By embracing three key mindsets, Kennedy channeled his ardour for AI and studying right into a profitable profession

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Meet Kennedy, a 24-year-old data professional and tech community builder. After getting his first taste of artificial intelligence (AI) four years ago in Kenya, he started learning on Coursera before putting his skills to work for companies like NVIDIA and NCBA Bank. Now, he’s a rising leader in developer communities in sub-Saharan Africa, championing better representation of Black people in tech industries and empowering future African AI talent. 

We loved learning about Kennedy’s career journey and valuable advice he shared about three mindsets that helped him along the way. You can learn more in the interview below.

Thanks for taking the time to speak with us, Kennedy. From starting out in online learning, to directing AI developers, you’ve achieved some amazing accomplishments. If you had to give advice to learners early on in their career journey, what would it be?

Thank you! To learners just starting out, I’d say: be different, build a solid portfolio of practical solutions, and keep solving more and more complex projects. In fact, become so good at your projects, you make them your credentials! 

That’s great advice. Do you have a certain mindset in approaching your projects and goals?

Definitely. I actually think the best advice I’d give is to embrace three key mindsets: the learning mindset, the focused mindset, and the self-trust mindset.

Can you tell us more about these three mindsets you’ve found to be helpful and why—starting with the first “learning mindset”?

Of course. For the learning mindset, it’s all about becoming a life-long learner and staying relevant. It’s important to adapt to changes in your desired field, and remain agile and flexible to tap into unique opportunities. My recommendation is to be constantly building up your knowledge and expertise at any chance you can.

For my own learning, online courses have been a wonderful place to learn specific skills I’ve been able to apply in job roles and community work. For instance, after completing the Deep Learning Specialization, I developed a deeper understanding of neural networks, machine learning concepts, workflows, and algorithms. In the Deeplearning.AI program, I had an opportunity to gain practical experience, but also build relationships with peers and learn from them. And in completing the Machine Learning Engineering for Production (MLOps) Specialization, I learned how to operationalize and scale leading-edge AI technologies—going on to use that knowledge to solve real-world problems while working at Xetova.

Outside of coursework, I’ve been able to share the experience of learning with others by setting up a Deeplearning.AI Nairobi Community. We all share ideas, collaborate, and build innovatively together—which energizes me for advocating for accessible AI Education across Africa. My peers actually started nicknaming me “The Data Captain from Africa,” and I couldn’t be more humbled.

Learning is so important for growth—and involves a lot of self-discipline. Which leads us to the next mindset, the “focused mindset.” What does this mean to you, and how do you find focus?

The focused mindset is all about maintaining high levels of discipline, dedication, and patience along your journey. I’ve found that motivation is key to navigating the complexity of a subject and not getting disheartened by the wealth of information. You’ll also need to build discipline so that you will continue working after the motivation goes away. It’s important to be dedicated and have patience with yourself.

To make real progress in machine learning, I realized it was important to be mindful of not becoming swayed by overly trendy ideas or hype, and not getting burdened with projects springing up around these two things. Instead, it’s critical to remember to stay motivated and focused toward your goal, and continue to augment your skills.

Also, before enrolling in any course, I always consider whether the specific program will help me achieve my objectives. Analyzing and evaluating this by using metrics can be very helpful, and keeps me focused. For example, metrics I typically tend to use are: industry-ready curriculum, affordability, highly practical sessions, mentorship support, and type of instructor.

Developing the ability to focus is an invaluable skill. In terms of the last mindset, the “self-trust mindset,” why is it important to be able to trust yourself, and how did you learn to do this?

Yes, so with the self-trust mindset, it’s pretty straightforward: you’ve got to be able to trust yourself if you want to achieve your goals. Be courageous enough to follow your passions aggressively, and believe in your capabilities to evolve and grow. Don’t give up on the things you believe in and want to achieve.

One way I started believing in my capabilities is my experience taking the Online Community Leadership course. By complementing my other technical skills with leadership skills, I’ve become an impactful AI and tech community builder, have been able to amplify community work, and contribute to the Deeplearning.AI Coursera Community, where I can mentor the next generation of the global machine learning community.

Growing my leadership abilities has helped me empower, support, and strengthen the professional development of new African AI talent by connecting with the community—whether by participating in interviews and thought leadership AI talks on television, radio, print media, in policy debates, webinars, or LinkedIn.

Remember: skills and learning material alone don’t make you successful. Instead, it’s on you to prove to yourself that what you’ve learned is valuable and beneficial to what you want to accomplish. For me, it’s solving existing problems in the community and seeing the positive impact I’ve helped make—from machine translation projects for Kenyan languages (Kikuyu and Kiswahili languages as part of the Masakhane Community) to using AI to solve pressing problems in the supply chain field.

All three of these mindsets sound extremely helpful for navigating a career. For our last question: What are your personal and career goals for the future?

A few of my goals include: becoming fluent in Spanish and French; furthering my academics; owning a house, new car, and becoming debt-free; transitioning into an executive position at a Fortune 500 company; and building a successful AI startup and solve problems in agriculture and finance. 

Lastly, my long-term goal is to become a subject matter expert in machine learning engineering, time series, data visualization, and storytelling. I want to become the go-to person for anytime that data holds the key to solving a problem—however esoteric, complex, unreal, or challenging it may be.

Thanks so much, Kennedy! 

We hope you enjoyed hearing from Kennedy as much as we did. Interested in learning more about data science and AI? Here’s a list of courses Kennedy has taken:

  1. Natural Language Processing Specialization by DeepLearning.AI
  2. Advanced Machine Learning Specialization by HSE University
  3. Advanced Machine Learning on Google Cloud Specialization by Google Cloud
  4. Deep Learning Specialization by DeepLearning.AI
  5. Business Analytics Specialization by the University of Pennsylvania
  6. AI for Medicine Specialization by DeepLearning.AI
  7. DeepLearning.AI TensorFlow Developer Professional Certificate by DeepLearning.AI
  8. Mathematics for Machine Learning Specialization by Imperial College London
  9. Machine Learning Engineering for Production (MLOPs) Specialization by DeepLearning.AI
  10. Data Visualization with Tableau Specialization by the University of California, Davis
  11. Applied Data Science with Python Specialization by the University of Michigan
  12. Career Success Specialization by the University of California, Irvine 
  13. IBM Data Science Professional Certificate by IBM
  14. Project Management Principles and Practices Specialization by the University of California, Irvine
  15. Tensorflow: Data and Deployment Specialization by DeepLearning.AI

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