Data Preparation View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2015

AUTHORS

Roger Barga , Valentine Fontama , Wee Hyong Tok

ABSTRACT

Machine learning can feel magical. You provide Azure ML with training data, select an appropriate leaning algorithm, and it can learn patterns in that data. In many cases, the performance of the model that you build, if done correctly, will outperform a human expert. But, like so many problems in the world, there is a significant “garbage in, garbage out” aspect to machine learning. If the data you give it is rubbish, the learning algorithm is unlikely to be able to overcome it. Machine learning can’t perform “data alchemy” and turn data lead into gold; that’s why we practice good data science, and first clean and enhance the data so that the learning algorithm can do its magic. Done correctly, it’s the perfect collaboration between data scientist and machine learning algorithms. More... »

PAGES

45-79

Book

TITLE

Predictive Analytics with Microsoft Azure Machine Learning

ISBN

978-1-4842-1201-1
978-1-4842-1200-4

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-1-4842-1200-4_3

DOI

http://dx.doi.org/10.1007/978-1-4842-1200-4_3

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1034201578


Indexing Status Check whether this publication has been indexed by Scopus and Web Of Science using the SN Indexing Status Tool
Incoming Citations Browse incoming citations for this publication using opencitations.net

JSON-LD is the canonical representation for SciGraph data.

TIP: You can open this SciGraph record using an external JSON-LD service: JSON-LD Playground Google SDTT

[
  {
    "@context": "https://springernature.github.io/scigraph/jsonld/sgcontext.json", 
    "about": [
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/0801", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Artificial Intelligence and Image Processing", 
        "type": "DefinedTerm"
      }, 
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/08", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Information and Computing Sciences", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "familyName": "Barga", 
        "givenName": "Roger", 
        "type": "Person"
      }, 
      {
        "familyName": "Fontama", 
        "givenName": "Valentine", 
        "type": "Person"
      }, 
      {
        "familyName": "Tok", 
        "givenName": "Wee Hyong", 
        "type": "Person"
      }
    ], 
    "datePublished": "2015", 
    "datePublishedReg": "2015-01-01", 
    "description": "Machine learning can feel magical. You provide Azure ML with training data, select an appropriate leaning algorithm, and it can learn patterns in that data. In many cases, the performance of the model that you build, if done correctly, will outperform a human expert. But, like so many problems in the world, there is a significant \u201cgarbage in, garbage out\u201d aspect to machine learning. If the data you give it is rubbish, the learning algorithm is unlikely to be able to overcome it. Machine learning can\u2019t perform \u201cdata alchemy\u201d and turn data lead into gold; that\u2019s why we practice good data science, and first clean and enhance the data so that the learning algorithm can do its magic. Done correctly, it\u2019s the perfect collaboration between data scientist and machine learning algorithms.", 
    "genre": "chapter", 
    "id": "sg:pub.10.1007/978-1-4842-1200-4_3", 
    "inLanguage": [
      "en"
    ], 
    "isAccessibleForFree": false, 
    "isPartOf": {
      "isbn": [
        "978-1-4842-1201-1", 
        "978-1-4842-1200-4"
      ], 
      "name": "Predictive Analytics with Microsoft Azure Machine Learning", 
      "type": "Book"
    }, 
    "name": "Data Preparation", 
    "pagination": "45-79", 
    "productId": [
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1007/978-1-4842-1200-4_3"
        ]
      }, 
      {
        "name": "readcube_id", 
        "type": "PropertyValue", 
        "value": [
          "a2ecba4e5528ba2347513334596536e5a1e7b225d3a10928694235212ed67e1b"
        ]
      }, 
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1034201578"
        ]
      }
    ], 
    "publisher": {
      "location": "Berkeley, CA", 
      "name": "Apress", 
      "type": "Organisation"
    }, 
    "sameAs": [
      "https://doi.org/10.1007/978-1-4842-1200-4_3", 
      "https://app.dimensions.ai/details/publication/pub.1034201578"
    ], 
    "sdDataset": "chapters", 
    "sdDatePublished": "2019-04-15T15:09", 
    "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
    "sdPublisher": {
      "name": "Springer Nature - SN SciGraph project", 
      "type": "Organization"
    }, 
    "sdSource": "s3://com-uberresearch-data-dimensions-target-20181106-alternative/cleanup/v134/2549eaecd7973599484d7c17b260dba0a4ecb94b/merge/v9/a6c9fde33151104705d4d7ff012ea9563521a3ce/jats-lookup/v90/0000000001_0000000264/records_8672_00000058.jsonl", 
    "type": "Chapter", 
    "url": "http://link.springer.com/10.1007/978-1-4842-1200-4_3"
  }
]
 

Download the RDF metadata as:  json-ld nt turtle xml License info

HOW TO GET THIS DATA PROGRAMMATICALLY:

JSON-LD is a popular format for linked data which is fully compatible with JSON.

curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/pub.10.1007/978-1-4842-1200-4_3'

N-Triples is a line-based linked data format ideal for batch operations.

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1007/978-1-4842-1200-4_3'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/978-1-4842-1200-4_3'

RDF/XML is a standard XML format for linked data.

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/978-1-4842-1200-4_3'


 

This table displays all metadata directly associated to this object as RDF triples.

64 TRIPLES      21 PREDICATES      26 URIs      19 LITERALS      7 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1007/978-1-4842-1200-4_3 schema:about anzsrc-for:08
2 anzsrc-for:0801
3 schema:author N56f85166c5584d818a34b23227d123cf
4 schema:datePublished 2015
5 schema:datePublishedReg 2015-01-01
6 schema:description Machine learning can feel magical. You provide Azure ML with training data, select an appropriate leaning algorithm, and it can learn patterns in that data. In many cases, the performance of the model that you build, if done correctly, will outperform a human expert. But, like so many problems in the world, there is a significant “garbage in, garbage out” aspect to machine learning. If the data you give it is rubbish, the learning algorithm is unlikely to be able to overcome it. Machine learning can’t perform “data alchemy” and turn data lead into gold; that’s why we practice good data science, and first clean and enhance the data so that the learning algorithm can do its magic. Done correctly, it’s the perfect collaboration between data scientist and machine learning algorithms.
7 schema:genre chapter
8 schema:inLanguage en
9 schema:isAccessibleForFree false
10 schema:isPartOf Nd05ec7a2792a4006b8458174078ff4f5
11 schema:name Data Preparation
12 schema:pagination 45-79
13 schema:productId N1a69b83a64164ca8aa784c932aa1dc43
14 Nf134fbb917074fbf97b2992d547dc812
15 Nfb2292dfc8e24de79dd2471aad977994
16 schema:publisher Nd7789006e9354d4496de4a04f8679664
17 schema:sameAs https://app.dimensions.ai/details/publication/pub.1034201578
18 https://doi.org/10.1007/978-1-4842-1200-4_3
19 schema:sdDatePublished 2019-04-15T15:09
20 schema:sdLicense https://scigraph.springernature.com/explorer/license/
21 schema:sdPublisher Neb0f6533e72945809d103d6c60d4e398
22 schema:url http://link.springer.com/10.1007/978-1-4842-1200-4_3
23 sgo:license sg:explorer/license/
24 sgo:sdDataset chapters
25 rdf:type schema:Chapter
26 N10b0e6cf361e4a5c96c6cf100ae952b1 schema:familyName Fontama
27 schema:givenName Valentine
28 rdf:type schema:Person
29 N1a69b83a64164ca8aa784c932aa1dc43 schema:name dimensions_id
30 schema:value pub.1034201578
31 rdf:type schema:PropertyValue
32 N56f85166c5584d818a34b23227d123cf rdf:first Nfa5cf5f2bfe848cf9bda63b48c1ee55f
33 rdf:rest N8bc95a7498ba4dbc887867e9317eb792
34 N5806bfcdc6c342bea09ca65c521e89ad schema:familyName Tok
35 schema:givenName Wee Hyong
36 rdf:type schema:Person
37 N8bc95a7498ba4dbc887867e9317eb792 rdf:first N10b0e6cf361e4a5c96c6cf100ae952b1
38 rdf:rest Ne4319331f16246c2a2ed74ab2ec9cc54
39 Nd05ec7a2792a4006b8458174078ff4f5 schema:isbn 978-1-4842-1200-4
40 978-1-4842-1201-1
41 schema:name Predictive Analytics with Microsoft Azure Machine Learning
42 rdf:type schema:Book
43 Nd7789006e9354d4496de4a04f8679664 schema:location Berkeley, CA
44 schema:name Apress
45 rdf:type schema:Organisation
46 Ne4319331f16246c2a2ed74ab2ec9cc54 rdf:first N5806bfcdc6c342bea09ca65c521e89ad
47 rdf:rest rdf:nil
48 Neb0f6533e72945809d103d6c60d4e398 schema:name Springer Nature - SN SciGraph project
49 rdf:type schema:Organization
50 Nf134fbb917074fbf97b2992d547dc812 schema:name readcube_id
51 schema:value a2ecba4e5528ba2347513334596536e5a1e7b225d3a10928694235212ed67e1b
52 rdf:type schema:PropertyValue
53 Nfa5cf5f2bfe848cf9bda63b48c1ee55f schema:familyName Barga
54 schema:givenName Roger
55 rdf:type schema:Person
56 Nfb2292dfc8e24de79dd2471aad977994 schema:name doi
57 schema:value 10.1007/978-1-4842-1200-4_3
58 rdf:type schema:PropertyValue
59 anzsrc-for:08 schema:inDefinedTermSet anzsrc-for:
60 schema:name Information and Computing Sciences
61 rdf:type schema:DefinedTerm
62 anzsrc-for:0801 schema:inDefinedTermSet anzsrc-for:
63 schema:name Artificial Intelligence and Image Processing
64 rdf:type schema:DefinedTerm
 




Preview window. Press ESC to close (or click here)


...