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Machine Learning Presentation

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MACHINE LEARNING
GROUP-1
ABHAYAKRISHNA VV(19001)
ASHWIN SHETTY(19013)
KATTAMURI V N S JAYARAJYALAKSHMI(19025)
PRABJOT KAUR(19037)
SHRADDHA TIWARI(19049)

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Introduction

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MACHINE LEARNING GROUP-1 ABHAYAKRISHNA VV(19001) ASHWIN SHETTY(19013) KATTAMURI V N S JAYARAJYALAKSHMI(19025) PRABJOT KAUR(19037) SHRADDHA TIWARI(19049) Introduction Machine Learning Machine learning is a branch of artificial intelligence that allows computer systems to learn directly from examples, data, and experience. Through enabling computers to perform specific tasks intelligently, machine learning systems can carry out complex processes by learning from data, rather than following pre-programmed rules. Terminologies  Model: specific representation learned from data by applying some machine learning algorithm.  Feature:  Target: A target variable or label is the value to be predicted by our model  Training: The idea is to give a set of inputs (features) and it’s expected outputs (labels), so after training, we will have a model (hypothesis) that will then map new data to one of the categories trained on.  Prediction: Once model is ready, set of inputs is fed and the model will give predicted output. Classification of machine learning  Supervised machine learning: It is by using labelled examples to predict future events. Starting from the analysis of a known training dataset, the learning algorithm produces an inferred function to make predictions about the output values. The training process continues until the model achieves the desired level of accuracy on the training data.  Unsupervised machine learning: It is used w ...
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