LEADER 03682nam 2200625Ia 450 001 9910662212203321 005 20200520144314.0 010 $a1-74224-142-5 010 $a1-74224-631-1 035 $a(CKB)2670000000341724 035 $a(EBL)1184831 035 $a(OCoLC)840119775 035 $a(SSID)ssj0001036754 035 $a(PQKBManifestationID)12468895 035 $a(PQKBTitleCode)TC0001036754 035 $a(PQKBWorkID)11042620 035 $a(PQKB)11166729 035 $a(MiAaPQ)EBC1184831 035 $a(MiAaPQ)EBC1154621 035 $a(Au-PeEL)EBL1184831 035 $a(CaPaEBR)ebr10675229 035 $a(Au-PeEL)EBL1154621 035 $a(OCoLC)831117829 035 $a(EXLCZ)992670000000341724 100 $a20130409d2013 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aAir disaster Canberra$b[electronic resource] $ethe plane crash that destroyed a government /$fAndrew Tink 205 $a1st ed. 210 $aSydney, NSW, Australia $cNew South$dc2013 215 $a1 online resource (458 p.) 300 $aDescription based upon print version of record. 311 $a1-74223-357-0 320 $aIncludes bibliographical references and index. 327 $aCover; Title Page; Copyright; Contents; Acknowledgments; Acronyms and abbreviations; Prologue; Part I: The political rise of the Anzac generation; 1. Nose first; 2. Some had fought; 3. Others hadn't; 4. Anzac generation into Parliament; 5. Menzies backs Lyons; 6. Fairbairn, Menzies and Street enter Federal Parliament; 7. Fairbairn, Gullett and Street back Menzies; 8. Australia's leadership malaise; 9. Menzies' resignation; 10. Menzies trumps Page; 11. Menzies PM; 12. Menzies' right-hand men; 13. The war cabinet; 14. Cincinnatus; 15. France falls; 16. The flying MP 327 $a17. Minister for civil aviation18. Minister for air; 19. Flight Lieutenant R.E. (Bob) Hitchcock; Part II: The air disaster; 20. The Lockheed Hudson; 21. Laverton; 22. Laverton to Essendon; 23. Essendon; 24. Essendon to eternity; 25. A dreadful calamity; 26. The Canberra inquests; 27. The air force inquiries; 28. The judicial inquiry: The players; 29. The judicial inquiry: The hearing; 30. The judicial inquiry: The findings; Part III: A wartime government destroyed; 31. The political fallout; 32. A hung Parliament; 33. Menzies goes to London; 34. Menzies digs in overseas 327 $a35. The prime ministerial stand in36. Menzies returns; 37. A political lynching; 38. Coles brings down the government; Epilogue; Notes; Bibliography; Index 330 $aIn August 1940 Australia had been at war for almost a year when a Hudson bomber - the A16-97 - carrying ten people, including three cabinet ministers, crashed into a ridge near Canberra. In the ghastly inferno that followed the crash, the nation lost its key war leaders. Over the next twelve months, it became clear that the passing of Geoffrey Street, Sir Henry Gullett and James Fairbairn had destabilized Robert Menzies' wartime government. As a direct but delayed consequence, John Curtin became prime minister in October 1941. Controversially, t 606 $aAircraft accidents$zAustralia$y1940$xPolitical aspects 607 $aAustralia$xPolitics and government$y1901-1945 608 $aElectronic books. 615 0$aAircraft accidents$xPolitical aspects. 676 $a320.994 676 $a994/.04/0924 700 $aTink$b Andrew$01114406 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910662212203321 996 $aAir disaster Canberra$92640944 997 $aUNINA LEADER 03537nam 2200541 450 001 9910677891003321 005 20220422110803.0 010 $a1-5231-3319-8 010 $a1-119-59157-0 010 $a1-119-59153-8 010 $a1-119-59154-6 035 $a(CKB)4100000010870980 035 $a(MiAaPQ)EBC6174019 035 $a(CaSebORM)9781119591511 035 $a(PPN)270072101 035 $a(EXLCZ)994100000010870980 100 $a20200730h20202020 uy 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aPractical machine learning in R /$fFred Nwanganga, Mike Chapple 210 1$aIndianapolis :$cJohn Wiley and Sons,$d[2020] 210 4$aŠ2020 215 $a1 online resource (466 pages) $cillustrations 300 $aIncludes index. 311 $a1-119-59151-1 330 $aGuides professionals and students through the rapidly growing field of machine learning with hands-on examples in the popular R programming language Machine learning?a branch of Artificial Intelligence (AI) which enables computers to improve their results and learn new approaches without explicit instructions?allows organizations to reveal patterns in their data and incorporate predictive analytics into their decision-making process. Practical Machine Learning in R provides a hands-on approach to solving business problems with intelligent, self-learning computer algorithms. Bestselling author and data analytics experts Fred Nwanganga and Mike Chapple explain what machine learning is, demonstrate its organizational benefits, and provide hands-on examples created in the R programming language. A perfect guide for professional self-taught learners or students in an introductory machine learning course, this reader-friendly book illustrates the numerous real-world business uses of machine learning approaches. Clear and detailed chapters cover data wrangling, R programming with the popular RStudio tool, classification and regression techniques, performance evaluation, and more. Explores data management techniques, including data collection, exploration and dimensionality reduction Covers unsupervised learning, where readers identify and summarize patterns using approaches such as apriori, eclat and clustering Describes the principles behind the Nearest Neighbor, Decision Tree and Naive Bayes classification techniques Explains how to evaluate and choose the right model, as well as how to improve model performance using ensemble methods such as Random Forest and XGBoost Practical Machine Learning in R is a must-have guide for business analysts, data scientists, and other professionals interested in leveraging the power of AI to solve business problems, as well as students and independent learners seeking to enter the field. 606 $aMachine learning 606 $aR (Computer program language) 606 $aAprenentatge automātic$2thub 606 $aR (Llenguatge de programaciķ)$2thub 608 $aLlibres electrōnics$2thub 615 0$aMachine learning. 615 0$aR (Computer program language) 615 7$aAprenentatge automātic 615 7$aR (Llenguatge de programaciķ) 676 $a617.9 700 $aNwanganga$b Fred Chukwuka$01345965 702 $aChapple$b Mike 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910677891003321 996 $aPractical machine learning in R$93071822 997 $aUNINA