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所在平台: Udemy |
课程主页: https://www.udemy.com/course/a-foundation-for-machine-learning-and-data-science/
课程评论：没有评论
Coursera 课程 《机器学习与数据科学基础》 课程总结： 本课程由一位拥有超过 20 年 IT 行业经验（包括 15 年项目/计划管理经验）的行业专家授课，并结合其在机器学习与数据科学领域十余年的独立学习和研究经验。课程旨在为学习者打下坚实的理论基础和必备的实践技能，以应对机器学习模型的学习与应用。 课程强调，构建高性能机器学习模型并非仅仅在于熟悉算法数量，更在于对已有知识的熟练运用。为确保学习者能够无歧义地理解概念，课程大量运用了生动的可视化和动画效果。 课程内容结构包含九个部分： 1. 机器学习导论 2. Anaconda 概述与安装 3. JupyterLab 概述 4. Python 概述 5. 线性代数概述 6. 统计学概述 7. 概率论概述 8. 面向对象编程 (OOPs) 概述 9. 重要库概述 课程共包含 20 个讲座、10 次实操环节以及 10 个可下载的资产。讲师坚信，完成本课程将显著提升学员在求职面试中的表现，使其远超未参与课程的同侪。
This course is designed by an industry expert who has over 2 decades of IT industry experience including 1.5 decades of project/ program management experience, and over a decade of experience in independent study and research in the fields of Machine Learning and Data Science.The course will equip students with a solid understanding of the theory and practical skills necessary to learn machine learning models and data science.When building a high-performing ML model, it's not just about how many algorithms you know; instead, it's about how well you use what you already know.Throughout the course, I have used appealing visualization and animations to explain the concepts so that you understand them without any ambiguity.This course contains 9 sections: 1. Introduction to Machine Learning 2. Anaconda - An Overview & Installation 3. JupyterLab - An Overview 4. Python - An Overview 5. Linear Algebra - An Overview 6. Statistics - An Overview 7. Probability - An Overview 8. OOPs - An Overview 9. Important Libraries - An OverviewThis course includes 20 lectures, 10 hands-on sessions, and 10 downloadable assets.By the end of this course, I am confident that you will outperform in your job interviews much better than those who have not taken this course, for sure.
