Here at Women’s Tech Hub, we are keen to encourage women to explore new possibilities for future careers. We are building a great network of sponsors and supporters who offer help to our members with networking, updating CV’s, developing confidence etc as well as offering practical introductions to new technical skills. We were recently very lucky to be able to run a 6 session course in Machine Learning & AI to a small group of WTHubbers, offered and taught by Chew-Yean Yam.
Chew-Yean gave her time and expertise for free, insisting that the fee we charged for the course was her donation to support WTHub during our setup period. Her generosity allowed us to pay for the venue for our Diversity in Tech event on 24th June. (Thanks Chew-Yean!)
Read Chew-Yean’s blogpost below and if this inspires you to support us by offering training sessions then we are more than happy to start a conversation. We love giving opportunities to our members, especially those which connect them to local tech companies. While we are on the subject we’d also like to thank KETL again for sponsoring one of the places on the Machine Learning course. KETL are also finding ways to offer training to encourage people into tech jobs and are currently running their second training course on the TALEND tool, which is a great practical introduction to Data analytics and last time resulted in them recruiting two of our members. We want more of this sort of thing!
So, here is Chew-Yean’s blogpost about the course (reposted from her blog here) and why she offered to run it. There are some great quotes from participants at the end!
“My Very Small Step to the Epic Journey of Making STEM More Inclusive” ~ Blogpost by Chew-Yean Yam
I offered this series of Machine Learning workshops for free, for 2 reasons:
- I am passionate about Data Science and Machine Learning;
- I am even more passionate about making STEM more inclusive, by enabling people with such desire, to get into STEM by providing practical help: skill and industrial contacts. The one single criteria that I have set is: have access to a laptop.
The participants paid a nominal fee to guarantee their places in this series, where all the fees are donated to WTHub. The donation has enabled WTHub to host their first ever Diversity in Tech 2017.
This initiative has attracted sponsors from local companies:
- Desklodge has sponsored the venue for the entire series,
- iMBD has sponsored the data,
- KETL has sponsored bursary for those who otherwise can not participate. We are grateful of their contributions.
The outcome of this initiative has been beyond my expectation, especially when there is no prior knowledge of Machine Learning, nor Python! One of the teams predictive models even beat a couple of published ideas!
- This series spans 6 workshops, where during the 6th workshop, the participants present their insights and machine learning models to the industries and individual enthusiasts.
- Format of workshop: collaborative + hands-on. No downloading of endless slides! This format has encouraged self-starter behaviour and pro-active exploration on topic of discussion. I estimated that over 80% of the knowledge gained is through active verbal (in-person) Q&A in a collaborative manner.
- Repository for this series records the activities carried out in this series, together with links to each team’s repositories (code, presentation, video). Due to the collaborative format, this largely records the structure of the material rather than the material itself. However, the outstanding outcome can be witnessed in their presentations.
1. Team Fun With Data Team repo
Highlight: overview on data science and machine learning process
2. Team Robot Iris Team repo
Highlight: Predicting Facebook Likes: Deep analysis on data
3. Team PyFun Team repo
Highlight: Predicting Gross Earning of a Movie: Results beat some published methods, including a published paper
- ‘I really liked the exploration we did the first day, collecting information and doing the guided example on the Azure ML environment. Please keep it!’
- ‘… on day 4, it could be helpful to have some kind of practical assessment. … A smaller task to apply all the knowledge we have built up, before we embark on the bigger task of investigating the ImdB data. A task where we then could see a possible solution and learn from it.’
- ‘The course material covered was perfectly on point – explaining the concepts of machine learning, as well as actually showing us how to use the KNN algorithm helped immensely to wrap my head around what the end goal was. ‘
- ‘… practical assignment and a presentation made us put an effort in. ‘
- ’ … have small individual assignments in between as well, so that we could practice.’
- ’ … a fantastic teacher who understands that learning should be done by the student by practicing the skill that was being thought instead of just hearing the lectures in class. ‘
- ’ … would recommend having an open day allowing opportunity for potential participants to find out more prior to commencing the workshop.’
- ‘… make sure that the students have all necessary software installed beforehand by getting a step by step guide … to set up all the necessary tools. ‘
- ’ Overall, I am extremely happy with the course and the way it was thought. I would happily recommend it to anybody who would like to gain some understanding about machine learning and AI.’
- ‘Thanks again for today, it was very enjoyable and good to see the level of engagement and the progress that your groups made in such a short amount of time. It’s inspired me to think about what training opportunities we might be able to provide to the tech community.’ – iMDB
- ‘… inspired us to think about how to apply machine learning to our data …’ – Zeetta
- KETL and WTH getting women in to Tech
- An Introduction to Machine Learning & AI