gedeapriana | portfolio

I am grateful for the opportunity to have participated in the Bangkit 2023 program. It has been a challenging but rewarding experience. I have learned a lot about machine learning, but more importantly, I have learned how to adapt to change and embrace new challenges. I am confident that these skills will help me succeed in my career and in life.

Interacting

I had to communicate with my teammates who came from different universities and provinces. We had to collaborate online using various tools such as Google Meet, Discord and GitHub. To adapt to these interactions, I learned how to use the tools properly, how to express my ideas clearly and respectfully, how to listen actively and ask questions, and how to give and receive constructive criticism.

Achieving

I had to complete several online courses on Coursera platform that covered topics such as Python programming, and many more. To adapt to these achievements, I learned how to manage my time effectively, how to set SMART goals (specific, measurable, achievable, relevant, and time-bound), how to monitor my progress and adjust my strategies accordingly, and how to celebrate my successes and learn from my failures.

Learning

I had to learn about the latest technologies and trends in the field of machine learning, such as natural language processing, computer vision, deep learning models, and data pipelines. To adapt to these learnings, I learned how to use various resources such as online courses, books, articles, videos, podcasts, and blogs. I also learned how to apply what I learned in practical projects that demonstrated my skills and knowledge.

Working

I had to work with teammates who had different skill levels, interests, personalities, and preferences. We had to divide our tasks according to our strengths and weaknesses. To adapt to these workings, I learned how to use agile methodologies such as Scrum and Kanban that helped us organize our workflow and deliver value incrementally.

Thinking

I had to think about the problem definition, data collection, data analysis, data preprocessing, model selection, model training, model evaluation, model deployment, and model maintenance. To adapt to these thinkings, I learned how to use frameworks such as CRISP-DM (Cross-Industry Standard Process for Data Mining) and OSEMN (Obtain-Scrub-Explore-Model-iNterpret) that helped me structure my data science process.

Gede Apriana

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