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所在平台: Udemy |
课程主页: https://www.udemy.com/course/machine-learning-data-science-foundations-masterclass/
课程评论：没有评论
Coursera 机器学习数学基础课程摘要 本课程深入探讨机器学习和数据科学的核心数学原理，重点是线性代数和微积分。 **核心内容：** * **线性代数：** 涵盖数据结构、张量运算、矩阵特性、特征向量与特征值，以及用于机器学习的矩阵运算。 * **微积分：** 讲解极限、导数与微分、自动微分和偏导数计算，以及积分。 **课程特色：** * **实践导向：** 包含大量动手练习、Python 代码演示和实际应用，帮助学员扎实掌握数学知识。 * **理论与实践结合：** 强调理解算法背后的数学原理，帮助学员解决建模问题并创造新方案。 * **讲师权威：** 由深度学习专家 Dr. Jon Krohn 教授。 * **未来内容扩展：** 未来将增加概率、统计、数据结构、算法和优化等相关主题内容，现有学员可免费获得所有未来更新。 **目标受众：** 希望提升数据科学和机器学习技能，深入理解算法原理，并渴望成为杰出数据科学家的学习者。
Mathematics forms the core of data science and machine learning. Thus, to be the best data scientist you can be, you must have a working understanding of the most relevant math.Getting started in data science is easy thanks to high-level libraries like Scikit-learn and Keras. But understanding the math behind the algorithms in these libraries opens an infinite number of possibilities up to you. From identifying modeling issues to inventing new and more powerful solutions, understanding the math behind it all can dramatically increase the impact you can make over the course of your career.Led by deep learning guru Dr. Jon Krohn, this course provides a firm grasp of the mathematics - namely linear algebra and calculus - that underlies machine learning algorithms and data science models.Course SectionsLinear Algebra Data StructuresTensor OperationsMatrix PropertiesEigenvectors and EigenvaluesMatrix Operations for Machine LearningLimitsDerivatives and DifferentiationAutomatic DifferentiationPartial-Derivative CalculusIntegral CalculusThroughout each of the sections, you'll find plenty of hands-on assignments, Python code demos, and practical exercises to get your math game in top form!This Mathematical Foundations of Machine Learning course is complete, but in the future, we intend on adding extra content from related subjects beyond math, namely: probability, statistics, data structures, algorithms, and optimization. Enrollment now includes free, unlimited access to all of this future course content - over 25 hours in total. Are you ready to become an outstanding data scientist? See you in the classroom.
