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所在平台: Udemy |
课程主页: https://www.udemy.com/course/data-science-linear-regression-in-python/
课程评论：没有评论
课程名称：深度学习前置知识：Python中的线性回归 课程概述：你是否曾想过AI技术（例如OpenAI的ChatGPT、GPT-4、DALL-E、Midjourney和Stable Diffusion）是如何工作的？在本课程中，你将学习这些开创性应用的基础知识。课程主要讲解机器学习、数据科学和统计学中的一种常用技术：线性回归。我们将从基础理论入手，包括解的推导与实际应用，教你如何使用Python编写线性回归模块。 线性回归是你能学习的最简单的机器学习模型，然而其深度令人着迷，你会在今后的学习中不断回归。课程非常适合那些希望在深度学习、机器学习、数据科学和统计学领域迈出第一步的学习者。 课程内容包括：首先，通过一维线性回归证明摩尔定律的成立；紧接着，我们将一维线性回归扩展至任意维度，创建一个能够学习多输入的机器学习模型，应用于预测患者的收缩压；最后，我们将讨论在数据分析中需要注意的一些实际机器学习问题，如泛化、过拟合和训练-测试分割等。 本课程不需要任何外部材料。所有需要的工具（Python及一些Python库）都是免费的。无论你是想提升编程能力的程序员，还是希望将自己的技术或数学背景应用于软件工程或“黑客”领域的学习者，这门课程都将为你提供帮助。 本课程的重点在于“如何构建和理解”，而不仅仅是“如何使用”。你将通过实验来实际了解模型内部的运作，而不仅仅是记住事实。如果你希望深入了解机器学习模型，而不仅仅停留在表面，这门课程是你的理想选择。 建议前置知识：微积分（取导数）、矩阵运算、概率、Python编程（条件语句、循环、列表、字典、集合）、Numpy编程（矩阵和向量操作，加载CSV文件）。 课程学习顺序：请查看“机器学习和人工智能前置知识路线图”的讲座（在我任何课程的常见问题部分均可找到，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 teaches you about one popular technique used in machine learning, data science and statistics: linear 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 linear regression module in Python.Linear regression is the simplest machine learning model you can learn, yet there is so much depth that you'll be returning to it for years to come. That's why it's a great introductory course if you're interested in taking your first steps in the fields of:deep learningmachine learningdata sciencestatisticsIn the first section, I will show you how to use 1-D linear regression to prove that Moore's Law is true.What's that you say? Moore's Law is not linear?You are correct! I will show you how linear regression can still be applied.In the next section, we will extend 1-D linear regression to any-dimensional linear regression - in other words, how to create a machine learning model that can learn from multiple inputs.We will apply multi-dimensional linear regression to predicting a patient's systolic blood pressure given their age and weight.Finally, we will discuss some practical machine learning issues that you want to be mindful of when you perform data analysis, such as generalization, overfitting, train-test splits, and so on.This course does not require any external materials. Everything needed (Python, and some Python libraries) can be obtained for FREE.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 to know how to apply your skills as a software engineer or "hacker", this course may be useful.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)
