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所在平台: Udemy |
课程主页: https://www.udemy.com/course/linear-algebra-for-data-science-machine-learning/
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课程名称：数据科学与机器学习的线性代数 - 2020 课程概述： 本课程旨在教授线性代数的基础知识，帮助学员理解其在数据科学和机器学习中的重要性。学习线性代数的原因包括：掌握向量和矩阵的运算可以提高对机器学习方法的直观理解，深入理解算法及其约束有助于个性化应用并更好地了解调参对结果的影响。 课程内容包括但不限于： - 集合和线性方程组 - 标量和向量的算术运算 - 向量的加法与减法 - 标量乘法及点积和叉积 - 向量子空间和线性组合 - 线性方程组的解法 - 矩阵运算（包括乘法、范畴和特征向量/特征值） 课程还提供课堂支持，确保学员在学习过程中能够及时获得问题解答。DATAI TEAM是一支专门的Python程序员和数据科学家团队，为学员提供支持与服务。 欢迎注册本课程，开启数据科学与机器学习的线性代数学习之旅！
LINEAR ALGEBRA for DATA SCIENCE & MACHINE LEARNING COURSE DESCRIPTIONWhy Learn Linear Algebra?SetsLinear Equation SystemsWhat is a Scalar?Scalar & Vector ArithmeticVector Addition and SubtractionScalar Multiplication of VectorsDot & Cross ProductDot Product Linear Algebra StyleVector SubspaceLinear Combinations of VectorsSpanLinear Dependence and IndependenceSolving Systems of linear equationsLinear Equation ExampleGenerating Set and BasisLinear Mapping/Linear TransformationAdditivityHomogeneityKernelMatrices - TensorsMatrix MultiplicationRange of a MatrixKernel of a MatrixDeterminant of a MatrixIdentity , Transpose and Inverse MatrixEigenvector and EigenvalueWHY LINEAR ALGEBRA?Linear algebra is absolutely key to understanding the calculus and statistics you need in machine learning and data science.If you can understand machine learning methods at the level of vectors and matrices you will improve your intuition for how and when they work.A deeper understanding of the algorithm and its constraints will allow you to customize its application and better understand the impact of tuning parameters on the results.THE OPPORTUNITIES YOU WILL HAVE WITH THIS COURSEIn-class support: We don't just give you video lessons. We have created a professional Python Programmer team and community to support you. This means that you will get answers to your questions within 24 hours.WHO WE ARE: DATAI TEAM ACADEMYDATAI TEAM is a team of Python Programmers and Data Scientists.Let's register for the course and start to Linear Algebra for Data Science & Machine Learning.
