|
所在平台: Udemy |
课程主页: https://www.udemy.com/course/python-for-machine-learning-beginners/
课程评论：没有评论
课程名称：Python机器学习：全面初学者课程 概述：本机器学习课程旨在帮助您理解Google、Amazon和Udemy等组织如何利用机器学习和人工智能（AI）从庞大的数据集中提取有意义的见解。根据Glassdoor和Indeed的数据，数据科学家的平均收入为$120,000，这只是平均水平！在竞争激烈的就业市场中，拥有独特科学训练、计算机技能和分析能力的人才稀缺，因此数据科学家在职场上的吸引力非常强。 如今的数据科学家被期望具备类似1980和1990年代华尔街“量化分析师”的技能组合。那时，物理和数学背景的人才涌向投资银行和对冲基金，因为他们能够提出创新的算法和数据方法。因此，数据科学已成为21世纪最具成功潜力的职业之一，具有计算机化、编程驱动和分析的特性。 近年来，数据科学家的需求不断增长，而相关人才的供应则非常有限。要获得被聘为数据科学家所需的知识和技能并不容易。 本课程将尽量减少数学符号和术语的使用，以简单易懂的英语解释每个主题，使学习过程更为顺畅。在掌握代码后，学员将能够进行实践与扩展。课程重点是理解和应用这些算法于现实世界，而非理论或学术背景。每节视频结束后，学员都会获得一个可以立即应用的新想法！ 本课程欢迎所有技能水平的学员参与，即使没有任何统计学经验，也能顺利学习并取得成功！
To understand how organizations like Google, Amazon, and even Udemy use machine learning and artificial intelligence (AI) to extract meaning and insights from enormous data sets, this machine learning course will provide you with the essentials. According to Glassdoor and Indeed, data scientists earn an average income of $120,000, and that is just the norm! When it comes to being attractive, data scientists are already there. In a highly competitive job market, it is tough to keep them after they have been hired. People with a unique mix of scientific training, computer expertise, and analytical abilities are hard to find.Like the Wall Street "quants" of the 1980s and 1990s, modern-day data scientists are expected to have a similar skill set. People with a background in physics and mathematics flocked to investment banks and hedge funds in those days because they could come up with novel algorithms and data methods.That being said, data science is becoming one of the most well-suited occupations for success in the twenty-first century. It is computerized, programming-driven, and analytical in nature. Consequently, it comes as no surprise that the need for data scientists has been increasing in the employment market over the last several years.The supply, on the other hand, has been quite restricted. It is challenging to get the knowledge and abilities required to be recruited as a data scientist.In this course, mathematical notations and jargon are minimized, each topic is explained in simple English, making it easier to understand. Once you've gotten your hands on the code, you'll be able to play with it and build on it. The emphasis of this course is on understanding and using these algorithms in the real world, not in a theoretical or academic context. You'll walk away from each video with a fresh idea that you can put to use right away!All skill levels are welcome in this course, and even if you have no prior statistical experience, you will be able to succeed!
