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所在平台: Udemy |
课程主页: https://www.udemy.com/course/probability-data-science-machine-learning/
课程评论：没有评论
课程名称：数学 0-1：数据科学与机器学习的概率 课程概述：在探索机器学习和数据科学的过程中，许多人会因为数学知识不足而感到困惑。无论你是从未学习过相关数学，还是已经遗忘多年的知识，这门课程旨在帮助你克服这些困难。概率是数据科学和机器学习的重要数学基础，是理解现代算法（如ChatGPT、Stable Diffusion和Midjourney等）所必需的。 课程内容将涵盖概率的基本概念，包括随机变量、离散和连续概率分布、随机变量的函数、多元分布、期望值、生成函数、大数法则和中心极限定理等主要理论，并从头开始推导重要定理。通过这门课程，你将会建立起扎实的概率基础，避免未来在应用时出现错误。 此外，这门课程还将介绍与机器学习相关的几种特定模型，如线性回归、K均值聚类、主成分分析和神经网络，所有这些模型都与概率密切相关。课程目的是让你具备足够的概率知识，以便在数据科学和机器学习领域中有效应用。 建议的先备知识包括：微分积分、向量微积分、线性代数，以及对大学/学院水平数学的基本理解。准备好迎接挑战了吗？让我们开始学习吧！
Common scenario: You try to get into machine learning and data science, but there's SO MUCH MATH.Either you never studied this math, or you studied it so long ago you've forgotten it all.What do you do?Well my friends, that is why I created this course.Probability is one of the most important math prerequisites for data science and machine learning. It's required to understand essentially everything we do, from the latest LLMs like ChatGPT, to diffusion models like Stable Diffusion and Midjourney, to statistics (what I like to call "probability part 2").Markov chains, an important concept in probability, form the basis of popular models like the Hidden Markov Model (with applications in speech recognition, DNA analysis, and stock trading) and the Markov Decision Process or MDP (the basis for Reinforcement Learning).Machine learning (statistical learning) itself has a probabilistic foundation. Specific models, like Linear Regression, K-Means Clustering, Principal Components Analysis, and Neural Networks, all make use of probability.In short, probability cannot be avoided!If you want to do machine learning beyond just copying library code from blogs and tutorials, you must know probability.This course will cover everything that you'd learn (and maybe a bit more) in an undergraduate-level probability class. This includes random variables and random vectors, discrete and continuous probability distributions, functions of random variables, multivariate distributions, expectation, generating functions, the law of large numbers, and the central limit theorem.Most important theorems will be derived from scratch. Don't worry, as long as you meet the prerequisites, they won't be difficult to understand. This will ensure you have the strongest foundation possible in this subject. No more memorizing "rules" only to apply them incorrectly / inappropriately in the future! This course will provide you with a deep understanding of probability so that you can apply it correctly and effectively in data science, machine learning, and beyond.Are you ready?Let's go!Suggested prerequisites:Differential calculus, integral calculus, and vector calculusLinear algebraGeneral comfort with university/collegelevel mathematics
