Scikit Learn Tutorial | Machine Learning with Python | Python for Data Science Training | Edureka
The document provides an overview of Python certification training focused on machine learning, detailing topics such as scikit-learn installation, types of machine learning (supervised, unsupervised, reinforcement), and various classification and regression algorithms. It emphasizes practical implementations using algorithms like logistic regression and support vector machines, particularly applied to the Iris dataset. Additionally, the document includes installation commands for scikit-learn and highlights its open-source nature and foundational libraries.
www.edureka.co/pythonPython Certification Training Itis a type of Artificial Intelligence that allows software applications to learn from the data and become more accurate in predicting outcomes without human intervention. What is Machine Learning? Training Data Learn Algorithm Build Model Perform Feedback
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www.edureka.co/pythonPython Certification Training Typesof Machine Learning 03 Supervised 01 This is a process of an algorithm learning from the training dataset. This is a process where a model is trained using an information which is not labelled. Reinforcement learning is learning by interacting with a space or an environment. Unsupervised 02 Reinforcement 03
www.edureka.co/pythonPython Certification Training Regression& Classification Classification Classification is the problem identifying to which set of categories a new observation belongs. Classifier Regression Regression is the prediction of a numeric value and often takes input as a continuous value.
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www.edureka.co/pythonPython Certification Training Dataset ➢The data set consists of 50 samples from three species of Iris - Iris Setosa, Virginica and versicolor ➢ Four features were measured from each sample: Length and the width of the sepals and petals, in centimetres. IRIS Dataset
www.edureka.co/pythonPython Certification Training SupportVector Machine (SVM) ➢ SVM is a supervised machine learning algorithm which can be used for both classification or regression challenges ➢ It tries to define a hyperplane which can split the data in the most optimal way