**11+ Machine Learning 0 To Hero PNG**. Machine learning is the rave of the moment. Just as humans learn from experience, ml systems learn from data.

For example, if i flip a coin and expect a “heads”, there is a 50%, or 1⁄2, chance that my expectation will be met, provided the “act of flipping”, is unbiased (a fair, or unbiased coin has the same probability to get head or tail). The bayes theorem is a lot more than just a theorem based on conditional probability. Let’s take another classic example of rolling a dice.

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Machine learning is the rave of the moment. The other variants which are discussed in this section are best used for text classification problems, wherein the data features are discrete. Which is the best library for machine learning? If you are not interested in classification, but estimation, i.e.