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Avis et commentaires pour d'étudiants pour Machine Learning Under the Hood: The Technical Tips, Tricks, and Pitfalls par SAS

4.8
étoiles
29 évaluations
15 avis

À propos du cours

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. If you want to participate in the deployment of machine learning (aka predictive analytics), you've got to learn how it works. Even if you work as a business leader rather than a hands-on practitioner – even if you won't crunch the numbers yourself – you need to grasp the underlying mechanics in order to help navigate the overall project. Whether you're an executive, decision maker, or operational manager overseeing how predictive models integrate to drive decisions, the more you know, the better. And yet, looking under the hood will delight you. The science behind machine learning intrigues and surprises, and an intuitive understanding is not hard to come by. With its impact on the world growing so quickly, it's time to demystify the predictive power of data – and how to scientifically tap it. This course will show you how machine learning works. It covers the foundational underpinnings, the way insights are gleaned from data, how we can trust these insights are reliable, and how well predictive models perform – which can be established with pretty straightforward arithmetic. These are things every business professional needs to know, in addition to the quants. And this course continues beyond machine learning standards to also cover cutting-edge, advanced methods, as well as preparing you to circumvent prevalent pitfalls that seldom receive the attention they deserve. The course dives deeply into these topics, and yet remains accessible to non-technical learners and newcomers. With this course, you'll learn what works and what doesn't – the good, the bad, and the fuzzy: – How predictive modeling algorithms work, including decision trees, logistic regression, and neural networks – Treacherous pitfalls such as overfitting, p-hacking, and presuming causation from correlations – How to interpret a predictive model in detail and explain how it works – Advanced methods such as ensembles and uplift modeling (aka persuasion modeling) – How to pick a tool, selecting from the many machine learning software options – How to evaluate a predictive model, reporting on its performance in business terms – How to screen a predictive model for potential bias against protected classes – aka AI ethics IN-DEPTH YET ACCESSIBLE. Brought to you by industry leader Eric Siegel – a winner of teaching awards when he was a professor at Columbia University – this curriculum stands out as one of the most thorough, engaging, and surprisingly accessible on the subject of machine learning. NO HANDS-ON AND NO HEAVY MATH. Rather than a hands-on training, this course serves both business leaders and burgeoning data scientists alike with expansive coverage of the state-of-the-art techniques and the most pernicious pitfalls. There are no exercises involving coding or the use of machine learning software. However, for one of the assessments, you'll perform a hands-on exercise, creating a predictive model by hand in Excel or Google Sheets and visualizing how it improves before your eyes. 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. It contextualizes the core technology with a strong conceptual framework and covers topics that are generally omitted from even the most technical of courses, including uplift modeling (aka persuasion modeling) and some particularly treacherous pitfalls. VENDOR-NEUTRAL. This course includes illuminating software demos of machine learning in action using SAS products. However, 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. PREREQUISITES. Before this course, learners should take the first two of this specialization's three courses, "The Power of Machine Learning" and "Launching Machine Learning."...

Meilleurs avis

DB
24 août 2020

I'll be honest. This course made me feel more capable on the quantitive algorithms than I think any coding class ever could. When it's taught the right way, this stuff is actually intuitive.

SD
13 août 2020

Thought provoking and innovative approach to learning Machine Learning aspects in unburdened\n\nmanner, beneficial to the beginner and the advanced learner alike.

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1 - 15 sur 15 Avis pour Machine Learning Under the Hood: The Technical Tips, Tricks, and Pitfalls

par Calle J

19 août 2020

Excellent course!

This is a course that everyone should take. It is engaging and fun. I was always inspired to go to the next lesson and learn more. It has some interesting subjects that most other courses don't address, and it has some surprises and eye-opening moments. Two such things are P-hacking and Uplift Modeling, among many other things. The course also brings up a very important topic for Machine Learning: Ethics.

All of this was taught in a fun and easy understandable way. Thank You for this course. I really enjoyed it.

par Carlos P

13 août 2020

Another excellent Course! This is a very nice introduction to the Technical side of Machine Learning, the course even includes a hands on exercise based on Excel/Google Sheet which helps to consolidate what has been learnt. Eric makes fairly complex algorithms/ideas easy to understand.

par Diego B

25 août 2020

I'll be honest. This course made me feel more capable on the quantitive algorithms than I think any coding class ever could. When it's taught the right way, this stuff is actually intuitive.

par Sudarshan K D

14 août 2020

Thought provoking and innovative approach to learning Machine Learning aspects in unburdened

manner, beneficial to the beginner and the advanced learner alike.

par Oleg K

25 août 2020

What a clear course. I love the word algorithm now. Modeling is elegant and I understand that now.

par Yelyzaveta R

15 août 2020

Very interesting and well-structured course. I think it will be very useful for all learners

par Christina M

24 août 2020

A very enjoyable and worthwhile course for ML novices as well as techies.

par AlOtaibi, S M

16 août 2020

Great course

par Laxman G

19 sept. 2020

If only all cars had, the 'machine' learning capability, taught in this course, under their hood .....,,

we would all be driving the safest, fastest, cleanest, coolest, friendliest, most energy efficient cars.

This is the Bugatti La Voiture Noire of Machine Learning Specialization courses.

Thank you Prof. Eric Siegel, SAS and Coursera.

par Eugene Q

25 sept. 2020

Brilliant! I really enjoyed this course. It helped me to understand more about what to do and how (and what not to do) when implementing ML projects. 5 stars!

par Selin N

14 oct. 2020

Great

par Dmitry B

18 août 2020

This course covers quite a few core issues that arise when you apply a machine learning algorithm to real-world problems. However, if you are a seasoned practitioner, you may find that the course does not deliver to the promise given in the description. I was expecting gotchas and a-ha moments, but that did not happen.

par Jorge T

3 oct. 2020

It has been a great effort to democratize access to Machine Learning for everybody. And I think it was achieved. I expected some hands on exercises so as to test the knowledge adquired. Anyway I learnt many usefull concepts for ML Project Management, Pitfalls and Ethics. Recommended course to start.

par Somrita S

29 sept. 2020

The maths must be explained a bit more. At times the information is redundant.Otherwise ,its a great experience to be earned.

par Nakasanga C

14 août 2020

I couldnt go through the entire course yet, so I had to make a thin slice judgement of the course. The presentation is creative and appealing, liked it.Quiz 1 was entirely mathematical, business professionals may step out as soon as they fail the first quiz, from the fear of what lays ahead, I also think that more details of the benefits of the course and/ or application to the workplace should be included in the introduction.( it serves as a major source of motivation to take and finish the course).