员在真实数据集上应用这些算法，亲自体验它们的强大。同时，我们将探讨随机森林、AdaBoost与深度学习神经网络之间的相似性。所有课程材料均为免费，您可以在Windows、Linux或Mac上轻松下载和安装Python、Numpy和Scipy。课程强调“如何构建与理解”，而非仅仅“如何使用”。通过实验，您将深入理解机器学习模型的内部运作。 课程预备知识包括微积分（导数）、概率、面向对象编程、Python编程（如条件语句、循环、列表、字典和集合）、Numpy编程（矩阵和向量运算）以及简单的机器学习模型（如线性回归和决策树）。 独特特点: - 每行代码都有详细解释，学员可以随时发邮件讨论。 - 无需浪费时间“打字”，而是注重学习的内容。 - 不惧怕大学水平的数学，提供算法的重要细节，而其他课程可能会遗漏。 如果您想深入了解机器学习模型，并进行实现，欢迎参加本课程。
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.We've already learned some classic machine learning models like k-nearest neighbor and decision tree. We've studied their limitations and drawbacks.But what if we could combine these models to eliminate those limitations and produce a much more powerful classifier or regressor?In this course you'll study ways to combine models like decision trees and logistic regression to build models that can reach much higher accuracies than the base models they are made of.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.We'll do plenty of experiments and use these algorithms on real datasets so you can see first-hand how powerful they are.Since deep learning is so popular these days, we will study some interesting commonalities between random forests, AdaBoost, and deep learning neural networks.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."If you can't implement it, you don't understand it"Or as the great physicist Richard Feynman said: "What I cannot create, I do not understand".My courses are the ONLY courses where you will learn how to implement machine learning algorithms from scratchOther courses will teach you how to plug in your data into a library, but do you really need help with 3 lines of code?After doing the same thing with 10 datasets, you realize you didn't learn 10 things. You learned 1 thing, and just repeated the same 3 lines of code 10 times...Suggested Prerequisites:Calculus (derivatives)ProbabilityObject-oriented programmingPython coding: if/else, loops, lists, dicts, setsNumpy coding: matrix and vector operationsSimple machine learning models like linear regression and decision treesWHAT ORDER SHOULD I TAKE YOUR COURSES IN?:Check out the lecture "Machine Learning and AI Prerequisite Roadmap" (available in the FAQ of any of my courses, including the free Numpy course)UNIQUE FEATURESEvery line of code explained in detail - email me any time if you disagreeNo wasted time "typing" on the keyboard like other courses - let's be honest, nobody can really write code worth learning about in just 20 minutes from scratchNot afraid of university-level math - get important details about algorithms that other courses leave out
