Spezialisierung für Machine Learning Rock Star – the End-to-End Practice

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Spezialisierung für Machine Learning Rock Star – the End-to-End Practice

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.

Dozent: Eric Siegel

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3-teilige Kursreihe

Befassen Sie sich eingehend mit einem Thema

4.8

(144 Bewertungen)

Stufe Anfänger

Empfohlene Erfahrung

4 Wochen zu vervollständigen

unter 10 Stunden pro Woche

Flexibler Zeitplan

In Ihrem eigenen Lerntempo lernen

3-teilige Kursreihe

Befassen Sie sich eingehend mit einem Thema

4.8

(144 Bewertungen)

Stufe Anfänger

Empfohlene Erfahrung

4 Wochen zu vervollständigen

unter 10 Stunden pro Woche

Flexibler Zeitplan

In Ihrem eigenen Lerntempo lernen

Was Sie lernen werden

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

Kompetenzen, die Sie erwerben

Kategorie: MLOps (Machine Learning Operations)
Kategorie: Decision Tree Learning
Kategorie: Applied Machine Learning
Kategorie: Machine Learning
Kategorie: Artificial Intelligence and Machine Learning (AI/ML)
Kategorie: Statistical Modeling
Kategorie: Business Leadership
Kategorie: Technical Management
Kategorie: Performance Measurement
Kategorie: Predictive Analytics
Kategorie: Data Ethics
Kategorie: Business Analytics

Wichtige Details

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Unterrichtet in Englisch

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Spezialisierung - 3 Kursreihen

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.

Praktisches Lernprojekt

Problem-solving challenges: Form an elevator pitch, build a predictive model by hand in Excel or Google Sheets to visualize how it improves, and more (no exercises involve 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.

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

KURS 114 Stunden

Was Sie lernen werden

Participate in the deployment of machine learning
Identify potential machine learning deployments that will generate value for your organization
Report on the predictive performance of machine learning and the profit it generates
Understand the potential of machine learning and avoid the false promises of “artificial intelligence”

Kompetenzen, die Sie erwerben

Kategorie: Machine Learning

Kategorie: Predictive Modeling

Kategorie: Decision Tree Learning

Kategorie: Predictive Analytics

Kategorie: Artificial Intelligence

Kategorie: Data Science

Kategorie: Business Analytics

Kategorie: Data Ethics

Kategorie: Applied Machine Learning

Kategorie: Social Justice

Kategorie: Business Ethics

Kategorie: Data-Driven Decision-Making

Kategorie: Performance Analysis

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

KURS 213 Stunden

Was Sie lernen werden

Apply ML: Identify opportunities where machine learning can improve marketing, sales, financial credit scoring, insurance, fraud detection, and more
Plan ML: Determine the way machine learning will be operationally integrated and deployed, and the staffing and data requirements to get there
Greenlight ML: Forecast the effectiveness of a machine learning project and then internally sell it, gaining buy-in from your colleagues
Lead ML: Manage a machine learning project, from the generation of predictive models to their launch

Kompetenzen, die Sie erwerben

Kategorie: Applied Machine Learning

Kategorie: Business Metrics

Kategorie: Performance Measurement

Kategorie: Data Science

Kategorie: Business Transformation

Kategorie: Leadership and Management

Kategorie: Data Ethics

Kategorie: Machine Learning

Kategorie: Predictive Analytics

Kategorie: Strategic Leadership

Kategorie: Business Leadership

Kategorie: MLOps (Machine Learning Operations)

Kategorie: Data Processing

Kategorie: Technical Management

Kategorie: Data-Driven Decision-Making

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

KURS 317 Stunden

Was Sie lernen werden

Participate in the application of machine learning, helping select between and evaluate technical approaches
Interpret a predictive model for a manager or executive, explaining how it works and how well it predicts
Circumvent the most common technical pitfalls of machine learning
Screen a predictive model for bias against protected classes – aka AI ethics

Kompetenzen, die Sie erwerben

Kategorie: Ethical Standards And Conduct

Kategorie: Decision Tree Learning

Kategorie: Predictive Modeling

Kategorie: Deep Learning

Kategorie: Artificial Neural Networks

Kategorie: Data Science

Kategorie: Data Analysis

Kategorie: Regression Analysis

Kategorie: Machine Learning

Kategorie: Business Metrics

Kategorie: Artificial Intelligence and Machine Learning (AI/ML)

Kategorie: Statistical Modeling

Kategorie: Performance Measurement

Kategorie: Machine Learning Algorithms

Kategorie: Data Ethics

Kategorie: Predictive Analytics

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Fügen Sie dieses Zeugnis Ihrem LinkedIn-Profil, Lebenslauf oder CV hinzu. Teilen Sie sie in Social Media und in Ihrer Leistungsbeurteilung.

Dozent

Eric Siegel

SAS

5 Kurse16.933 Lernende

von

SAS

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Häufig gestellte Fragen

It's for both. To run a successful machine learning project, business leaders need to learn how machine learning works – even if they're not going to be doing the number crunching themselves. On the other hand, data scientists also benefit from a holistic curriculum that covers not only the core analytical methods, but contextualizes those methods in business terms. This curriculum serves both business leaders and data scientists, but it will not prepare you to be a hands-on practitioner – you'll need additional training for that. Rather, it is complementary to hands-on training, covering topics usually skipped there, including machine learning project management, how to prepare the data to serve business-level requirements, evaluation – calculating and reporting on the performance of a predictive model in business terms – and a deep dive into ethical issues, identifying risks to social justice and civil liberties that arise with a machine learning project and presenting options to avert these risks.

This curriculum is fully accessible to non-technical learners, business managers, and newcomers. No heavy math or coding is involved and no background in statistics or programming is required. The most technical course of this three-course specialization is the last one, which delves into the predictive modeling methods themselves. It does so in as revealing and concrete a manner as possible so as to remain relevant and understandable to non-technical learners.

No, the curriculum is vendor-neutral and universally-applicable. The contents and learning objectives apply, regardless of which machine learning software tools you end up choosing to work with. However, this specialization includes several illuminating software demos of machine learning in action using SAS products.

It's for both. This specialization focuses on commercial deployment and yet the curriculum is conceptually complete like a university course, as the instructor is a former university professor. It serves business professionals and decision makers of all kinds, such as executives, directors, line of business managers, and consultants – as well as data scientists. And it's 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 three-course specialization is equivalent to one full-semester MBA or graduate-level course.

The ordered sequence of three courses is intentional and important. The first one covers the basics and sets the foundation. Then the second focuses on the business side of machine learning and the third on the tech side. Take them in this order:

Course 1 – The Power of Machine Learning: Boost Business, Accumulate Clicks, Fight Fraud, and Deny Deadbeats

Course 2 – Launching Machine Learning: Delivering Operational Success with Gold Standard ML Leadership

Course 3 – Machine Learning Under the Hood: The Technical Tips, Tricks, and Pitfalls

When you use machine learning, you aren't just optimizing models and streamlining business. You're governing. The models you develop embody policies that determine access to opportunities and resources for many people. Building equitable algorithms is a crucial priority. Doing so is fundamental to harnessing the power of machine learning in a responsible manner. But a great challenge comes in defining and agreeing on the specific standards that qualify as equitable.

Each of the three courses of this specialization end with several videos covering topics in machine learning ethics. This coverage aims to move the discussion forward and to help form concrete standards. This means progressing beyond vague platitudes such as "be fair" and "use ML responsibly" and establishing specific, actionable principles. The topics covered include machine bias, discriminatory algorithms, model transparency and explainability, and the right to explanation.