LEADER 04149nam 22005895 450 001 9910369899903321 005 20201110133249.0 010 $a9781523150489 010 $a1523150483 010 $a9781484253168 010 $a1484253167 024 7 $a10.1007/978-1-4842-5316-8 035 $a(CKB)4100000009940199 035 $a(MiAaPQ)EBC5988135 035 $a(DE-He213)978-1-4842-5316-8 035 $a(CaSebORM)9781484253168 035 $a(PPN)242826539 035 $a(OCoLC)1140553237 035 $a(OCoLC)on1140553237 035 $a(EXLCZ)994100000009940199 100 $a20191130d2020 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aIndustrial Machine Learning $eUsing Artificial Intelligence as a Transformational Disruptor /$fby Andreas François Vermeulen 205 $a1st ed. 2020. 210 1$aBerkeley, CA :$cApress :$cImprint: Apress,$d2020. 215 $a1 online resource (652 pages) 311 08$a9781484253151 311 08$a1484253159 320 $aIncludes bibliographical references. 327 $aChapter 1: Introduction -- Chapter 2: Background Knowledge -- Chapter 3: Classic Machine Learning -- Chapter 4: Supervised Learning: Using labeled data for Insights -- Chapter 5: Supervised Learning: Advanced Algorithms -- Chapter 6: Unsupervised Learning: Using Unlabeled Data -- Chapter 7: Unsupervised Learning: Neural Network Toolkits -- Chapter 8: Unsupervised Learning: Deep Learning -- Chapter 9: Reinforcement Learning: Using Newly Gained Knowledge for Insights -- Chapter 10: Evolutionary Computing -- Chapter 11: Mechatronics -- Chapter 12: Robotics Revolution -- Chapter 13: Fourth Industrial Revolution (4IR ) -- Chapter 14: Industrialized Artificial Intelligence -- Chapter 15: Final Industrialization Project -- Appendix: Reference Material -- . 330 $aUnderstand the industrialization of machine learning (ML) and take the first steps toward identifying and generating the transformational disruptors of artificial intelligence (AI). You will learn to apply ML to data lakes in various industries, supplying data professionals with the advanced skills required to handle the future of data engineering and data science. Data lakes currently generated by worldwide industrialized business activities are projected to reach 35 zettabytes (ZB) as the Fourth Industrial Revolution produces an exponential increase of volume, velocity, variety, variability, veracity, visualization, and value. Industrialization of ML evolves from AI and studying pattern recognition against the increasingly unstructured resource stored in data lakes. Industrial Machine Learning supplies advanced, yet practical examples in different industries, including finance, public safety, health care, transportation, manufactory, supply chain, 3D printing, education, research, and data science. The book covers: supervised learning, unsupervised learning, reinforcement learning, evolutionary computing principles, soft robotics disruptors, and hard robotics disruptors. You will: Generate and identify transformational disruptors of artificial intelligence (AI) Understand the field of machine learning (ML) and apply it to handle big data and process the data lakes in your environment Hone the skills required to handle the future of data engineering and data science. 606 $aArtificial intelligence 606 $aBig data 606 $aArtificial Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/I21000 606 $aBig Data$3https://scigraph.springernature.com/ontologies/product-market-codes/I29120 615 0$aArtificial intelligence. 615 0$aBig data. 615 14$aArtificial Intelligence. 615 24$aBig Data. 676 $a006.3 700 $aVermeulen$b Andreas François$4aut$4http://id.loc.gov/vocabulary/relators/aut$0995399 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910369899903321 996 $aIndustrial Machine Learning$92280493 997 $aUNINA