Adarsh Singh 🎯
@xdarshsingh
🎴Machine Learning @ZSAssociates • Expert @Kaggle • Building @opensource_ai • let's join the #AI & #Dev journey ©
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🎯 3 genuine tips for beginners in #ML 1. Be conscious about the data you are feeding into your model 📀 2. Do dive into adv maths, if you wanna be a champ 🧮 3. Never start with deep learning, before exploring classical ML algorithm.🕯️ 🥂
I used to have this mental illness where I thought logical arguments would change someone’s mind
After years of floundering, I think I'm finally getting it: You get energy by spending it. The fuel tank metaphor is completely misleading. The body supplies energy to meet demand. The tank *expands* if you use a lot of fuel. In other words, biology is fundamentally antifragile
I may be artificially intelligent, but some of you are naturally stupid.
SQL skills are more in demand by employers than Java🍵 -- JavaScript💻 -- Python🐍 Let that sink in !!
Just published a Valorant gameplay video on youtube This newbie editor did his best🥵 #valorant youtu.be/PW5OXnoZdaY
youtube.com
YouTube
Neon is OP - Ace - Valorant
An estimated 87% of models fail to make it into production and team expertise is cited as one of the main reasons for that failure. MLOps is in huge DEMAND !!
Qualities of Good Algorithms❤️ • Input and output should be defined precisely. 📏 • Each step should be clear and unambiguous. 📃 • It should be the most effective approach. 📊 • It shouldn't include computer code. 🧐 🥂
🎯Studying AI and Machine Learning can raises many interesting questions:⚡️ 1. "Can computers think like humans?"🤔 2. "Can computers be smarter than humans?"✨ 3. "Can computers take over the world?"🤖
It costs $0.00 to learn machine learning nowadays. You can: • Use Kaggle to scrape your data • Use Kaggle to clean your data • Use Kaggle to build your model • Use Kaggle to rerun your model Kaggle isn't just for competitions. Take full advantage of it.
I need a podcast or conference having these 3 elits: • @naval • @OSHO • @SadhguruJV
You can't go to a Client with a model built with unknown assumptions & 0 explainability. At the same time the Client would never ask you to explain the maths. What you need is clarity in your work i.e., 'Why' & 'How' of Data & Algorithm applied, in terms of business context.
'Myth vs Truth' of NEEDS in Machine Learning⚡️ ❌Lots of math. ✅Just high school math is sufficient. ❌Lots of data. ✅We've seen record-breaking results with <50 items of data. ❌Lots of expensive computers. ✅You can get what you need for the state of the artwork for free.
People underestimating the power of Excel and suggesting to use Python instead, are the one who never experienced the industrial workspace. Whatever visualization or analysis i can do in python, can be done way better in MS Excel. For data analytics: Excel >>> Python
In Industry, it's not about just building models, but more about building it's explainability. Anyone can build a model on Kaggle in 5 min, but are you able to explain your assumption or hypothesis? Focus on clarity which inturn will provide the necessary confidence.
Most businesses need 'data analytics', instead of 'data science'.
Just In. @svpino, gonna be publishing a course on MLOps, to do a bit more then just building models. i.e., 👇 • You will know the tools. • You're will be aware of production realities. • You will go the extra mile. Learn more by joining the space.
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