000			nam a22 7a 4500
999			_c7834 _d7834
005			20181207154558.0
008			181207b xxu||||| |||| 00| 0 eng d
020			_a9789353160258
040			_cAIKTC-KRRC
041			_aENG
082			_2DDC23 _a006.31 _bGOP
100			_96890 _aGopal, M.
245			_aApplied machine learning
260			_aChennai _bMcGraw Hill Education _c2018
300			_axix, 630p _bPaperback _c24*18.5 cm
520			_aApplied Machine Learning textcovers all the fundamentals and theoretical concepts and presents a widerange of techniques (algorithms) applicable to challenges in our day-to-daylives. The book recognizes that most of the ideas behind machinelearning are simple and straightforward. It provides a platform for hands-onexperience through self-study machine learning projects. Datasets for somebenchmark applications have been explained to encourage the use of algorithmscovered in this book. This is a comprehensive textbook on machine learning for undergraduates in computer science and allengineering degree programs. Post graduates and research scholars will find ita useful initial exposure to the subject, before they go for highly theoreticaldepth in the specific areas of their research. For engineers, scientists, business managers and other practitioners,the book will help build the foundations of machine learning. _b CONTENT: 1. Introduction 2. Supervised Learning: Rationale and Basics 3. Statistical Learning 4. Learning With Support Vector Machines (SVM) 5. Learning With Neural Networks (NN) 6. Fuzzy Inference Systems 7. Data Clustering and Data Transformations 8. Decision Tree Learning 9. Business Intelligence and Data Mining: Techniques andApplications ? Appendix AGenetic Algorithm (GA) For Search Optimization ? Appendix BReinforcement Learning (RL) ? Datasets fromReal-Life Applications for Machine Learning Experiments
650		0	_94622 _aComputer Engineering
942			_2ddc _cBK