Beschreibung:
Book Contents - 1. Introduction to Machine Learning 2. Preparing to Model 3. Modelling and Evaluation 4. Basics of Feature Engineering 5. Brief Overview of Probability 6. Bayesian Concept Learning 7. Supervised Learning. Classification 8. Supervised Learning. Regression 9. Unsupervised Learning 10. Basics of Neural Network 11. Other Types of Learning Appendix A Programming Machine Learning in R Appendix B Programming Machine Learning in Python Appendix C A Case Study on Machine Learning Application. Grouping Similar Service Requests and Classifying a New One Model Question Paper-1 Model Question Paper-2 Model Question Paper-3.