On April 13, DeepLearning.AI and Omdena assembled a panel of machine learning practitioners who shared their first-hand experience of going from non-traditional starting points to building a career in AI. For any aspiring machine learning engineers who missed the event, here’s a few of the speakers’ tips that we hope you’ll find helpful:
Title: Senior Deep Learning Engineer, Nvidia Location: Santa Clara, California Education: Bachelor of Technology, Electronics and Communication Engineering, Manipal University; MS, Electrical and Computer Engineering, Carnegie Mellon University Favorite areas: Natural language processing, autoML, and interpretable AI Favorite researchers: Christopher…
Kennedy Kamande Wangari got his first taste of AI just two years ago in Kenya. Since then, he has been sprinting towards a top-tier career by taking numerous online courses, working entry-level jobs, volunteering to grow his local AI community, and considering a startup. Next step: Earning a graduate degree. Kennedy shares lessons he learned from starting out at a breakneck pace and explains how he balances professional obligations with personal life.
Jade Abbott turned a childhood desire for a robotic best friend into a career training computers to understand human language. Having studied AI in school, she got her first job coding conventional software, but she found ways to apply machine learning to her work until that became central to her role. In the meantime, she founded an open source project to train NLP models on African languages. She spoke with us about her fascination with language, the importance of community, and how to incorporate wine into your learning practice.
Adrián Munguía spent almost two decades working to create AI-powered products in key science and engineering roles at Silicon Valley startups and Fortune 500 corporations globally. In 2019, he founded AI MEXICO with an aim to educate AI professionals and create AI solutions for companies.
Steffen Konrath studied business administration with a focus on marketing and computer science. He’s worked on numerous digital products and also created an AI community in Zurich called AI Suisse.He is fascinated with trying to find the truth beneath the surface and uses data for this purpose.
Yan Xu completed her PhD in electrical engineering and currently works as a senior scientist at PROS Inc in Houston to develop innovative machine learning algorithms for pricing and sales effectiveness in e-commerce. She creates machine learning applications to drive business growth across industries and spends her spare time speaking on AI and ML-related topics in public.
Hailing from Lebanon, Mo Rebaie completed his Bachelor’s in Math and Computer Science. He works as a deep learning engineer at Rebaie Analytics Group developing algorithms and creating prototypes for AI research labs and as an AI instructor at Coursera. He believes that providing opportunities via events, meetups, and panels can help foster a sense of community.
Ghaith Sankari graduated with a Bachelor’s Degree in AI and NLP from the University of Aleppo, Syria and now works in Dubai. Apart from being a DeepLearning.AI Event Ambassador, he’s also a mentor for our AI for Medicine Specialization. He believes in bringing people together to achieve collective excellence and make the world better.
A year ago, we started our Pie & AI Event Ambassador Program. Since then, 100+ highly accomplished Event Ambassadors have organized more than 200 Pie & AI events – attended by 40K+ learners from 45 countries! This week, Ayyüce Kızrak is an AI researcher at the Digital Transformation Office (The Presidency of The Republic of Turkey) and at the Department of AI Engineering at Bahçeşehir University. She believes that it’s her social responsibility to use AI for environmental good.
Public service runs deep in Ronisha Carter’s family. Her mother was a Navy medical technician, and her father worked for the Air Force in information management. The two parents ignited a sense of curiosity and love of learning that led Ronisha and her siblings to explore technology. Her sister was the first in the family to earn a Bachelor’s degree, and her brothers became software developers. Ronisha joined the Air Force and went on to earn a Bachelor’s in computer science and a Master’s in computer engineering before carving a unique path for herself in AI.
As a child, Jasjeet Thind led the other kids at his daycare to engage in shenanigans. He still exercises his leadership skills, but instead he uses them to organize Zillow’s army of engineers as they aim to automate everything about real estate. Thind became fascinated with data as a college student at Cornell University in the 1990s, and his career exploded along with the deep learning revolution. Read more.
Growing up in Mauritania, Adama Diallo was fascinated by the human brain. Now, as an AI developer at the software company Paradigm, he’s using artificial intelligence to map architectural spaces. In this edition of our Working AI series, Adama discusses his projects, advice for learners, and views on social bias in the industry. Read more.
[et_pb_section][et_pb_row][et_pb_column type=”4_4″][et_pb_text] After a decade in the wireless communications industry, Cherif was ready for a change. As he gained expertise at Palm and Apple, he kept a close eye on the rise of deep…
While much of the AI revolution has served consumers, AI has a lot to offer to other aspects of society. I work at Noble.AI mainly because it gives me the opportunity to stay close to science. At Noble.AI, we’re building software that speeds up the work of scientists, researchers, and engineers by 10 to 100 times.
Searching for more information, I stumbled upon the Deep Learning Specialization. I wasn’t sure how far I’d go, but after taking the first course in the Specialization, I was completely hooked. For the next 7 months, I ate courses for breakfast, lunch and dinner.
Nitin pushed the limits of his software engineering skills to build important products, but he knew he needed to learn more. After taking the Deep Learning Specialization, he specifically applied to jobs that would allow him to transition from web performance to machine learning engineer.
