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所在平台: Udemy |
课程主页: https://www.udemy.com/course/data-science-logistic-regression-in-python/
课程评论：没有评论
课程名称：深度学习基础：Python中的逻辑回归 课程概述：您是否曾想过像OpenAI的ChatGPT、GPT-4、DALL-E、Midjourney和Stable Diffusion等人工智能技术是如何运作的？在本课程中，您将学习这些开创性应用的基础知识。课程作为深度学习和神经网络的引导，重点讲解机器学习、数据科学和统计中的一种流行和基础技术：逻辑回归。我们将从基础理论开始，推导解决方案，并应用于实际问题。同时，我们还会展示如何在Python中编写自己的逻辑回归模块。 本课程不需要任何外部材料。所有所需的工具（Python及一些Python库）均可免费获得。课程中提供了很多实际案例，帮助您真正理解深度学习可以应用于各种场景。整个课程将包括一个项目，演示如何利用用户数据预测网站用户行为，例如用户是否使用移动设备、浏览了多少产品、在网站上停留的时间、是否为回访者及访问时间等。 课程结束时还有一个项目，展示如何利用深度学习进行面部表情识别。想象一下，仅凭一张照片就能预测某人的情感！ 如果您是一位程序员，希望通过学习数据科学提升编程能力，这门课程将非常适合您。如果您有技术或数学背景，想要利用这些技能做出数据驱动的决策并通过科学原理优化业务，此课程同样适合您。 本课程的重点在于“如何构建和理解”，而不仅仅是“如何使用”。任何人在阅读一些文档后都可以在15分钟内使用API。学习的重点在于通过实验“亲自看见”而非“记住事实”。课程将教您如何可视化模型内部发生的过程。对于希望深入理解机器学习模型的学习者而言，这门课程是理想之选。 如同伟大的物理学家理查德·费曼所言：“我无法创造的东西，我就不理解。”我的课程是您学习机器学习算法从零开始实现的唯一课程。其他课程可能教您如何将数据插入库中，但您真的需要在3行代码上花时间吗？完成10个数据集的同样操作后，您会发现自己并没有学到10件事，而只是重复了同样的3行代码。 建议先修课程： - 微积分（导数） - 矩阵运算 - 概率 - Python编码（if/else、循环、列表、字典、集合） - Numpy编码（矩阵和向量运算、加载CSV文件） 建议学习顺序：请参考“机器学习和人工智能先修路线图”讲座（在任何一门课程的常见问题解答中都有，包括免费的Numpy课程）。
Ever wondered how AI technologies like OpenAI ChatGPT, GPT-4, DALL-E, Midjourney, and Stable Diffusion really work? In this course, you will learn the foundations of these groundbreaking applications.This course is a lead-in to deep learning and neural networks - it covers a popular and fundamental technique used in machine learning, data science and statistics: logistic regression. We cover the theory from the ground up: derivation of the solution, and applications to real-world problems. We show you how one might code their own logistic regression module in Python.This course does not require any external materials. Everything needed (Python, and some Python libraries) can be obtained for free.This course provides you with many practical examples so that you can really see how deep learning can be used on anything. Throughout the course, we'll do a course project, which will show you how to predict user actions on a website given user data like whether or not that user is on a mobile device, the number of products they viewed, how long they stayed on your site, whether or not they are a returning visitor, and what time of day they visited.Another project at the end of the course shows you how you can use deep learning for facial expression recognition. Imagine being able to predict someone's emotions just based on a picture!If you are a programmer and you want to enhance your coding abilities by learning about data science, then this course is for you. If you have a technical or mathematical background, and you want use your skills to make data-driven decisions and optimize your business using scientific principles, then this course is for you.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 (taking derivatives)matrix arithmeticprobabilityPython coding: if/else, loops, lists, dicts, setsNumpy coding: matrix and vector operations, loading a CSV fileWHAT 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)
