Machine Learning for Everyone with Eric Siegel 专项课程
An End-to-End Guide to Leading and Launching ML. This expansive machine learning curriculum is accessible to business-level learners and yet vital to techies as well. It covers both the state-of-the-art techniques and the business-side best practices.
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您将学到的内容有
Lead ML: Manage or participate in the end-to-end implementation of machine learning
Apply ML: Identify the opportunities where machine learning can improve marketing, sales, financial credit scoring, insurance, fraud detection, and much more
Greenlight ML: Forecast the effectiveness of and scope the requirements for a machine learning project and then internally sell it to gain buy-in
Regulate ML: Manage ethical pitfalls, the risks to social justice that stem from machine learning – aka AI ethics
您将获得的技能
关于此 专项课程
应用的学习项目
For one exercise, create a predictive model by hand in Excel or Google Sheets and visualize how it improves before your eyes. Beyond this, there are no exercises involving the use of ML software.
Vendor-Neutral
This specialization includes several illuminating software demos of ML in action using SAS products. However, the curriculum is vendor-neutral and universally-applicable. The learnings apply, regardless of which ML software you end up choosing to work with.
In-Depth Yet Accessible
Brought to you by a veteran industry leader who won teaching awards when he was a professor at Columbia University, this specialization stands out as one of the most thorough, engaging, and surprisingly accessible on the subject of ML.
Like a University Course
These three courses are also a good fit for college students, or for those planning for or currently enrolled in an MBA program. The breadth and depth of this specialization is equivalent to one full-semester MBA or graduate-level course.
Accessible to business-side learners yet also vital to techies. Engage in the commercial use of ML – whether you're an enterprise leader or a quant.
Accessible to business-side learners yet also vital to techies. Engage in the commercial use of ML – whether you're an enterprise leader or a quant.
此专项课程包含 3 门课程
The Power of Machine Learning: Boost Business, Accumulate Clicks, Fight Fraud, and Deny Deadbeats
It's the age of machine learning. Companies are seizing upon the power of this technology to combat risk, boost sales, cut costs, block fraud, streamline manufacturing, conquer spam, toughen crime fighting, and win elections.
Launching Machine Learning: Delivering Operational Success with Gold Standard ML Leadership
Machine learning runs the world. It generates predictions for each individual customer, employee, voter, and suspect, and these predictions drive millions of business decisions more effectively, determining whom to call, mail, approve, test, diagnose, warn, investigate, incarcerate, set up on a date, or medicate.
Machine Learning Under the Hood: The Technical Tips, Tricks, and Pitfalls
Machine learning. Your team needs it, your boss demands it, and your career loves it. After all, LinkedIn places it as one of the top few "Skills Companies Need Most" and as the very top emerging job in the U.S.
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SAS
Through innovative software and services, SAS empowers and inspires customers around the world to transform data into intelligence. SAS is a trusted analytics powerhouse for organizations seeking immediate value from their data. A deep bench of analytics solutions and broad industry knowledge keep our customers coming back and feeling confident. With SAS®, you can discover insights from your data and make sense of it all. Identify what’s working and fix what isn’t. Make more intelligent decisions. And drive relevant change.
常见问题
What is the refund policy?
我可以只注册一门课程吗?
Is financial aid available?
我可以免费学习课程吗?
此课程是 100% 在线学习吗?是否需要现场参加课程?
完成专项课程后我会获得大学学分吗?
完成专项课程后我会获得大学学分吗?
How technical is this specialization and how much math is involved?
Are the learnings specific to SAS software?
Is this specialization for industry professionals or for university students?
Do I need to take the courses in a specific order?
AI ethics: Is equitable machine learning possible or will predictive models always perpetuate social injustice?
还有其他问题吗?请访问 学生帮助中心。