Break into AI: I took an online course, what's next? | Summary and Q&A

November 19, 2020
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Break into AI: I took an online course, what's next?


Learn how to transition into a career in AI research, even without a computer science background, by focusing on building practical skills and leveraging available resources.

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Key Insights

  • 🌍 Practical skills, such as participating in competitions and real-world projects, are crucial for gaining experience in machine learning.
  • 🤗 Leveraging open-source frameworks like TensorFlow and Keras can expedite the development process and allow for faster deployment of models.
  • 💪 Building a strong network and collaborating with AI communities can provide valuable mentorship and learning opportunities.
  • 👨‍🔬 Domain expertise and a deep understanding of specific industries can be advantageous when searching for AI roles in specialized fields.


hi everyone and welcome my name is sandia simhan and i'm the director of marketing and communications at we appreciate you taking some time out to join us for this event at our mission is to make world-class ai education accessible to everyone we aim to empower our community of aspiring and current machine learning p... Read More

Questions & Answers

Q: How can I gain experience in machine learning if I don't meet the job requirements?

Focus on your strengths and find opportunities where your skills align. Take on freelance projects, participate in open-source work, and collaborate with AI communities. Showcase your ability to solve problems and build real-world applications.

Q: Are theoretical or practical skills more important for a machine learning engineer in big companies?

Both theoretical and practical skills are important. Companies expect machine learning engineers to have a strong foundation in software engineering principles while also being proficient in machine learning algorithms and frameworks. Practical skills in software engineering and deploying models are crucial for success in big companies.

Q: Is it necessary to have higher education, such as a master's or a PhD, to work as an AI researcher?

Higher education can be beneficial, especially for research-focused roles in big companies or academic institutions. However, it is not the only path. Building a strong portfolio through research projects, publishing papers, and collaborating with established researchers can also be a valuable alternative for gaining experience in AI research.

Summary & Key Takeaways

  • The panelists discuss their journeys from non-computer science backgrounds to successful careers in AI.

  • They emphasize the importance of practical skills, such as participating in competitions, taking on real-world projects, and working with AI communities.

  • The panelists recommend focusing on building a portfolio, demonstrating domain expertise, and continuously learning and staying updated with research papers and new technologies.

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