Predictive Analysis Concept for Beginner
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Artificial intelligent Basic Concept
Predictive Analysis Concept for Beginner
- 2. 犖о鹸犖犖∇顕犖犖迦肩犖犖犢犖犢犖犖÷弦犖 犖犖犖犢犖犖о顕犖÷牽犖項犖犖朽犖犖橿犖犢犖犢犖犖犖迦牽犖犖橿犖迦犖犢犖迦 犖о鹸犖犖∇顕犖犖迦肩犖犖犢犖犢犖犖÷弦犖 犖犖謹犖犢犖犢犖犖犖迦牽犖犖犖 犖犖犖迦犖犖о顕犖÷犖犖朽犖∇硯犖犖迦犖犖迦犖犖朽検犖犖迦 犖犢犖迦犢 犖犖園犖о鹸犖犖∇顕犖犖迦牽犖犖犖÷犖巌硯犢犖犖犖犢 犖犖園犖犖犖巌犖犖迦肩犖犖犢犖犖犖劇賢犖犖園犖犖犖巌犖 犖犖項犢犖犖朽犖∇硯犖犖迦犢犖犖犖迦鍵犖犢犖迦犖犢犖迦犢
- 6. Machine Learning vs Optimization
- 7. Computer Language L a n g u a g e L e v e l s L o w - l e v e l l a n g u a g e V e r y f a s t s p e e d b u t ti m e c o n s u m i n g t o w r i t e H i g h - l e v e l l a n g u a g e A d o p t t o E n g l i s h l i k e s t r u c t u r e o f s t a t e m e n t s b u t r e q u i r e d t r a n s l a ti o n p r o c e s s C l o s e r t o s u e d b y h u m a n b e i n g C + + , J A V A , v i s u a l b a s i c L a n g u a g e T y p e s A l g o r i t h m : F o r t r a n , A L G O L , L I S P , C B u s i n e s s - o r i e n t e d : C O B O L , S Q L E d u c a ti o n - o r i e n t e d : B A S I C , P a s c a l , L o g o O b j e c t - o r i e n t e d : C + + , A d a , J a v a , V B D o c u m e n t F o r m a tti n g : T e X , P o s t s c r i p t , S G M L W W W d i s p l a y : H T M L , X M L
- 8. Language for AI Java, Python, LISP, Prolog and C++ Advantage Python: rich and extensive variety of library C++: fastest language Java: vert portable, appropriate for NLP LISP: suitable for ML Prolog: fast prototyping for released frequently Disadvantage Python: not suitable for mobile computing C++: poor in multitasking Java: less speed, still needs development LISP: few developer Prolog: not full standardized
- 9. Programming Types L o w - l e v e l l a n g u a g e V e r y f a s t s p e e d b u t ti m e c o n s u m i n g t o w r i t e H i g h - l e v e l l a n g u a g e A d o p t t o E n g l i s h l i k e s t r u c t u r e o f s t a t e m e n t s b u t r e q u i r e d t r a n s l a ti o n p r o c e s s C l o s e r t o s u e d b y h u m a n b e i n g C + + , J A V A , v i s u a l b a s i c
- 17. Regression 犖犢 犢犖犢犖 Machine Learning Support vector machine Naive Bayes Gradient boosting Classification trees / random forest
- 21. 犖犖÷賢犖犖犖犢犖犖犖朽権犖 犢犖犢犖犖園犖 Hidden Layer 犢犖犖劇犖犖÷犢犖 犖犖萎見犖о犖迦 input 犢犖ム鍵 output
- 22. EX.
- 23. Deep Learning 犖犖劇賢 ANN 犖犖朽犖÷元 Hidden Layer 犖犖ム顕犖∇犖園犖
- 25. 犖о鹸犖犖朽犖迦牽犖犖 犖犖迦 犖犖犖 Machine Learning 犢犖ム鍵 Deep Learning
- 27. Process Flow of ML application
- 28. ML Model Management MLOps is a set of practices for collaboration and communication between data scientists and operations professionals. It makes it easier to align models with business needs and regulatory requirements.
- 30. Azure Machine Leaning Designer
- 32. Q & A Useful Links: http://azure.microsoft.com/ - sign up for your trial https://studio.azureml.net/ - log into the studio https://gallery.azureml.net/ - check out the gallery
- 33. Q & A
Editor's Notes
- #7: 犖犖迦県犖迦犖犖÷犖巌硯犢犖犖犖犢 犖犖朽犖÷犖伍県犖∇犢犖犢犖犖劇犖犖犖迦牽 犖÷元犖犖犖犖犖萎犖園 犖犖劇賢 low level 犢犖犢犖犖犖迦県犖迦犖朽犢犖犢犖犖伍権犢犖犖犖萎犖園犖犖伍犖犖犖犢犢犖犖∇犖犢犖犖犖園肩犖犖項犖∇犖犖犖謹犖 犢犖ム鍵犖犖迦県犖 high level 犖犖萎検犖朽献犖園犖犖犖萎犖ム犖迦権犖犖ム原犖犖犖園犖犖迦県犖迦検犖犖伍県犖∇犖÷顕犖犖犖謹犖 犖犖∇犖迦犢犖犖犢犖犖迦検犖犖迦県犖迦牽犖項犢犖犖犖犖朽犖犖萎犖項犢犖犖ム犢犖犢犖犖犖迦県犖 low level 犢犖犖犖朽犖犖伍 犖犖園硯犖犖∇犖迦犖犖迦県犖迦犖犖犖迦牽犢犖犢犖犖迦犢犖犖犖犢犖迦犢 (犢犖犖劇犖犢犖犢犖犖項犖犖園犢犖犢犖犖項犖о犖迦検犖朽犖迦県犖迦見犖ム顕犖犖犖ム顕犖∇検犖迦 犢犖犖劇犖犢犖犢犖犖項犖о犖迦検犖犖伍県犖∇犖犖犢犖迦犖犖迦県犖迦犖犖犖迦牽犖犖劇犖犖犖迦牽犖犖ム賢犖犢犖о顕犖犖園犖犖巌犖犖犢犖迦犢犖犖犖犖犖園硯)
- #8: 犖犖迦県犖迦犖朽犢犖犢犢犖犖犖迦牽犖犖園犖犖迦犖園犖犖迦牽犖萎犖巌県犖犢 犢犖犢犖 python, C++, java, LISP 犢犖ム鍵 prolog 犢犖犢犖ム鍵犖犖迦県犖迦検犖朽犢犖犖犖朽犢犖犢犖犖朽権犖犢犖迦犖犖園 犢犖犖∇犖迦県犖迦犖犢犖ム鍵犖犖迦県犖迦犖犖巌犖犖謹犖犖犖迦検犖犢犖о犢犖о献犖迦犢犖迦犢 犖犖謹犢犖犖÷顕犖萎肩犖÷犖園犖犖迦牽犖犖犖萎権犖伍犖犢犢犖犢犖犖迦犖犢犖迦犢