{"product_id":"9789819811694","title":"MACHINE LEARNING IN BUSINESS FINANCE USING PYTHON","description":"\u003cp\u003eThis book is an introduction to machine learning using Python programming language with applications in finance and business. Coverages include the prediction methods of logistic regression, Naïve Bayes, k-Nearest Neighbor, Support Vector Machine, Random Forest, Gradient Boosting, and various types of Neural Networks. Performance measurements and assessments of feature importance are also explained. The book also contains detailed examples of the applications with data. Python codes are explained in a step-by-step manner using Jupyter Notebook so that the readers can practise on their own.\u003c\/p\u003e\u003cp\u003e\u003cb\u003eContents:\u003c\/b\u003e \u003c\/p\u003e\u003cul\u003e\n\u003cli\u003e\n\u003cb\u003e\u003ci\u003eFinancial Data Correlations:\u003c\/i\u003e\u003c\/b\u003e\u003cul\u003e\n\u003cli\u003ePortfolio Diversification\u003c\/li\u003e\n\u003cli\u003eWorked Example: Data\u003c\/li\u003e\n\u003cli\u003eForming Optimal Portfolios\u003c\/li\u003e\n\u003cli\u003eDenoising the Correlation Matrix\u003c\/li\u003e\n\u003cli\u003eUsing a More Accurate Forward Predictor\u003c\/li\u003e\n\u003cli\u003eConcluding Thoughts\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003c\/li\u003e\n\u003cli\u003e\n\u003cb\u003e\u003ci\u003eRegression and Regularization:\u003c\/i\u003e\u003c\/b\u003e\u003cul\u003e\n\u003cli\u003eRegression and Regularization\u003c\/li\u003e\n\u003cli\u003eWorked Example: Data\u003c\/li\u003e\n\u003cli\u003eLinear Regression Prediction\u003c\/li\u003e\n\u003cli\u003eFine-Tuning Hyperparameters\u003c\/li\u003e\n\u003cli\u003ePrediction Using Hold-Out Test Set\u003c\/li\u003e\n\u003cli\u003eCross-Validation\u003c\/li\u003e\n\u003cli\u003eConcluding Thoughts\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003c\/li\u003e\n\u003cli\u003e\n\u003cb\u003e\u003ci\u003eCorporate Reporting Data and Analyses:\u003c\/i\u003e\u003c\/b\u003e\u003cul\u003e\n\u003cli\u003eFinancial Statements\u003c\/li\u003e\n\u003cli\u003eBinary Classification Performance Metrics\u003c\/li\u003e\n\u003cli\u003eLogistic Regression\u003c\/li\u003e\n\u003cli\u003eWorked Example: Data\u003c\/li\u003e\n\u003cli\u003eDimension Reduction and Principal Component Analysis\u003c\/li\u003e\n\u003cli\u003eConcluding Thoughts\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003c\/li\u003e\n\u003cli\u003e\n\u003cb\u003e\u003ci\u003eNaïve Bayes, k-NN, and Support Vector Machines:\u003c\/i\u003e\u003c\/b\u003e\u003cul\u003e\n\u003cli\u003eNaïve Bayes\u003c\/li\u003e\n\u003cli\u003ek-Nearest Neighbors Algorithm\u003c\/li\u003e\n\u003cli\u003eSupport Vector Machine\u003c\/li\u003e\n\u003cli\u003eLagrange Duality Solution\u003c\/li\u003e\n\u003cli\u003eSupport Vector Regression\u003c\/li\u003e\n\u003cli\u003eWorked Examples: Data\u003c\/li\u003e\n\u003cli\u003eConcluding Thoughts\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003c\/li\u003e\n\u003cli\u003e\n\u003cb\u003e\u003ci\u003eDecision Trees, Random Forest, and Multi-Class Prediction:\u003c\/i\u003e\u003c\/b\u003e\u003cul\u003e\n\u003cli\u003eDecision Tree Method\u003c\/li\u003e\n\u003cli\u003eWorked Example DT: Data\u003c\/li\u003e\n\u003cli\u003eRandom Forest\u003c\/li\u003e\n\u003cli\u003eMulti-Class Classification\u003c\/li\u003e\n\u003cli\u003eConcluding Thoughts\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003c\/li\u003e\n\u003cli\u003e\n\u003cb\u003e\u003ci\u003eGradient Boosting, SHAP Values:\u003c\/i\u003e\u003c\/b\u003e\u003cul\u003e\n\u003cli\u003eGradient Boosting\u003c\/li\u003e\n\u003cli\u003ePopular Alternatives\u003c\/li\u003e\n\u003cli\u003eWorked Example GB: Data\u003c\/li\u003e\n\u003cli\u003eShapley and SHAP Values\u003c\/li\u003e\n\u003cli\u003eConcluding Thoughts\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003c\/li\u003e\n\u003cli\u003e\n\u003cb\u003e\u003ci\u003eArtificial Neural Network I:\u003c\/i\u003e\u003c\/b\u003e\u003cul\u003e\n\u003cli\u003eForward Propagation\u003c\/li\u003e\n\u003cli\u003eBackward Propagation\u003c\/li\u003e\n\u003cli\u003eWorked Example: Data\u003c\/li\u003e\n\u003cli\u003eImproving the NN and Understanding the Res\u003c\/li\u003e\n\u003cli\u003eConcluding Thoughts\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003c\/li\u003e\n\u003cli\u003e\n\u003cb\u003e\u003ci\u003eArtificial Neural Network II:\u003c\/i\u003e\u003c\/b\u003e\u003cul\u003e\n\u003cli\u003eRecurrent Neural Network\u003c\/li\u003e\n\u003cli\u003eWorked Example I: Data\u003c\/li\u003e\n\u003cli\u003eVariants of RNN\u003c\/li\u003e\n\u003cli\u003eWorked Example II: Data\u003c\/li\u003e\n\u003cli\u003eConcluding Thoughts\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003c\/li\u003e\n\u003c\/ul\u003e\u003cp\u003e\u003cb\u003eReadership:\u003c\/b\u003e For undergraduate and graduate students in Machine Learning and Algorithms, Quantitative Finance, Computational Finance, Machine Learning, and Business Finance, as well as general public readers who want to improve their general knowledge on Machine Learning.\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e","brand":"WSPC","offers":[{"title":"Default Title","offer_id":47801134055665,"sku":"9789819811694","price":39.0,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0674\/5433\/7265\/files\/9789819811694_p0.jpg?v=1770922986","url":"https:\/\/shop.barnesandnoble.com\/products\/9789819811694","provider":"Barnes \u0026 Noble","version":"1.0","type":"link"}