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所在平台: Udemy |
课程主页: https://www.udemy.com/course/master-special-probability-distributions-in-statistics/
课程评论：没有评论
**Coursera 课程总结：计算机科学完整统计学大师 - II** 本课程是计算机科学领域统计学学习的进阶篇，旨在帮助您深入理解并运用统计学知识，尤其关注在机器学习、神经网络和数据科学等领域的实际应用。 **核心内容概述：** 课程前半部分专注于**特殊概率分布**的学习。您将系统地学习并掌握多种在实践中被广泛应用的概率分布，包括： * **二项分布 (Binomial Distribution)** * **泊松分布 (Poisson Distribution)** * **几何分布 (Geometric Distribution)** * **超几何分布 (Hypergeometric Distribution)** * **均匀分布/矩形分布 (Uniform or Rectangular Distribution)** * **指数分布/负指数分布 (Exponential or Negative Exponential Distribution)** * **爱尔兰分布/一般伽马分布 (Erlang or General Gamma Distribution)** * **威布尔分布 (Weibull Distribution)** * **正态分布/高斯分布 (Normal or Gaussian Distribution)** * **中心极限定理 (Central Limit Theorem)** 这些分布不仅涵盖了概率论的基础，更直接关联到各种随机实验和现实世界现象的模型，是理解复杂数据和构建预测模型的基石。 课程后半部分则转向**推断统计 (Inferential Statistics)**，也称为**统计推断**。这部分内容将教您如何从样本数据中推断出关于总体的信息，即： * **假设检验 (Hypotheses Testing)** * **大样本检验 (Large Sample Test) - 大样本显著性检验** * **小样本检验 (Small Sample Test) - 小样本显著性检验** * **卡方检验 - 拟合优度检验 (Chi - Square Test - Test of Goodness of Fit)** 通过这些方法，您将能够对样本结果进行归纳和推广，从而做出更科学的决策和结论。 **课程亮点：** * **超过 150 讲视频讲解**，从基础概念到高级应用，层层递进。 * **超过 85 个包含详细解答的示例**，通过实践加深理解，检验学习效果。 * **结构清晰的课程章节**，系统性地涵盖了所有关键统计概念。 本课程是致力于提升统计分析能力，为在数据科学和人工智能领域取得成功的学习者的必备课程.
As it turns out, there are some specific distributions that are used over and over in practice for e.g. Normal Distribution, Binomial Distribution, Poisson Distribution, Exponential Distribution etc.There is a random experiment behind each of these distributions. Since these random experiments model a lot of real life phenomenon, these special distribution are used in different applications like Machine Learning, Neural Network, Data Science etc. That is why they have been given a special names and we devote a course "Master Complete Statistics For Computer Science - II" to study them. After learning about special probability distribution, the second half of this course is devoted for data analysis through inferential statistics which is also referred to as statistical inference.Technically speaking, the methods of statistical inference help in generalizing the results of a sample to the entire population from which the sample is drawn.This 150+ lecture course includes video explanations of everything from Special Probability Distributions and Sampling Distribution, and it includes more than 85+ examples (with detailed solutions) to help you test your understanding along the way. "Master Complete Statistics For Computer Science - II" is organized into the following sections:IntroductionBinomial DistributionPoisson DistributionGeometric DistributionHypergeometric DistributionUniform or Rectangular DistributionExponential or Negative Exponential DistributionErlang or General Gamma DistributionWeibull DistributionNormal or Gaussian Distribution Central Limit TheoremHypotheses TestingLarge Sample Test - Tests of Significance for Large SamplesSmall Sample Test - Tests of Significance for Small SamplesChi - Square Test - Test of Goodness of Fit
