If I wanted to break into Machine Learning in 2024, these are 3 types of projects I would have in my portfolio:
Forget the cookie-cutter approach of "1 LLM-powered chatbot, 1 Pytorch project, and 1 scikit-learn implementation".
Trust me, no one's scanning portfolios for general implementations of popular libraries.
Instead of following the crowd, focus on demonstrating depth and breadth in key ML competencies:
1. An End-to-End ML Pipeline
Skills to demonstrate: Data preprocessing, feature engineering, model selection, hyperparameter tuning, deployment, and monitoring.
Trust me you will learn a LOT by doing this.
2. A State-of-the-Art Model Implementation
Skills to demonstrate: Deep understanding of cutting-edge algorithms, ability to read and implement research papers,by translating math equations into working Python code.
3. Find A Real-World Problem you care about.
Skills to demonstrate: Problem framing, business impact assessment, data acquisition, approach and tech stack selection, ethical considerations, and project documentation.
You could integrate all 3 aspects into one comprehensive project or showcase them separately. The key is demonstrating your ability to tackle real-world ML challenges and make a tangible impact.
Stop endlessly consuming courses and start building!
Your future in ML starts with hands-on experience.
If you want to learn more about how to build a portfolio read this article:
Every time I open my feed, it's either on LinkedIn or Substack, I learn new things from you @Meri Nova.
Thank you for providing the beautiful insights from your Journey.
If I wanted to break into Machine Learning in 2024, these are 3 types of projects I would have in my portfolio:
Forget the cookie-cutter approach of "1 LLM-powered chatbot, 1 Pytorch project, and 1 scikit-learn implementation".
Trust me, no one's scanning portfolios for general implementations of popular libraries.
Instead of following the crowd, focus on demonstrating depth and breadth in key ML competencies:
1. An End-to-End ML Pipeline
Skills to demonstrate: Data preprocessing, feature engineering, model selection, hyperparameter tuning, deployment, and monitoring.
Trust me you will learn a LOT by doing this.
2. A State-of-the-Art Model Implementation
Skills to demonstrate: Deep understanding of cutting-edge algorithms, ability to read and implement research papers,by translating math equations into working Python code.
3. Find A Real-World Problem you care about.
Skills to demonstrate: Problem framing, business impact assessment, data acquisition, approach and tech stack selection, ethical considerations, and project documentation.
You could integrate all 3 aspects into one comprehensive project or showcase them separately. The key is demonstrating your ability to tackle real-world ML challenges and make a tangible impact.
Stop endlessly consuming courses and start building!
Your future in ML starts with hands-on experience.
If you want to learn more about how to build a portfolio read this article:
https://lnkd.in/g7sTg8Re
Happy coding!
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