Complete beginner’s guide to Machine Learning in PythonMachine learning has been a disruptive force in the world of software, and today it is being driven even further with deep learning Want to get up to speed fast?Completely up to date, this guide to Python Machine Learning includes a detailed treatment of the popular TensorFlow deep learning library, scikit-learn, and much more. By reading this book, you will be better prepared to meet the challenges and opportunities of data analysis that exist in the world today, and tomorrow.Here is a preview of what you will learn in this guide:What is Machine Learning?Machine Learning TasksSupervised vs. Unsupervised LearningMachine Learning ApplicationsClassificationClusteringDensity EstimationDimensionality ReductionRegression AnalysisMachine Learning in PythonScikit – learnTensorFlowWhat Version of Python to Use?Installing the relevant libraries (If not using Anaconda)Introductory Machine Learning in Python: Iris FlowersImporting the Requisite Libraries and dataSummarizing the Data SetData VisualizationCreating Data ModelsMaking PredictionsCommon Data Models Used in Machine LearningSimple Linear AlgorithmsNonlinear AlgorithmsWhat is a Neural Network?Sample Neural Network CodeAnd so much more!If you aren’t a tech-savvy person or have no programming or machine learning experience, have no fear! With this guide in your hands that will not be a barrier for you any longer. Understand Machine Learning in Python quickly and easily when you grab this guide now!
Author: Leonard Lee