LEADER 04266nam 22006015 450 001 9910369902203321 005 20200702232745.0 010 $a9781484253731 010 $a1484253736 024 7 $a10.1007/978-1-4842-5373-1 035 $a(CKB)4100000009844914 035 $a(DE-He213)978-1-4842-5373-1 035 $a(MiAaPQ)EBC5979665 035 $a(CaSebORM)9781484253731 035 $a(OCoLC)1139336072 035 $a(OCoLC)on1139336072 035 $a(EXLCZ)994100000009844914 100 $a20191116d2020 u| 0 101 0 $aeng 135 $aurnn|008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aHands-on Scikit-Learn for Machine Learning Applications $eData Science Fundamentals with Python /$fby David Paper 205 $a1st ed. 2020. 210 1$aBerkeley, CA :$cApress :$cImprint: Apress,$d2020. 215 $a1 online resource (XIII, 242 p. 33 illus.) 300 $aIncludes index. 311 08$a9781484253724 311 08$a1484253728 327 $a1. Introduction to Scikit-Learn -- 2. Classification from Simple Training Sets -- 3. Classification from Complex Training Sets -- 4. Predictive Modeling through Regression -- 5. Scikit-Learn Classifier Tuning from Simple Training Sets -- 6. Scikit-Learn Classifier Tuning from Complex Training Sets -- 7. Scikit-Learn RegressionTuning -- 8. Putting it All Together. 330 $aAspiring data science professionals can learn the Scikit-Learn library along with the fundamentals of machine learning with this book. The book combines the Anaconda Python distribution with the popular Scikit-Learn library to demonstrate a wide range of supervised and unsupervised machine learning algorithms. Care is taken to walk you through the principles of machine learning through clear examples written in Python that you can try out and experiment with at home on your own machine. All applied math and programming skills required to master the content are covered in this book. In-depth knowledge of object-oriented programming is not required as working and complete examples are provided and explained. Coding examples are in-depth and complex when necessary. They are also concise, accurate, and complete, and complement the machine learning concepts introduced. Working the examples helps to build the skills necessary to understand and apply complex machine learning algorithms. Hands-on Scikit-Learn for Machine Learning Applications is an excellent starting point for those pursuing a career in machine learning. Students of this book will learn the fundamentals that are a prerequisite to competency. Readers will be exposed to the Anaconda distribution of Python that is designed specifically for data science professionals, and will build skills in the popular Scikit-Learn library that underlies many machine learning applications in the world of Python. What You'll Learn Work with simple and complex datasets common to Scikit-Learn Manipulate data into vectors and matrices for algorithmic processing Become familiar with the Anaconda distribution used in data science Apply machine learning with Classifiers, Regressors, and Dimensionality Reduction Tune algorithms and find the best algorithms for each dataset Load data from and save to CSV, JSON, Numpy, and Pandas formats. 606 $aMachine learning 606 $aPython (Computer program language) 606 $aBig data 606 $aMachine Learning$3https://scigraph.springernature.com/ontologies/product-market-codes/I21010 606 $aPython$3https://scigraph.springernature.com/ontologies/product-market-codes/I29080 606 $aBig Data$3https://scigraph.springernature.com/ontologies/product-market-codes/I29120 615 0$aMachine learning. 615 0$aPython (Computer program language) 615 0$aBig data. 615 14$aMachine Learning. 615 24$aPython. 615 24$aBig Data. 676 $a006.31 700 $aPaper$b David$4aut$4http://id.loc.gov/vocabulary/relators/aut$0995402 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910369902203321 996 $aHands-on Scikit-Learn for Machine Learning Applications$92280497 997 $aUNINA