A Beginner’s Guide to AI/ML 🤖👶
Curated from: medium.com
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Why machine learning matters
Artificial intelligence will shape our future more powerfully than any other innovation this century.
Much of our day-to-day technology is powered by artificial intelligence. AI is used to design evidence-based treatment plans for cancer patients, instantly analyze results from medical tests to escalate to the appropriate specialist immediately, and conduct scientific research for drug discovery.
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The semantic tree: artificial intelligence and machine learning
Today AI is used to design evidence-based treatment plans for cancer patients, instantly analyze results from medical tests to escalate to the appropriate specialist immediately, and conduct scientific research for drug discovery.
Machine learning is a subfield of artificial intelligence. Its goal is to enable computers to learn on their own. A machine’s learning algorithm enables it to identify patterns in observed data, build models that explain the world, and predict things without having explicit pre-programmed rules and models.
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Studying Machine Learning
The advent of greater-than-human-level artificial superintelligence (ASI) could be one of the best or worst things to happen to our species. It carries with it the immense challenge of specifying what AIs will want in a way that is friendly to humans.
Machine learning is at the core of our journey towards artificial general intelligence, and in the meantime, it will change every industry and have a massive impact on our day-to-day lives.
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How to approach machine learning
- T-shaped approach - Read from beginning to end. Summarize each section in your own words as you go; this encourages active reading and stronger retention.
- Focused approach - Jump straight to the sections you’re most curious about and focus your mental energy there.
- 80/20 approach - Skim everything in one go, make a few notes on interesting high-level concepts, and call it a night.
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