Practical Project Management for Machine Learning Projects (Udemy.com)

( 10 Reviews )

Created by: Zorina Alliata

Produced in 2018

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Overall Score : 82 / 100

Course Description

The last few years have seen a meteoricrise in disciplines related to using and exploring large quantities of data(Big Data), such as Artificial Intelligence and the Internet of Things. A partof the Artificial Intelligence domain, Machine Learning and Data Science inparticular took hold in many corporations and started impacting the businessoutcomes. In turn, IT Project Managers are suddenly facing a different type ofproject they are asked to manage: the Machine Learning project. This course is addressed to experienced IT Project Managers whowant to understand how to manage Machine Learning projects, what are thespecific challenges they will face, and what are some best practices to helpthem successfully deliver business value. Who this course is for:

This course is addressed to experienced IT Project Managers who want to understand how to manage Machine Learning projects, what are the specific challenges they will face, and what are some best practices to help them successfully deliver business value.

Time: 37m

Instructor Details

Zorina Alliata

4.1 Rating
10 Reviews

Zorina Alliata has been in the I.T. industry for over twenty years, after earning a Bachelor of Science in Computer Science and a Master in Business Administration degree. Zorina started as a programmer for eCommerce websites, then moved into Business Analysis and eventually Project Management, which she has been doing for more than ten years, working in the financial industry, education and insurance. During her career, Zorina mentored younger employees, as well as participated in several charitable programs to explain what I.T. really is and teach people how to get jobs in this industry. Zorina is the creator of the book series "Get I.T.!" where she, in collaboration with other authors, addresses an under-served audience - people with no formal background, education or training in I.T. who are interested in breaking into the industry.

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By Dylan Alliata

Clear explanations, interesting material, originals and creative examples. Really helped my understanding of how to manage a machine language learning project.

By Liubov Abdullina

Nice course! In 40 minutes you will get a clear understanding about specific features of PM in ML! It would be nice to have a course about ML adoption. Thank you!

By Guillaume Tourneur

I would have give it a 5 if the class was enriched with a use case at the end and the audio was equalized.

By Subhendu Ghatak

Good, useful course. What the instructor said tallies with my own limited experience in machine learning - so I know that I am on the right track. However, agile methodology seems to be necessary according to the instructor - we need to brush up on that. Overall, the course is short, yet complete. Thanks.

By Leonardo Belalcazar Fernandez

Deseo profundizar mas en el tema

By Lyubov Berzin

I am about to start working with my first ML team as a Scrum Master/Project Manager. I started looking for help on how to approach the management aspect and am very glad that I came across this course! I got half-way through it, went to my first meeting with the team leadership and impressed them with the questions I was able to ask thanks to what I learned from this course! There are things this course isn’t- it’s not in depth, it doesn’t go into specific ML concepts and it’s not very long. But if you need a solid grasp of what you might find, and need to consider, at each ML project phase, this will definitely help you. The instructor even addresses the fact that your team is very likely to be young (since ML and data science are new disciplines and you get kids that are just coming out of school with these skills) and how to adapt to that! Recommend!

By Anand Huilgol

I think the course is very basic. Focuses more on Agile rather than Machine Learning projects. What we need to know more is specific issues in Machine Learning programs e.g.

1. When model(s) are being explored iteratively, how do you control/evaluate/track?

2. Approximately what % of effort goes in various phases. (We have the knowledge about regular projects, so would help to know the same for ML project)

3. How do you fix the project plan? The model itself is not finalized, and we have to make a plan.

4. Systematic way or framework of 'identification & mitigation' of risks for ML projects?

By Damon Dagnall

This course is very relevant to the Project Manager's role in ML and AI projects and is ground-breaking. It has been an immediate help in some of the challenges we have encountered to date and I recommend it to the professional Project Manager.

By Laura Christiana