Machine Learning is a programming which gives capability to learn automatically through the machine. This means in other words that these programs change their behavior by learning from data. The three types of machine learning in the process.
Supervised Learning: Supervised learning, in the format of artificial intelligence (AI) and machine learning, is a type of system in which both input and desired output data are provided. Input and output data are labelled for classification to provide a learning basis for future data processing.
Unsupervised Learning: Unsupervised learning is the training of an artificial intelligence (AI) algorithm using information that is neither classified nor labeled and allowing the algorithm to act on that information without guidance.
Using Python in Machine Learning:
Machine learning is broad concept using python and R programming language. Artificial intelligence is the concept running through For each major algorithm that we cover, we will discuss the high level intuitions of the algorithms and how they are logically meant to work. Next, we’ll apply the algorithms in code using real world datasets along with a module, such as with Scikit-Learn. Finally, we’ll be diving into the inner workings of each of the algorithms by recreating them in code, from scratch, ourselves, including all of the math involved. This should give you a complete understanding of exactly how the algorithms work, how they can be tweaked, what advantages are, and what their disadvantages are.
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