๐๐ป๐๐ฟ๐ผ ๐๐ผ ๐ ๐ฎ๐ฐ๐ต๐ถ๐ป๐ฒ ๐๐ฒ๐ฎ๐ฟ๐ป๐ถ๐ป๐ด ๐ก
Let me introduce you to the absolute starting of Machine Learning
-What is ML?
-Its types
-Some real-world applications
๐ช๐ต๐ฎ๐ ๐ถ๐ ๐ ๐? ๐ป
I have said this earlier too that I have done various ML courses but one of the best definition is from Andrew Ng course-
"Machine learning is the science of getting computers to act without being explicitly programmed."
๐ง๐๐ฝ๐ฒ๐ ๐ผ๐ณ ๐ ๐ฎ๐ฐ๐ต๐ถ๐ป๐ฒ ๐๐ฒ๐ฎ๐ฟ๐ป๐ถ๐ป๐ด
-Supervised
-Unsupervised
-Reinforcement
-Semi-supervised
๐ฆ๐๐ฝ๐ฒ๐ฟ๐๐ถ๐๐ฒ๐ฑ ๐๐ฒ๐ฎ๐ฟ๐ป๐ถ๐ป๐ด ๐ฌ
If the input is square, triangle, and hexagon.
We trained the model that -
4 sides = square
3 sides = triangle
6 sides = hexagon
and when we test on new data it already knows how to predict and it does on basis of number of sides.
๐จ๐ป๐๐๐ฝ๐ฒ๐ฟ๐๐ถ๐๐ฒ๐ฑ ๐๐ฒ๐ฎ๐ฟ๐ป๐ถ๐ป๐ด ๐
Here we do not classify/label data.
For example, if watermelon and apple are mixed together.
Algorithm will create two clusters and learn from data itself that apple is small red and watermelon is big green etc.
๐ฅ๐ฒ๐ถ๐ป๐ณ๐ผ๐ฟ๐ฐ๐ฒ๐บ๐ฒ๐ป๐ ๐๐ฒ๐ฎ๐ฟ๐ป๐ถ๐ป๐ด ๐ค
The concept of RL is so simple.
It is basically like training your pet to do tricks.
If it does it correctly you reward otherwise you punish and on the basis of feedback from the environment, it learns.
๐ฆ๐ฒ๐บ๐ถ-๐๐๐ฝ๐ฒ๐ฟ๐๐ถ๐๐ฒ๐ฑ ๐๐ฒ๐ฎ๐ฟ๐ป๐ถ๐ป๐ด ๐
In the real world, there are many datasets that are neither completely labeled nor completely unlabeled.
Therefore both supervised and unsupervised learning are not the best training methods here.
S-S works on both.
๐ฅ๐ฒ๐ฎ๐น-๐ช๐ผ๐ฟ๐น๐ฑ ๐๐ฝ๐ฝ๐น๐ถ๐ฐ๐ฎ๐๐ถ๐ผ๐ป๐ ๐ผ๐ณ ๐ ๐ โ
-Voice Assistants (Alexa, Siri, etc)
-Virtual Chatbots(Zomato, swiggy, amazon Customer Care complaint)
-Tesla Driverless Car
-Weather Prediction
-Netflix Movie Recommendation
Hey all, hope you all are learning something new from my threads.
I daily post threads about ML, Python, etc.
If you like my content, give retweet to the first tweet of the thread :)
Thanks !!
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