Aria Chang How to break into machine learning technologies as a young professional

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Unlocking Your Potential: A Journey Into Machine Learning

Welcome to a session designed to demystify the world of Machine Learning. Be it beginners just dipping their toes into coding or experienced professionals proficient in programming, this article helps elucidate the process of diving into Machine Learning and improving your portfolio.

My Journey In Machine Learning

My journey in Machine Learning began during my Bachelor's at UC Davis where I received one of the Top 50 MLH Most Inspirational Hackers award and an IEEE conference paper award. Even with these successes, it's essential to acknowledge that my journey wasn't always smooth.

Three years before these accolades, I was struggling to understand basic computer science concepts, felt lost and disheartened. However, an internship in web development at a nonprofit human rights organization turned things around, bestowing me with the confidence that I needed and a defining moment in my tech career.

While learning can be arduous initially, remember, there is always light at the end of the tunnel – never give up!

Developing Machine Learning Expertise

The first step to getting started in machine learning is to identify your passion. Mine lay in the intrinsic beauty of writing math proofs and implementing a neural network using Python. Remember to stick to your enthusiasm – whatever it might be!

Creating Your Machine Learning Application

  1. Build a Support System: This could be a mentor, professor, or an industry professional who can provide you with insights and feedback.
  2. Decide the Type of Application: It's crucial to have a team that covers the entire app development stack, backend server and database management, front-end development, and has knowledge of the machine learning libraries.
  3. Know Your Target Audience: Understanding who is going to use your model is crucial in evaluating its performance.
  4. Find Training Data: Remember, finding data to train your machine learning model isn't the hard part; it's finding data you can use for testing after you have trained your model.
  5. Develop and Deploy Your Model: Finally, decide on your platform for deployment.

Incorporate these steps into creating your application if you decide to venture into research.

An Example: Reinforcement Learning

During an AI class in my undergrad days, my team developed a reinforcement learning model for the real-time strategy game Starcraft. The successful deployment of this project, where the AI showed marginal improvement, sparked my interest in machine learning, leading me to a summer internship at a Smart Grid Institute.

The Crucial Takeaway

Always apply what you have learned to new projects and take on new challenges. Remember, every meaningful project you complete adds up, vastly improving your portfolio and increasing your chances of securing a valuable position in AI or machine learning.

Building a Machine Learning Portfolio

Want to gain visibility? Attend hackathons or participate in competitions. Engaging in such events instills confidence and enhances your profile. More importantly, use such platforms to create projects on something you are passionate about, whether it's beneficial for social good or healthcare needs.

Stay committed, ask questions, seek and give help freely, and never be afraid to take on new challenges.

Conclusion

Remember, "It doesn't matter how long it takes you to get to where you want as long as you get there in the end". Engage, experience, learn, and you too can carve your path in Machine Learning. Connect with me on LinkedIn and feel free to ask any queries or discuss any doubts. Happy learning!


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