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Inside this Book – Machine learning is a subfield of computer science that is concerned with building algorithms which, to be useful, rely on a collection of examples of some phenomenon. These examples can come from nature, be handcrafted by humans or generated by another algorithm. Machine learning can also be defined as the process of solving a practical problem by 1) gathering a dataset, and 2) algorithmically building a statistical model based on that dataset. That statistical model is assumed to be used somehow to solve the practical problem. To save keystrokes, I use the terms “learning” and “machine learning” interchangeably.
Inside this book –The Hundred Page Machine Learning Book PDF Book by Andriy Burkov – Why is a machine-learned model capable of predicting correctly the labels of new, previously unseen examples? To understand that, look at the plot in fig. 1. If two classes are separable from one another by a decision boundary, then, obviously, examples that belong to each class are located in two different subspaces which the decision boundary creates. If the examples used for training were selected randomly, independently of one another, and following the same procedure, then, statistically, it is more likely that the new negative example will be located on the plot somewhere not too far from other negative examples. The same concerns the new positive example: it will likely come from the surroundings of other positive examples. In such a case, our decision boundary will still, with high probability, separate well new positive and negative examples from one another. For other, less likely situations, our model will make errors, but because such situations are less likely, the number of errors will likely be smaller than the number of correct predictions. Intuitively, the larger is the set of training examples, the more unlikely that the new examples will be dissimilar to (and lie on the plot far from) the examples used for training. To minimize the probability of making errors on new examples, the SVM algorithm, by looking for the largest margin, explicitly tries to draw the decision boundary in such a way that it lies as far as possible from examples of both classes.
The Hundred Page Machine Learning Book by Andriy Burkov PDF : eBook Information
- Full Book Name – The Hundred Page Machine Learning Book
- Author of this Book – Andriy Burkov
- Language – English
- Book Genre – Science, Technology
- Download Format – PDF
- Size – 7 MB
- eBook Pages – 152
- Price – Free