Spécialisation Machine Learning Rock Star – the End-to-End Practice

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Spécialisation Machine Learning Rock Star – the End-to-End Practice

Name: Machine Learning Rock Star – the End-to-End Practice
Rating: 4.806034482758621 (144 reviews)

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.

Instructeur : Eric Siegel

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Série de 3 cours

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4.8

(144 avis)

niveau Débutant

Expérience recommandée

1 mois

à 10 heures par semaine

Planning flexible

Apprenez à votre propre rythme

Série de 3 cours

Approfondissez votre connaissance d’un sujet

4.8

(144 avis)

niveau Débutant

Expérience recommandée

1 mois

à 10 heures par semaine

Planning flexible

Apprenez à votre propre rythme

Ce que vous apprendrez

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

Compétences que vous acquerrez

Catégorie : Predictive Analytics
Catégorie : Ethics Of Artificial Intelligence
Catégorie : Artificial Intelligence (AI)
Catégorie : Data Science
Catégorie : Machine Learning
Catégorie : Machine learning strategy and leadership
Catégorie : Machine Learning (ML) Algorithms

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Spécialisation - 3 séries de cours

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.

Projet d'apprentissage appliqué

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

COURS 114 heures4.8 (147 évaluations)

Ce que vous apprendrez

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”

Compétences que vous acquerrez

Catégorie : Predictive Analytics

Catégorie : Ethics Of Artificial Intelligence

Catégorie : Artificial Intelligence (AI)

Catégorie : Data Science

Catégorie : Machine Learning

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

COURS 213 heures4.8 (77 évaluations)

Ce que vous apprendrez

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

Compétences que vous acquerrez

Catégorie : Predictive Analytics

Catégorie : Artificial Intelligence (AI)

Catégorie : Data Science

Catégorie : Machine Learning

Catégorie : Machine learning strategy and leadership

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

COURS 317 heures4.9 (64 évaluations)

Ce que vous apprendrez

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

Compétences que vous acquerrez

Catégorie : Predictive Analytics

Catégorie : Artificial Intelligence (AI)

Catégorie : Data Science

Catégorie : Machine Learning

Catégorie : Machine Learning (ML) Algorithms

Instructeur

Eric Siegel

SAS

5 Cours16 428 apprenants

Offert par

SAS

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Étudiant(e) depuis 2020

’J'ai directement appliqué les concepts et les compétences que j'ai appris de mes cours à un nouveau projet passionnant au travail.’

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Foire Aux Questions

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.

Yes! To get started, click the course card that interests you and enroll. You can enroll and complete the course to earn a shareable certificate, or you can audit it to view the course materials for free. When you subscribe to a course that is part of a Specialization, you’re automatically subscribed to the full Specialization. Visit your learner dashboard to track your progress.