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Pascal Janetzky
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Towards Data Science
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We’ve all been there. Our browsers are full of them, our notes are overflowing, and we often have detailed plan on tackling them: online courses about Machine Learning, articles about Machine Learning, videos about Machine Learning.
Roaming around the internet, we are endlessly populating our list of “cool ML resources.” When we find a pre-made curriculum about how we can learn ML in X days, it’s as if we have hit the jackpot. “That’s exactly what I needed,” we tell ourselves, “that’s how I can learn ML.” We are passionate about ML, and we quickly get geared up by shiny new things, courses, learning material.
When we find a resource that resonates with us, we readily envision ourselves sitting behind our desks, with books (or screens) all around us. We see ourselves happily reading page after page, coding line after line, implementing challenging algorithms. We can almost feel ourselves being X days further into the future.
Then we find another article about learning ML, complete with recommended courses, course orders, alternative paths. Another daydream starts.
It happens to anybody at any stage of the ML journey; it recently happened to me. Five years into my ML journey, I realized I needed to re-engage with the…
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