Upgrade to Pro
— share decks privately, control downloads, hide ads and more …
Speaker Deck
Features
Speaker Deck
PRO
Sign in
Sign up for free
Search
Search
Data Science BOOTCAMP Practices
Search
Yohei Munesada
April 28, 2017
Science
420
0
Share
Embed
Copy iframe code
Copy JS code
Copy link
Start on current slide
Data Science BOOTCAMP Practices
データサイエンス・機械学習の演習説明です。
http://www.sompo.io/bootcamp/
Yohei Munesada
April 28, 2017
More Decks by Yohei Munesada
See All by Yohei Munesada
G'sデータベース設計の講義
yoheimune
4
5.5k
How to create a service, How to google !
yoheimune
0
350
Machine Learning Basic and Python
yoheimune
1
560
Python Scraping and Web Apps for G's ACADEMY TOKYO
yoheimune
0
270
DevelopWorkflow and Solving Problems
yoheimune
0
500
Git and Github for Beginners
yoheimune
1
330
Data Science BOOTCAMP Practices - Recommendation
yoheimune
0
260
Machine Learning with Python
yoheimune
0
410
Python Basics for G's ACADEMY TOKYO
yoheimune
1
680
Other Decks in Science
See All in Science
Bリーグのショットデータを活用した得点期待値モデルの構築 / Construction of expected points model using shot data of B.LEAGUE
konakalab
0
210
人生を変えた一冊「独学大全」のはなし / Self-study ENCYCLOPEDIA: The Book Which Change My Life #独学大全 #EM推し本
expajp
0
200
AI bij literatuuronderzoek in de wetenschap
voginip
0
240
Physical AIを支えるWeights & Biases
olachinkei
1
540
俺たちは本当に分かり合えるのか? ~ PdMとスクラムチームの “ずれ” を科学する
bonotake
4
2.7k
Massey Ratings for Match Outcome Prediction in Table Tennis: Evidence of Greater Stability than the ITTF World Ranking
konakalab
0
130
O(log n)-Approximation Algorithms for Bipartiteness Ratio
tasusu
0
160
大黒市で発生した大規模インシデント の ポストモーテムから読み解く、 記憶媒体消去の大切さ
shucho0103
0
250
Inside the Mind of an LLM
baggiponte
0
230
データベース10: 拡張実体関連モデル
trycycle
PRO
0
1.6k
機械学習 - 決定木からはじめる機械学習
trycycle
PRO
0
1.6k
機械学習 - K近傍法 & 機械学習のお作法
trycycle
PRO
1
1.6k
Featured
See All Featured
Making Projects Easy
brettharned
120
6.7k
Navigating Weather and Climate Data
rabernat
0
500
Why Your Marketing Sucks and What You Can Do About It - Sophie Logan
marketingsoph
0
390
How to Think Like a Performance Engineer
csswizardry
28
2.8k
Responsive Adventures: Dirty Tricks From The Dark Corners of Front-End
smashingmag
254
22k
The Art of Delivering Value - GDevCon NA Keynote
reverentgeek
16
2.1k
Scaling GitHub
holman
464
140k
Crafting Experiences
bethany
1
270
Java REST API Framework Comparison - PWX 2021
mraible
34
9.7k
XXLCSS - How to scale CSS and keep your sanity
sugarenia
249
1.3M
I Don’t Have Time: Getting Over the Fear to Launch Your Podcast
jcasabona
35
2.8k
The Power of CSS Pseudo Elements
geoffreycrofte
82
6.5k
Transcript
Data Science BOOTCAMP ΞϓϦέʔγϣϯ੍࡞ԋश Yohei Munesada
About Me 㾎फఆ༸ฏ ΉͶͩ͞Α͏͍ 㾎 ג αΠόʔΤʔδΣϯτ 㾎(`TΞΧσϛʔϝϯλʔ 㾎IUUQXXXZPIFJNOFU 㾎ͱσʔλαΠΤϯε
िؒɺΈͳ͞·͍͔͕Ͱͨ͠Ͱ͠ΐ͏͔ʁ
May think as … 㾎ֶతͳجૅΛड͚͖ͯͨɻ 㾎Ӭా͞ΜߨٛͰ৭ʑͱख๏ΛֶΜͰ͖ͨɻ 㾎ߨٛதͷԋशΛղ͍͚ͨͲɺͬͱ͍ͯ͠Δͱ͜Ζ͋Δɻ 㾎੍࡞ԋशΛ௨ͯ͠ɺʹ͚͍ͨͱ͜Ζʂ
May think as … ͦ͏ͩʂԿ͔࡞ͬͯΈΑ͏ʂ
Exercises .PWJF-FOTΛ༻͍ͨϨίϝϯσʔγϣϯͷߏங ࠃௐࠪσʔλΛ༻͍ͨσʔλαΠΤϯε ҙͷެ։σʔλΛ༻͍ͨػցֶश ػցֶशܥΫϥυ"1*Λ༻͍ͨαʔϏε։ൃ ඞਢ՝
બ՝
Objective ՌΛग़͢͜ͱ ϑϩʔʹԊͬͨ࡞ۀεςοϓΛ౿Ή͜ͱ
ϑϩʔʹԊͬͨ࡞ۀ
How to ԋशʹऔΓΉͷݸਓͰ ൃදάϧʔϓͰ
Schedule .PWJF-FOTΛ༻͍ͨϨίϝϯσʔγϣϯͷൃද 5VF ϫʔΫ࣭࣌ؒٙԠλΠϜ 8FE ҙ՝ͷൃද 'SJ
Exercises - MovieLens .PWJF-FOTΛ༻͍ͨϨίϝϯσʔγϣϯͷߏங ඞਢ՝ .PWJF-FOTͱ͍͏ެ։σʔλʹɺөըͷใɺϢʔβʔͷөըʹର͢Δใ ͳͲؚ͕·Ε·͢ɻͦΕΒσʔλΛ༻͍ͯϨίϝϯυγεςϜΛߏங͍ͯͩ͘͠͞ɻ ٻΊΔΞτϓοτ ɹɾϢʔβʔʹରͯ͠өըΛਪન͢Δ
ϙΠϯτ ɹɾਪનʹ͍ͭͯͲͷΑ͏ʹػցֶशͱͯ͠ఆٛ͢Δ͔ʁ ɹɾͳͥͦͷϞσϧΛબ͢Δͷ͔ʁ ɹɾ༧ଌ݁ՌͷධՁ݁ՌʁͲͷΑ͏ʹධՁ͢Εྑ͍͔ʁ
Exercises - MovieLens ར༻Մೳͳσʔλ ɹIUUQTHSPVQMFOTPSHEBUBTFUTNPWJFMFOT .PWJF-FOTΛ༻͍ͨϨίϝϯσʔγϣϯͷߏங ඞਢ՝
Exercises - MovieLens
Exercises - ࠃௐࠪ ࠃௐࠪσʔλΛ༻͍ͨσʔλαΠΤϯε બ՝ ࠃௐࠪσʔλ͔ΒਓޱɺՈߏɺ৬ۀͳͲ༷ʑͳใΛಘΔ͜ͱ͕Ͱ͖·͢ɻ ԿΒ͔ͷϏδωε՝Λఆٛͨ͠ͷͪʹɺࠃௐࠪσʔλΛ༻͍ͯϏδωεͷ ҙࢥܾఆΛॿ͚ΔใΛఏ͍ࣔͯͩ͘͠͞ɻ ٻΊΔΞτϓοτ
ɹɾఆٛͨ͠Ϗδωε՝Կ͔ʁ ɹɾͦΕʹରͯ͠ࠃௐࠪσʔλΛͲͷΑ͏ʹ׆༻͔ͨ͠ʁ Ϗδωε՝ྫ ɹɾ*5ڭҭϏδωεΛల։͍ͨ͠ɻͲͷࢢொଜΛλʔήοτʹ͢Δ͖͔ʁ ɹɾϑΟϦϐϯਓʹ͚ͨΧϑΣϏδωεΛߦ͍͍ͨɻͲ͜ͰΔ͔ʁ ɹɾͳͲ
ར༻Մೳͳσʔλ ɹIUUQXXXTUBUHPKQEBUBLPLVTFJJOEFYIUN Exercises - ࠃௐࠪ ࠃௐࠪσʔλΛ༻͍ͨσʔλαΠΤϯε બ՝
Exercises - ࠃௐࠪ
Exercises - ҙͷσʔλͰʂ ҙͷެ։σʔλΛ༻͍ͨػցֶश બ՝ ੈͷதʹ༷ʑͳσʔλ͕ެ։͞Ε͓ͯΓɺػցֶशʹར༻Ͱ͖Δσʔλ ଟʑଘࡏ͠·͢ɻڵຯͷ͋Δσʔλʹ͍ͭͯԾઆΛఆٛͯ͠ػցֶशΛߦ͍ɺ ԿΒ͔ͷՌΛग़͢औΓΈΛ͍ͯͩ͘͠͞ɻ ٻΊΔΞτϓοτ
ɹɾͲͷΑ͏ͳσʔλΛ͏͔ʁ ɹɾͲΜͳԾઆΛઃఆ͔ͨ͠ʁ ɹɾͲͷΑ͏ͳՌΛಋ͍ͨͷ͔ʁ·ͨͦΕΛͲͷΑ͏ʹಋ͍ͨͷ͔ʁ
ར༻Մೳͳσʔλྫ ɹ6$*.BDIJOF-FBSOJOH ɹɹIUUQBSDIJWFJDTVDJFEVNM ɹࠃཱใֶݚڀॴ ɹɹIUUQXXXOJJBDKQETDJESEBUBMJTUIUNM ɹ%"5"(0+1 ɹɹIUUQXXXEBUBHPKQ ɹ*NBHF/FU ɹɹIUUQXXXJNBHFOFUPSH Exercises
- ҙͷσʔλͰʂ ɹ,BHHMF ɹɹIUUQTXXXLBHHMFDPNEBUBTFUT ɹ-JWFEPPSχϡʔε ɹɹIUUQOFXTMJWFEPPSDPN ɹ౦ژϝτϩΦʔϓϯσʔλ ɹɹIUUQTEFWFMPQFSUPLZPNFUSPBQQKQJOGP ɹ5XJUUFS"1*ɺͳͲ ҙͷެ։σʔλΛ༻͍ͨػցֶश બ՝
Exercises - ҙͷσʔλͰʂ
Exercises - ػցֶशAPIΛͬͯʂ ػցֶशܥΫϥυ"1*Λ༻͍ͨαʔϏε։ൃ બ՝ (PPHMF"84"[VSF#JOH*#.ͷ֤αʔϏεͰػցֶशܥͷ"1*͕ ఏڙ͞Ε͍ͯΔʢྫɿإೝࣝɺԻೝࣝɺςΩετUPεϐʔνɺFUDʣɻ ͜ΕΒͷ"1*Λ͍ɺԿΒཱ͔ͪͦ͏ͳΞϓϦαʔϏεΛ੍࡞͍ͯͩ͘͠͞ɻ ٻΊΔΞτϓοτ
ɹɾͲͷ"1*Λར༻͢Δͷ͔ʁ ɹɾԿʹཱͯΔͷ͔ʁͲͷΑ͏ͳαʔϏε͔ʁ ग़ҙਤ ɹɾֶशࡁΈͷϞσϧΛͲͷΑ͏ʹ࣮ੈքͰ׆͔͢ͷ͔ɺͦΕΛߟ͑ߦಈ͢Δɻ
Exercises - ػցֶशAPIΛͬͯʂ
Exercises બ՝͕͔͔࣌ؒΓ·͢ͷͰɺ ͓ૣΊʹʂ .PWJF-FOTΛ༻͍ͨϨίϝϯσʔγϣϯͷߏங ࠃௐࠪσʔλΛ༻͍ͨσʔλαΠΤϯε ҙͷެ։σʔλΛ༻͍ͨػցֶश
ػցֶशܥΫϥυ"1*Λ༻͍ͨαʔϏε։ൃ ඞਢ՝ બ՝
Q and A ࣭ٙԠλΠϜ
Team Building άϧʔϓ͚Λ͠·͢ ʢʙਓఔʣ
Team Building ࣗݾհͱσΟεΧογϣϯ
Thank you ͦΕͰྑ͍σʔλαΠΤϯεΛʂ