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

Highlights
Tuition fee
Free
Free
Unknown
Tuition fee
Free
Free
Unknown
Duration
1 months
Duration
1 months
Apply date
Anytime
Unknown
Apply date
Anytime
Unknown
Start date
Anytime
Unknown
Start date
Anytime
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Taught in
English
Taught in
English

About

This Machine Learning Rock Star – the End-to-End Practice course offered by Coursera in partnership with SAS empowers you to generate value with ML delivers the end-to-end expertise you need, covering both the core technology and the business-side practice.

Overview

The Machine Learning Rock Star – the End-to-End Practice course offered by Coursera in partnership with SAS empowers you to generate value with ML.

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.

Key facts

  • 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.
  • 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.
  • 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.

Applied Learning Project

  • 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).

Skills you'll gain

  • Machine Learning
  • Human Learning
  • Applied Machine Learning
  • Machine Learning Algorithms
  • Data Analysis

Programme Structure

Courses included:

  • The Power of Machine Learning: Boost Business, Accumulate Clicks, Fight Fraud, and Deny Deadbeats
  • Launching Machine Learning: Delivering Operational Success with Gold Standard ML Leadership
  • Machine Learning Under the Hood: The Technical Tips, Tricks, and Pitfalls

Key information

Duration

  • Part-time
    • 1 months
    • 10 hrs/week

Start dates & application deadlines

You can apply for and start this programme anytime.

Language

English

Delivered

Online

Campus Location

  • Mountain View, United States

What students do after studying

Join for free or log in to access our complete career info list.

Academic requirements

We are not aware of any specific GRE, GMAT or GPA grading score requirements for this programme.

English requirements

We are not aware of any English requirements for this programme.

Other requirements

General requirements

Beginner Level

  • 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.

Tuition Fees

Tuition fees are shown in and the most likely applicable fee is shown based on your nationality.
  • International

    Non-residents
    Free
  • Out-of-State
    Free

Additional Details

  • Coursera Plus: Subscribe to build job-ready skills from world-class institutions.
  • $59/month, cancel anytime or $399/year with 14-day money-back guarantee

Funding

Coursera provides financial aid to learners who cannot afford the fee. Apply for it by clicking on the Financial Aid link beneath the "Enroll" button on the left. You'll be prompted to complete an application and will be notified if you are approved. You'll need to complete this step for each course in the Specialization, including the Capstone Project. 

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