Machine learning (ML) is an interesting topic for a lot of people since 1959. Despite the interest, people are afraid to start learning. Because of the fundamentals of machine learning, it requires some knowledge of linear algebra and calculus. This barrier frightens people and keeps them away from ML with curiosity. I was one of them until the last couple of weeks. But my fear was not on the basics of linear algebra and calculus as I learned fundamentals of them when I get a computer engineering degree. I was afraid of more advanced math techniques that I also learned in university and forgot them all. After years of staying away, I decided to give it a shot and started Machine Learning course in Coursera. I was lost in first 6–7 weeks. My understanding was not enough, even if I passed quizzes and completed programming assignments with help from other students and forums. I was feeling incompetent. By the reason of this is my fear was with me all the time. But one day, I was talking with my friend about this and he said it’s completely normal that I don’t understand. This is the nature of learning. He finished his words with “Keep pushing and eventually the blur on your eyes will disappear.”. I pushed a bit more and one weekend, my blurred eyes are cleaned and what I saw was beautiful. I was not inadequate. That felt good. Yet here, I’m starting to write the things that I’ve learned.
Machine Learning is a field of computer science that gives computers the ability to learn without being explicitly programmed. (Wikipedia) Machine learning was born during the early development of artificial intelligence and pattern recognition. Like all the other technologies it grew into the industry with the help of the businesses. There is one widely used formal definition of ML algorithms:
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