Machine Learning is Fun!
The world’s easiest introduction to Machine Learning
1.1 Introduction
Following a drizzling, we go for a walk on the wet road. Feeling the gentle breeze and seeing the dusk glow, we bet weather would be great tomorrow. walking to an fruit stand, we pick up a green watermelon with wavy root and suppressed muffled sound; while hoping the watermelon is has arrived at the fitting stage or time to be collected or eaten, we expect some good college grades this semester later all the difficult work on examinations. We wish readers to have similar trust in their studies, yet in the first place, let us take a casual conversation on what is machine learning.
Taking a closer look at the scenario described above, we notice that it involves many experience-based predictions. For after observing the gentle breeze and sunset glow? We expect this lovely weather in the sky, from our experience, the weather on the next day is often excellent when we experience such a scene in the present day. Additionally, for what reason do we pick the watermelon with green color, wavy root, and muted sound? It is because we have eaten and enjoyed so many watermelons, and those satisfying the above standards are generally ready to be eaten. In a like manner, our learning experience lets us know that difficult work leads to great academic marks. We are sure about our predictions cause we know from our experience and made experience-based choices.
While human beings learn from experience, would computers be able to do the same? The answer is “yes”, and machine learning is what we need. Machine learning is the strategy that makes system performance better by learning from experience by-way-of computational methods. In computer systems, experience exists in the form of data, and the main task of machine learning is to develop learning algorithms that build models from data, we obtain a model that can make predictions (e.g., an uncut watermelon). If we consider computer science as the subject of algorithms, then machine learning is the subject of learning algorithms.
In this book, we use “model” as an overall term for the result gained from data given to machine. In another writing, the term “model” may refer to the global outcome(e.g., a decision tree), while the expression “pattern” refers to the local result (e.g., a single rule).
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