關於此 專項課程

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Machine learning reinvents industries and runs the world. Harvard Business Review calls it “the most important general-purpose technology of our era.” But while there are so many how-to courses for hands-on techies, there are practically none that also serve the business leadership of machine learning – a striking omission, since success with machine learning relies on a very particular project leadership practice just as much as it relies on adept number crunching. By filling that gap, this course empowers you to generate value with ML. It delivers the end-to-end expertise you need, covering both the core technology and the business-side practice. Why cover both sides? Because both sides need to learn both sides! This includes everyone leading or participating in the deployment of ML. NO HANDS-ON. Rather than a hands-on training, this specialization serves both business leaders and burgeoning data scientists with expansive, holistic coverage. BUT TECHNICAL LEARNERS SHOULD TAKE ANOTHER LOOK. Before jumping straight into the hands-on, as quants are inclined to do, consider one thing: This curriculum provides complementary know-how that all great techies also need to master. WHAT YOU'LL LEARN. How ML works, how to report on its ROI and predictive performance, best practices to lead an ML project, technical tips and tricks, how to avoid the major pitfalls, whether true AI is coming or is just a myth, and the risks to social justice that stem from ML.
可分享的證書
完成後獲得證書
100% 在線課程
立即開始,按照自己的計劃學習。
靈活的計劃
設置並保持靈活的截止日期。
初級
完成課程大約需要 3 個月
建議進度:4 小時/週
英語(English)
可分享的證書
完成後獲得證書
100% 在線課程
立即開始,按照自己的計劃學習。
靈活的計劃
設置並保持靈活的截止日期。
初級
完成課程大約需要 3 個月
建議進度:4 小時/週
英語(English)

專項課程的運作方式

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此專項課程包含 3 門課程

課程1

課程 1

The Power of Machine Learning: Boost Business, Accumulate Clicks, Fight Fraud, and Deny Deadbeats

4.8
117 個評分
50 條評論
課程2

課程 2

Launching Machine Learning: Delivering Operational Success with Gold Standard ML Leadership

4.9
63 個評分
22 條評論
課程3

課程 3

Machine Learning Under the Hood: The Technical Tips, Tricks, and Pitfalls

4.8
50 個評分
22 條評論

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