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所在平台: Udemy |
课程主页: https://www.udemy.com/course/applied-math-for-ml/
课程评论：没有评论
课程名称：机器学习的统计学与线性代数 课程概述：这门课程受到许多学生的高度评价，学员们表示课程有效解答了他们的疑惑，并帮助他们理解统计学与数据科学、机器学习之间的关系。学员Rubayet A.提到，尽管之前学习过统计学，但仍难以理解其与机器学习的联系，而这门课程使他豁然开朗。Dipesh S.称赞课程清晰且精准，非常适合初学者。他指出，课程中的基础概念描述得非常到位。Héctor Marañón R.表示，课程内容明确且富有教育意义，尤其是通过生动的类比加深理解。Clark D.和Oscar M.也对课程中的数学概念和科学研究背景表示赞赏，称其对初学者尤为重要。 背景介绍：本课程由一位人工智能专家主讲，他观察到很多学生和年轻专业人士在学习机器学习时常常忽略数学与统计学的核心概念。考虑到机器学习是统计学、概率论、计算机科学和数学等多个学科交叉的领域，因此学习者有必要深入理解这些核心概念。课程将帮助学员建立坚实的基础，为在人工智能领域的职业发展奠定良好基础。 课程内容：本课程将从应用的角度教授数学和统计学概念。了解这些概念不仅仅是理论上的学习，更重要的是理解它们的实际应用。课程强调了如何确保在机器学习的部署和利用中不会遇到挑战。学员将学习的主要内容包括： - 集中趋势与离散程度的度量 - 均值与标准差 - 百分位数 - 数据类型 - 依赖与独立变量 - 概率 - 抽样与总体 - 假设检验 - 稳定性的概念 - 分布类型 - 异常值 - 机器学习算法的数学基础（如回归、决策树和k最近邻） - 梯度下降 - 数组、向量、点积、大小 - 特征向量与特征值 - 余弦相似度 整个课程为期较短，面向初学者，旨在帮助学员打下扎实的数学和统计学基础，以便在机器学习领域获得成功。
Testimonials about the course"Great course. It cleared all my doubts. I learned statistics previously from HK Dass sir's book, but I couldn't understand there relationship in data science and machine learning. Loved this course!" Rubayet A."Simply amazing course where every basics are described clearly and precisely. Go for this course." Dipesh S "Es claro, preciso en los datos. Las ilustraciones son muy pedagógicas, sobre todo las analogías.". Héctor Marañón R."Good for beginners like me to learn the concepts of Machine Learning and the math behind of it. Great to review this course again. Thanks." Clark D"Excelentes conceptos, enfocados hacia las investigaciín de base científica" Oscar MBackground and IntroductionThe trainer of this course is an AI expert and he has observed that many students and young professionals make the mistake of learning machine learning without understanding the core concepts in maths and statistics. This course will help to address that gap in a big way.Since Machine Learning is a field at the intersection of multiple disciplines like statistics, probability, computer science, and mathematics, its essential for practitioners and budding enthusiasts to assimilate these core concepts.These concepts will help you to lay a strong foundation to build a thriving career in artificial intelligence.This course teaches you the concepts mathematics and statistics but from an application perspective. It's one thing to know about the concepts but it is another matter to understand the application of those concepts. Without this understanding, deploying and utilizing machine learning will always remain challenging.You will learn concepts like measures of central tendency vs dispersion, hypothesis testing, population vs sample, outliers and many interesting concepts. You will also gain insights into gradient decent and mathematics behind many algorithms.We cover the below concepts in this course:Measures of Central Tendency vs DispersionMean vs Standard DeviationPercentilesTypes of DataDependent vs independent variablesProbabilitySample Vs populationHypothesis testingConcept of stabilityTypes of distributionOutliersMaths behind machine learning algorithms like regression, decision tree and kNNGradient descent.ArraysVectorsDot productMagnitudeEigen vector, eigen valueCosine similarity.
