Probability & Statistics for Machine Learning & Data Science

所在平台: Coursera

课程主页: https://www.coursera.org/learn/machine-learning-probability-and-statistics

课程评论：没有评论

第一个写评论        关注课程

课程简介

课程名称：机器学习与数据科学的概率与统计 课程概述：本课程在2024年进行了新更新！这是一个由DeepLearning.AI创建的基础在线课程，由Luis Serrano讲授，专注于机器学习和数据科学中的数学应用。在这个专项课程中，您将通过编程实践所学的数学概念，因此需要具备基础到中级的Python编程技能。 完成本课程后，您将能够： 课程大纲： - 第1周：概率与概率分布简介 您将学习事件的概率及各种数学概率规则，以便准确进行概率运算。内容包括条件概率的概念以及贝叶斯定理的基本思想。此外，您还将了解随机变量的概率分布以及常见的概率分布类型，如二项分布和正态分布。 - 第2周：描述概率分布和多变量概率分布 本周将学习描述概率分布和数据集的不同方法，包括集中趋势的度量（均值、中位数和众数）、方差、偏度和峰度。您将介绍随机变量的期望值，并学习一些可视化工具以描述数据和分布。同时，您将了解两个或多个随机变量的概率分布，包括联合分布、边际分布和条件分布，并学习协方差的概念。 - 第3周：抽样与点估计 本周将重点由概率转向统计学。您将学习样本与总体的概念，以及与样本和总体相关的两个基本统计结果：大数法则和中心极限定理。此外，还将介绍点估计方法，特别是最常用的最大似然估计，并了解正则化如何防止过拟合。您也将学习贝叶斯统计如何将先验信念融入数据评估和推断中。 - 第4周：置信区间与假设检验 本周将学习一种新的估计方法，即区间估计，尤其是如何计算和正确解释置信区间。您将学习假设检验，了解如何在现有证据或样本数据的基础上形成假设并进行测试。您将掌握p值的概念及其在假设检验中的决策作用，并了解一些常见测试，如t检验、双样本t检验和配对t检验，最后将学习假设检验在数据科学中的应用案例：A/B测试。

课程大纲

Name:Week 1 - Introduction to Probability and Probability Distributions

Description:In this week, you will learn about probability of events and various rules of probability to correctly do arithmetic with probabilities. You will learn the concept of conditional probability and the key idea behind Bayes theorem. In lesson 2, we generalize the concept of probability of events to probability distribution over random variables. You will learn about some common probability distributions like the Binomial distribution and the Normal distribution.

Name:Week 2 - Describing probability distributions and probability distributions with multiple variables

Description:This week you will learn about different measures to describe probability distributions as well as any dataset. These include measures of central tendency (mean, median, and mode), variance, skewness, and kurtosis. The concept of the expected value of a random variable is introduced to help you understand each of these measures. You will also learn about some visual tools to describe data and distributions. In lesson 2, you will learn about the probability distribution of two or more random variables using concepts like joint distribution, marginal distribution, and conditional distribution. You will end the week by learning about covariance: a generalization of variance to two or more random variables.

Name:Week 3 - Sampling and Point estimation

Description:This week shifts its focus from probability to statistics. You will start by learning the concept of a sample and a population and two fundamental results from statistics that concern samples and population: the law of large numbers and the central limit theorem. In lesson 2, you will learn the first and the simplest method of estimation in statistics: point estimation. You will see how maximum likelihood estimation, the most common point estimation method, works and how regularization helps prevent overfitting. You'll then learn how Bayesian Statistics incorporates the concept of prior beliefs into the way data is evaluated and conclusions are reached.

Name:Week 4 - Confidence Intervals and Hypothesis testing

Description:This week you will learn another estimation method called interval estimation. The most common interval estimates are confidence intervals and you will see how they are calculated and how to correctly interpret them. In lesson 2, you will learn about hypothesis testing where estimates are formulated as a hypothesis and then tested in the presence of available evidence or a sample of data. You will learn the concept of p-value that helps in making a decision about a hypothesis test and also learn some common tests like the t-test, two-sample t-test, and the paired t-test. You will end the week with an interesting application of hypothesis testing in data science: A/B testing.

课程评论(0条)