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所在平台: Udemy |
课程主页: https://www.udemy.com/course/python-for-data-science-machine-learning-zero-to-hero/
课程评论：没有评论
课程名称：从零到英雄的Python数据科学与机器学习 概述：本课程将介绍机器学习的基本原理，揭示像谷歌、亚马逊和Udemy等公司如何利用机器学习和人工智能（AI）从大量数据集中提取有意义的见解。根据Glassdoor和Indeed的数据显示，数据科学家的平均年薪为120,000美元，显示了这一职业的高需求。 随着数据科学家在现代职场中的需求不断增长，他们的稀缺性和竞争力也随之上升。如今的数据科学家在科学培训、计算机技能和分析能力的结合上非常罕见，已与20世纪80年代和90年代华尔街的“量化分析师”有相似之处。当对于创新算法和数据方法的需求增加时，物理学家和数学家纷纷转向投资银行和对冲基金，这使得数据科学逐渐成为一个备受推崇的职业路径。 本课程的目的就是为满足这一需求，帮助学生获取成为数据科学家的必要教育和经验。课程内容用简单易懂的语言描述，尽量避免复杂的数学符号和术语。学员将获取源代码，进行实验和改进，重点在于将这些算法应用于现实世界中，而非仅限于理论与学术环境。 每个视频都将为学员带来新的视角，鼓励学员立即付诸实践。无论你的统计学背景如何，本课程对所有水平的学生持开放态度，欢迎大家报名参与。
This machine learning course will provide you the fundamentals of how companies like Google, Amazon, and even Udemy utilize machine learning and artificial intelligence (AI) to glean meaning and insights from massive data sets. Glassdoor and Indeed both report that the average salary for a data scientist is $120,000. This is the standard, not the exception.Data scientists are already quite desirable. It's difficult to keep them on staff in today's tight labor market. There is a severe shortage of people who possess the rare combination of scientific training, computer expertise, and analytical talents.Today's data scientists are held to the same standards as the Wall Street "quants" of the '80s and '90s. When the need arose for innovative algorithms and data approaches, physicists and mathematicians flocked to investment banks and hedge funds.So, it's no surprise that data science is rising to prominence as a promising career path in the modern day. It is analytic in focus, driven by code, and performed on a computer. As a result, it shouldn't be a shock that the demand for data scientists has been growing steadily in the workplace for the past few years.On the other hand, availability has been low. Obtaining the education and experience necessary to be hired as a data scientist is tough. And that's why we made this course in the first place!Each topic is described in plain English, and the course does its best to avoid mathematical notations and jargon. Once you have access to the source code, you can experiment with it and improve upon it. Learning and applying these algorithms in the real world, rather than in a theoretical or academic setting, is the focus of this course.Each video will leave you with a new perspective that you can implement right away!If you have no background in statistics, don't let that stop you from enrolling in this course; we welcome students of all levels.
