LEADER 04574nam 22006615 450 001 9910300756103321 005 20200704200345.0 010 $a9781484232071 010 $a1484232070 024 7 $a10.1007/978-1-4842-3207-1 035 $a(CKB)4100000001382183 035 $a(DE-He213)978-1-4842-3207-1 035 $a(MiAaPQ)EBC5210179 035 $a(CaSebORM)9781484232071 035 $a(PPN)22223119X 035 $a(OCoLC)1020493783 035 $a(OCoLC)on1020493783 035 $a(EXLCZ)994100000001382183 100 $a20171221d2018 u| 0 101 0 $aeng 135 $aurnn|008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aPractical Machine Learning with Python $eA Problem-Solver's Guide to Building Real-World Intelligent Systems /$fby Dipanjan Sarkar, Raghav Bali, Tushar Sharma 205 $a1st ed. 2018. 210 1$aBerkeley, CA :$cApress :$cImprint: Apress,$d2018. 215 $a1 online resource (XXV, 530 p. 273 illus., 209 illus. in color.) 300 $aIncludes index. 311 08$a9781484232064 311 08$a1484232062 327 $aChapter 1: Machine Learning Basics -- Chapter 2: The Python Machine Learning Ecosystem -- Chapter 3: Processing, Wrangling and Visualizing Data.-Chapter 4: Feature Engineering and Selection -- Chapter 5: Building, Tuning and Deploying Models.-Chapter 6: Analyzing Bike Sharing Trends -- Chapter 7: Analyzing Movie Reviews Sentiment -- Chapter 8: Customer Segmentation and Effective Cross Selling -- Chapter 9: Analyzing Wine Types and Quality -- Chapter 10: Analyzing Music Trends and Recommendations -- Chapter 11: Forecasting Stock and Commodity Prices -- Chapter 12: Deep Learning for Computer Vision. 330 $aMaster the essential skills needed to recognize and solve complex problems with machine learning and deep learning. Using real-world examples that leverage the popular Python machine learning ecosystem, this book is your perfect companion for learning the art and science of machine learning to become a successful practitioner. The concepts, techniques, tools, frameworks, and methodologies used in this book will teach you how to think, design, build, and execute machine learning systems and projects successfully. Practical Machine Learning with Python follows a structured and comprehensive three-tiered approach packed with hands-on examples and code. Part 1 focuses on understanding machine learning concepts and tools. Part 2 details standard machine learning pipelines, with an emphasis on data processing analysis, feature engineering, and modeling. Part 3 explores multiple real-world case studies spanning diverse domains and industries like retail, transportation, movies, music, marketing, computer vision and finance. Practical Machine Learning with Python will empower you to start solving your own problems with machine learning today! You will: Execute end-to-end machine learning projects and systems Implement hands-on examples with industry standard, open source, robust machine learning tools and frameworks Review case studies depicting applications of machine learning and deep learning on diverse domains and industries Apply a wide range of machine learning models including regression, classification, and clustering. 517 3 $aProblem-solver's guide to building real-world intelligent systems 606 $aArtificial intelligence 606 $aPython (Computer program language) 606 $aOpen source software 606 $aComputer programming 606 $aArtificial Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/I21000 606 $aPython$3https://scigraph.springernature.com/ontologies/product-market-codes/I29080 606 $aOpen Source$3https://scigraph.springernature.com/ontologies/product-market-codes/I29090 615 0$aArtificial intelligence. 615 0$aPython (Computer program language) 615 0$aOpen source software. 615 0$aComputer programming. 615 14$aArtificial Intelligence. 615 24$aPython. 615 24$aOpen Source. 676 $a006 700 $aSarkar$b Dipanjan$4aut$4http://id.loc.gov/vocabulary/relators/aut$0785722 702 $aBali$b Raghav$4aut$4http://id.loc.gov/vocabulary/relators/aut 702 $aSharma$b Tushar$4aut$4http://id.loc.gov/vocabulary/relators/aut 801 0$bUMI 801 1$bUMI 906 $aBOOK 912 $a9910300756103321 996 $aPractical Machine Learning with Python$92534274 997 $aUNINA