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所在平台: Udemy |
课程主页: https://www.udemy.com/course/statistics-probability-for-data-science-machine-learning/
课程评论：没有评论
**课程名称：** 统计学与概率学在数据科学与机器学习中的应用 **课程概述：** 本课程旨在从数据科学和机器学习的角度，深入讲解统计学和概率学的核心概念。我们将详细涵盖描述性统计和推断性统计的每一项内容，以及概率论的各个方面。课程提供大量实例，确保您对概念的理解清晰透彻，并能将其应用于日常工作中。 **详细课程内容：** * **描述性统计：** * 集中趋势度量（均值、中位数、众数） * 离散程度度量（极差、四分位距、方差、标准差、平均绝对偏差） * **回归分析：** * 线性回归与高级回归技术 * 假设检验（P值） * **相关性分析：** * 协方差矩阵 * Karl Pearson 相关系数 * Spearman 秩相关系数（附带实例） * **概率论：** * 概率基础知识 * 排列与组合 * 组合数学与概率 * 随机变量的概念 * 常见分布：二项分布、伯努利分布、几何分布、泊松分布 * **推断性统计：** * 抽样分布与中心极限定理 * 置信区间 * 误差范围 * t统计量和F统计量 * 假设检验详解（附带大量实例） * 第一类错误与第二类错误 * 卡方检验（Chi-Square Test） * 方差分析（ANOVA）与F统计量 * **分布的深入理解：** * 正态分布及其性质 * 对称分布、偏度、峰度 * 核密度估计（KDE） **课程目标：** 完成本课程后，您将熟练掌握统计学和概率学的相关知识，有信心与他人讨论统计学话题，并将所学知识有效地应用于日常工作中。 *(请注意：本课程无详细教学大纲。)*
This course is designed to get an in-depth knowledge of Statistics and Probability for Data Science and Machine Learning point of view. Here we are talking about each and every concept of Descriptive and Inferential statistics and Probability. We are covering the following topics in detail with many examples so that the concepts will be crystal clear and you can apply them in the day to day work. Extensive coverage of statistics in detail: The measure of Central Tendency (Mean Median and Mode) The Measure of Spread (Range, IQR, Variance, Standard Deviation and Mean Absolute deviation) Regression and Advanced regression in details with Hypothesis understanding (P-value) Covariance Matrix, Karl Pearson Correlation Coefficient, and Spearman Rank Correlation Coefficient with examplesDetailed understanding of Normal Distribution and its propertiesSymmetric Distribution, Skewness, Kurtosis, and KDE. Probability and its in-depth knowledge Permutations and Combinations Combinatorics and Probability Understanding of Random Variables Various distributions like Binomial, Bernoulli, Geometric, and Poisson Sampling distributions and Central Limit Theorem Confidence IntervalMargin of ErrorT-statistic and F-statisticSignificance tests in detail with various examples Type 1 and Type 2 ErrorsChi-Square Test ANOVA and F-statisticBy completing this course we are sure you will be very much proficient in Statistics and able to talk to anyone about stats with confidence apply the knowledge in your day to day work.
