SlideShare a Scribd company logo
1 of 36
Introduction about Jupyter
Notebook and Azure Machine
Learning Studio
Muralidharan Deenathayalan,
Technical Architect, Quanticate
1
What is Python?
• Python is an interpreted language.
• Python is an object-oriented, high-level programming language for general-purpose programming
• Created by Guido van Rossum and first released in 1991
2
Advantages of Python
• Extensive Support Libraries
• Integration Feature
• Improved Programmer’s Productivity
Ref : https://medium.com/@mindfiresolutions.usa/advantages-and-disadvantages-of-python-programming-language-fd0b394f2121
3
What is R ?
• R is a language and environment for statistical computing and graphics.
• It is a GNU project which is similar to the S language and environment which was developed at Bell
Laboratories (formerly AT&T, now Lucent Technologies) by John Chambers and colleagues.
• R can be considered as a different implementation of S
Ref : https://www.r-project.org/about.html
4
Advantages of R
• An effective data handling and storage facility.
• Suite of operators for calculations on arrays, in particular matrices.
• A large, coherent, integrated collection of intermediate tools for data analysis.
• Graphical facilities for data analysis and display either on-screen or on hardcopy.
• A well-developed, simple and effective programming language which includes conditionals, loops, user-
defined recursive functions and input and output facilities
Ref : https://www.r-project.org/about.html
5
What is Julia?
• Julia is a high-level, high-performance dynamic programming language for numerical computing.
• Julia provides a sophisticated compiler, distributed parallel execution, numerical accuracy, and an
extensive mathematical function library.
• Julia’s Base library, largely written in Julia itself.
• It integrates mature, best-of-breed open source C and Fortran libraries for linear algebra, random
number generation, signal processing, and string processing.
Ref :https://julialang.org/
6
Advantages of Julia
• Multiple dispatch: providing the ability to define function behaviour across many combinations of
argument types.
• Good performance, approaching that of statically-compiled languages like C
• Built-in package manager
• Call Python functions: use the PyCall package
• Call C functions directly: no wrappers or special APIs
Ref :https://julialang.org/
7
Limitations of Julia
• Not fully stabilized
• Lesser scientific tools
• Slower
Ref : https://www.allerin.com/blog/big-data-python-r-or-julia
8
What is iPython?
• iPython – Interactive Python command shell.
• It provides a rich toolkit to help you make the most of using Python interactively.
• Its main components are:
• A powerful interactive Python shell
• A Jupyter kernel to work with Python code in Jupyter notebooks and other interactive frontends.
Ref : https://ipython.readthedocs.io/en/stable/
9
Advantages of iPython
• Comprehensive object introspection.
• Input history, persistent across sessions.
• Caching of output results during a session with automatically generated references.
• Extensible tab completion, with support by default for completion of python variables and keywords,
filenames and function keywords.
• Extensible system of ‘magic’ commands for controlling the environment and performing many tasks
related to iPython or the operating system.
Ref : https://ipython.readthedocs.io/en/stable/
10
Limitations of iPython
• No native code session save.
• Unnatural keyboard shortcuts and no syntax debugger.
• Code cell allows lines that are too long and has no wrapping / autoindent.
• No easy drag and rearrange code cells.
• No table of content to show where html headers are.
• No easy hiding of code cells / code output.
Ref : https://www.quora.com/What-are-the-limitations-of-IPython-Notebook
11
What is Jupyter?
• Ju(lia) + Py(thon) + (e)R
• The Jupyter Notebook is an open-source web application that allows you to create and share documents.
• This document contain live code, equations, visualizations and narrative text.
Ref : https://www.oreilly.com/ideas/what-is-jupyter
12
Advantages of Jupyter?
• Useful for data cleaning and transformation, numerical simulation, statistical modelling, data
visualization, machine learning, and much more.
• Language of choice  40+ Languages
• Notebooks can be shared with others using email, Dropbox, GitHub and the Jupyter Notebook Viewer.
• Your code can produce rich, interactive output: HTML, images, videos, and custom MIME types.
• Big data integration - Leverage big data tools, such as Apache Spark, from Python, R and Scala. Explore
that same data with pandas, scikit-learn, ggplot2, TensorFlow.
Ref : http://jupyter.org/
13
Limitations of Jupyter
• It messes with your version control.
• The Jupyter Notebook format is just a big JSON, which contains your code and the outputs of the code
• Code can only be run in chunks.
Ref : http://opiateforthemass.es/articles/why-i-dont-like-jupyter-fka-ipython-notebook/
14
History of Jupyter & iPython
• Initial release : 2001; 17 years ago
• In 2014, Fernando Pérez announced a spin-off project from IPython called Project Jupyter.
• In 2015, GitHub and the Jupyter Project announced native rendering of Jupyter notebooks file format
(.ipynb files) on the GitHub platform.
Ref : https://en.wikipedia.org/wiki/IPython , https://en.wikipedia.org/wiki/Project_Jupyter#History
15
How Jupyter works?
Ref : https://en.wikipedia.org/wiki/IPython , https://en.wikipedia.org/wiki/Project_Jupyter#History
16
What is kernel in Jupyter?
• A notebook kernel is a “Computational Engine” that executes the code contained in a Notebook
document.
Ref : http://jupyter-notebook-beginner-guide.readthedocs.io/en/latest/what_is_jupyter.html
17
List of available Jupyter kernels
• There are 100+ kernels available (as of 22/11/2018)
• Interesting kernels are,
• IPyKernel
• IRKernel
• sas_kernel
• Ijava
• ICSharp
Ref : https://github.com/jupyter/jupyter/wiki/Jupyter-kernels
18
Installation of Jupyter Notebook
• http://jupyter-notebook-beginner-guide.readthedocs.io/en/latest/install.html
19
Jupyter Notebook on Cloud
• Navigate to https://notebooks.azure.com/
• Click Samples to navigate to https://notebooks.azure.com/Microsoft/libraries/samples
• Click anyone of the sample
• Click Clone option (You may get login dialog (if you’re not signed in, use your Hotmail/outlook/skype)
and login.)
• Enter library name and click Clone button
• Click on “Introduction to Python” sample and it launches, Jupyter notebook on Azure
• Select the statements on starts with In[1] … and select click Run button in the toolbar.
20
Sample Jupyter Notebook
• A simple python code sample from Jupyter Notebook.
21
Sample Jupyter Notebook
• Fetching data from Azure Machine Learning Studio to Jupyter Notebook.
22
What is Machine Learning(ML)?
• Machine Learning is about using the data you already have to make predictions.
• Machine Learning methods
Supervised machine learning algorithms
 Logistic Regression.
 Linear regression.
 Support vector machine (SVM)
Unsupervised machine learning algorithms
 K – means clustering
 Hierarchical clustering
 Hidden Markov models
Semi-supervised machine learning algorithms
Reinforcement machine learning algorithms
Ref : https://news.codecademy.com/what-is-machine-learning/, https://www.expertsystem.com/machine-learning-definition/ , http://dataaspirant.com/2014/09/19/supervised-and-
unsupervised-learning/ 23
Microsoft Azure Machine Learning Studio
• Navigate to https://studio.azureml.net/ (Sign- in, if not.)
24
Python and Azure ML
25
Python and Azure ML
import pandas as pd
def azureml_main(dataframe1):
for index, row in dataframe1.iterrows():
row[0]="Hello " + row[0] +"!"
# Return value must be of a sequence of pandas.DataFrame
return dataframe1
26
Python and Azure ML
27
Python and Azure ML Demo
Demo
28
R and Azure ML
29
R and Azure ML
dataset1 <- maml.mapInputPort(1)#class: data.frame
data.set <- data.frame(response=paste0("Hello ",dataset1$Names,"!"))
maml.mapOutputPort("data.set");
30
R and Azure ML
31
R and Azure ML Demo
Demo
32
Python, R and Azure ML
33
Q & A
Q & A
34
Keep in touch
Muralidharan Deenathayalan
Blogs : www.codingfreaks.net
Twitter : https://twitter.com/muralidharand
GitHub : https://github.com/muralidharand
LinkedIn : https://www.linkedin.com/in/muralidharand
35
Thanks
Thank you !
36

More Related Content

What's hot

Python Introduction | JNTUA | R19 | UNIT 1
Python Introduction | JNTUA | R19 | UNIT 1 Python Introduction | JNTUA | R19 | UNIT 1
Python Introduction | JNTUA | R19 | UNIT 1 FabMinds
 
A quick overview of why to use and how to set up iPython notebooks for research
A quick overview of why to use and how to set up iPython notebooks for researchA quick overview of why to use and how to set up iPython notebooks for research
A quick overview of why to use and how to set up iPython notebooks for researchAdam Pah
 
Getting started with Linux and Python by Caffe
Getting started with Linux and Python by CaffeGetting started with Linux and Python by Caffe
Getting started with Linux and Python by CaffeLihang Li
 
Introduction Jupyter Notebook
Introduction Jupyter NotebookIntroduction Jupyter Notebook
Introduction Jupyter Notebookthirumurugan133
 
Python for Science and Engineering: a presentation to A*STAR and the Singapor...
Python for Science and Engineering: a presentation to A*STAR and the Singapor...Python for Science and Engineering: a presentation to A*STAR and the Singapor...
Python for Science and Engineering: a presentation to A*STAR and the Singapor...pythoncharmers
 
11 Unit1 Chapter 1 Getting Started With Python
11   Unit1 Chapter 1 Getting Started With Python11   Unit1 Chapter 1 Getting Started With Python
11 Unit1 Chapter 1 Getting Started With PythonPraveen M Jigajinni
 
Programming with Python - Basic
Programming with Python - BasicProgramming with Python - Basic
Programming with Python - BasicMosky Liu
 
Python Libraries and Modules
Python Libraries and ModulesPython Libraries and Modules
Python Libraries and ModulesRaginiJain21
 
Advanced Python Tutorial | Learn Advanced Python Concepts | Python Programmin...
Advanced Python Tutorial | Learn Advanced Python Concepts | Python Programmin...Advanced Python Tutorial | Learn Advanced Python Concepts | Python Programmin...
Advanced Python Tutorial | Learn Advanced Python Concepts | Python Programmin...Edureka!
 
Python Programming Language
Python Programming LanguagePython Programming Language
Python Programming LanguageLaxman Puri
 
A Sneak Peek of MLIR in TensorFlow
A Sneak Peek of MLIR in TensorFlowA Sneak Peek of MLIR in TensorFlow
A Sneak Peek of MLIR in TensorFlowKoan-Sin Tan
 
Python Seminar PPT
Python Seminar PPTPython Seminar PPT
Python Seminar PPTShivam Gupta
 
Intro to Python for Non-Programmers
Intro to Python for Non-ProgrammersIntro to Python for Non-Programmers
Intro to Python for Non-ProgrammersAhmad Alhour
 

What's hot (19)

Python Introduction | JNTUA | R19 | UNIT 1
Python Introduction | JNTUA | R19 | UNIT 1 Python Introduction | JNTUA | R19 | UNIT 1
Python Introduction | JNTUA | R19 | UNIT 1
 
A quick overview of why to use and how to set up iPython notebooks for research
A quick overview of why to use and how to set up iPython notebooks for researchA quick overview of why to use and how to set up iPython notebooks for research
A quick overview of why to use and how to set up iPython notebooks for research
 
Getting started with Linux and Python by Caffe
Getting started with Linux and Python by CaffeGetting started with Linux and Python by Caffe
Getting started with Linux and Python by Caffe
 
Python - the basics
Python - the basicsPython - the basics
Python - the basics
 
Introduction Jupyter Notebook
Introduction Jupyter NotebookIntroduction Jupyter Notebook
Introduction Jupyter Notebook
 
Python for Science and Engineering: a presentation to A*STAR and the Singapor...
Python for Science and Engineering: a presentation to A*STAR and the Singapor...Python for Science and Engineering: a presentation to A*STAR and the Singapor...
Python for Science and Engineering: a presentation to A*STAR and the Singapor...
 
11 Unit1 Chapter 1 Getting Started With Python
11   Unit1 Chapter 1 Getting Started With Python11   Unit1 Chapter 1 Getting Started With Python
11 Unit1 Chapter 1 Getting Started With Python
 
Python Workshop
Python WorkshopPython Workshop
Python Workshop
 
Python final ppt
Python final pptPython final ppt
Python final ppt
 
Programming with Python - Basic
Programming with Python - BasicProgramming with Python - Basic
Programming with Python - Basic
 
Python Libraries and Modules
Python Libraries and ModulesPython Libraries and Modules
Python Libraries and Modules
 
Python tutorial
Python tutorialPython tutorial
Python tutorial
 
Python Workshop
Python WorkshopPython Workshop
Python Workshop
 
Advanced Python Tutorial | Learn Advanced Python Concepts | Python Programmin...
Advanced Python Tutorial | Learn Advanced Python Concepts | Python Programmin...Advanced Python Tutorial | Learn Advanced Python Concepts | Python Programmin...
Advanced Python Tutorial | Learn Advanced Python Concepts | Python Programmin...
 
Python Programming Language
Python Programming LanguagePython Programming Language
Python Programming Language
 
A Sneak Peek of MLIR in TensorFlow
A Sneak Peek of MLIR in TensorFlowA Sneak Peek of MLIR in TensorFlow
A Sneak Peek of MLIR in TensorFlow
 
Python Intro
Python IntroPython Intro
Python Intro
 
Python Seminar PPT
Python Seminar PPTPython Seminar PPT
Python Seminar PPT
 
Intro to Python for Non-Programmers
Intro to Python for Non-ProgrammersIntro to Python for Non-Programmers
Intro to Python for Non-Programmers
 

Similar to Introduction to Jupyter notebook and MS Azure Machine Learning Studio

Python workshop
Python workshopPython workshop
Python workshopShiraz LUG
 
Introduction to python history and platforms
Introduction to python history and platformsIntroduction to python history and platforms
Introduction to python history and platformsKirti Verma
 
Python programming ppt.pptx
Python programming ppt.pptxPython programming ppt.pptx
Python programming ppt.pptxnagendrasai12
 
Introduction to Python Programming Basics
Introduction  to  Python  Programming BasicsIntroduction  to  Python  Programming Basics
Introduction to Python Programming BasicsDhana malar
 
Python quick guide1
Python quick guide1Python quick guide1
Python quick guide1Kanchilug
 
Software Programming with Python II.pptx
Software Programming with Python II.pptxSoftware Programming with Python II.pptx
Software Programming with Python II.pptxGevitaChinnaiah
 
Introduction to Python Programming
Introduction to Python ProgrammingIntroduction to Python Programming
Introduction to Python ProgrammingAkhil Kaushik
 
Jupyter notebooks on steroids
Jupyter notebooks on steroidsJupyter notebooks on steroids
Jupyter notebooks on steroidsJose Enrique Ruiz
 
Exploring Five Lesser-Known Python Libraries
Exploring Five Lesser-Known Python LibrariesExploring Five Lesser-Known Python Libraries
Exploring Five Lesser-Known Python LibrariesMinhazulAbedin27
 
Apresentação - Minicurso de Introdução a Python, Data Science e Machine Learning
Apresentação - Minicurso de Introdução a Python, Data Science e Machine LearningApresentação - Minicurso de Introdução a Python, Data Science e Machine Learning
Apresentação - Minicurso de Introdução a Python, Data Science e Machine LearningArthur Emanuel
 
Python programming
Python programmingPython programming
Python programmingMegha V
 
Python_basics_tuples_sets_lists_control_loops.ppt
Python_basics_tuples_sets_lists_control_loops.pptPython_basics_tuples_sets_lists_control_loops.ppt
Python_basics_tuples_sets_lists_control_loops.pptVGaneshKarthikeyan
 
DEMO On PYTHON WEB Development.pptx
DEMO On PYTHON WEB Development.pptxDEMO On PYTHON WEB Development.pptx
DEMO On PYTHON WEB Development.pptxSHAIKIRFAN715544
 
Programming for data science in python
Programming for data science in pythonProgramming for data science in python
Programming for data science in pythonUmmeSalmaM1
 

Similar to Introduction to Jupyter notebook and MS Azure Machine Learning Studio (20)

Python workshop
Python workshopPython workshop
Python workshop
 
Python workshop
Python workshopPython workshop
Python workshop
 
What is python
What is pythonWhat is python
What is python
 
PYTHON UNIT 1
PYTHON UNIT 1PYTHON UNIT 1
PYTHON UNIT 1
 
Introduction to python history and platforms
Introduction to python history and platformsIntroduction to python history and platforms
Introduction to python history and platforms
 
Python programming ppt.pptx
Python programming ppt.pptxPython programming ppt.pptx
Python programming ppt.pptx
 
Introduction to Python Programming Basics
Introduction  to  Python  Programming BasicsIntroduction  to  Python  Programming Basics
Introduction to Python Programming Basics
 
IPT 2.pptx
IPT 2.pptxIPT 2.pptx
IPT 2.pptx
 
Python quick guide1
Python quick guide1Python quick guide1
Python quick guide1
 
Software Programming with Python II.pptx
Software Programming with Python II.pptxSoftware Programming with Python II.pptx
Software Programming with Python II.pptx
 
Introduction to Python Programming
Introduction to Python ProgrammingIntroduction to Python Programming
Introduction to Python Programming
 
Jupyter notebooks on steroids
Jupyter notebooks on steroidsJupyter notebooks on steroids
Jupyter notebooks on steroids
 
Exploring Five Lesser-Known Python Libraries
Exploring Five Lesser-Known Python LibrariesExploring Five Lesser-Known Python Libraries
Exploring Five Lesser-Known Python Libraries
 
Apresentação - Minicurso de Introdução a Python, Data Science e Machine Learning
Apresentação - Minicurso de Introdução a Python, Data Science e Machine LearningApresentação - Minicurso de Introdução a Python, Data Science e Machine Learning
Apresentação - Minicurso de Introdução a Python, Data Science e Machine Learning
 
Python programming
Python programmingPython programming
Python programming
 
hpcpp.pptx
hpcpp.pptxhpcpp.pptx
hpcpp.pptx
 
Python_basics_tuples_sets_lists_control_loops.ppt
Python_basics_tuples_sets_lists_control_loops.pptPython_basics_tuples_sets_lists_control_loops.ppt
Python_basics_tuples_sets_lists_control_loops.ppt
 
Top 10 python ide
Top 10 python ideTop 10 python ide
Top 10 python ide
 
DEMO On PYTHON WEB Development.pptx
DEMO On PYTHON WEB Development.pptxDEMO On PYTHON WEB Development.pptx
DEMO On PYTHON WEB Development.pptx
 
Programming for data science in python
Programming for data science in pythonProgramming for data science in python
Programming for data science in python
 

More from Muralidharan Deenathayalan (10)

What's new in C# 8.0 (beta)
What's new in C# 8.0 (beta)What's new in C# 8.0 (beta)
What's new in C# 8.0 (beta)
 
Alfresco 5.0 features
Alfresco 5.0 featuresAlfresco 5.0 features
Alfresco 5.0 features
 
Test drive on driven development process
Test drive on driven development processTest drive on driven development process
Test drive on driven development process
 
Map Reduce introduction
Map Reduce introductionMap Reduce introduction
Map Reduce introduction
 
Apache Hive - Introduction
Apache Hive - IntroductionApache Hive - Introduction
Apache Hive - Introduction
 
Apache cassandra
Apache cassandraApache cassandra
Apache cassandra
 
Alfresco share 4.1 to 4.2 customisation
Alfresco share 4.1 to 4.2 customisationAlfresco share 4.1 to 4.2 customisation
Alfresco share 4.1 to 4.2 customisation
 
Introduction about Alfresco webscript
Introduction about Alfresco webscriptIntroduction about Alfresco webscript
Introduction about Alfresco webscript
 
Alfresco activiti workflows
Alfresco activiti workflowsAlfresco activiti workflows
Alfresco activiti workflows
 
Alfresco content model
Alfresco content modelAlfresco content model
Alfresco content model
 

Recently uploaded

1111 ChatGPT Prompts PDF Free Download - Prompts for ChatGPT
1111 ChatGPT Prompts PDF Free Download - Prompts for ChatGPT1111 ChatGPT Prompts PDF Free Download - Prompts for ChatGPT
1111 ChatGPT Prompts PDF Free Download - Prompts for ChatGPTiSEO AI
 
Tales from a Passkey Provider Progress from Awareness to Implementation.pptx
Tales from a Passkey Provider  Progress from Awareness to Implementation.pptxTales from a Passkey Provider  Progress from Awareness to Implementation.pptx
Tales from a Passkey Provider Progress from Awareness to Implementation.pptxFIDO Alliance
 
Intro to Passkeys and the State of Passwordless.pptx
Intro to Passkeys and the State of Passwordless.pptxIntro to Passkeys and the State of Passwordless.pptx
Intro to Passkeys and the State of Passwordless.pptxFIDO Alliance
 
Event-Driven Architecture Masterclass: Challenges in Stream Processing
Event-Driven Architecture Masterclass: Challenges in Stream ProcessingEvent-Driven Architecture Masterclass: Challenges in Stream Processing
Event-Driven Architecture Masterclass: Challenges in Stream ProcessingScyllaDB
 
Design Guidelines for Passkeys 2024.pptx
Design Guidelines for Passkeys 2024.pptxDesign Guidelines for Passkeys 2024.pptx
Design Guidelines for Passkeys 2024.pptxFIDO Alliance
 
Oauth 2.0 Introduction and Flows with MuleSoft
Oauth 2.0 Introduction and Flows with MuleSoftOauth 2.0 Introduction and Flows with MuleSoft
Oauth 2.0 Introduction and Flows with MuleSoftshyamraj55
 
Collecting & Temporal Analysis of Behavioral Web Data - Tales From The Inside
Collecting & Temporal Analysis of Behavioral Web Data - Tales From The InsideCollecting & Temporal Analysis of Behavioral Web Data - Tales From The Inside
Collecting & Temporal Analysis of Behavioral Web Data - Tales From The InsideStefan Dietze
 
(Explainable) Data-Centric AI: what are you explaininhg, and to whom?
(Explainable) Data-Centric AI: what are you explaininhg, and to whom?(Explainable) Data-Centric AI: what are you explaininhg, and to whom?
(Explainable) Data-Centric AI: what are you explaininhg, and to whom?Paolo Missier
 
Portal Kombat : extension du réseau de propagande russe
Portal Kombat : extension du réseau de propagande russePortal Kombat : extension du réseau de propagande russe
Portal Kombat : extension du réseau de propagande russe中 央社
 
ADP Passwordless Journey Case Study.pptx
ADP Passwordless Journey Case Study.pptxADP Passwordless Journey Case Study.pptx
ADP Passwordless Journey Case Study.pptxFIDO Alliance
 
Google I/O Extended 2024 Warsaw
Google I/O Extended 2024 WarsawGoogle I/O Extended 2024 Warsaw
Google I/O Extended 2024 WarsawGDSC PJATK
 
Event-Driven Architecture Masterclass: Engineering a Robust, High-performance...
Event-Driven Architecture Masterclass: Engineering a Robust, High-performance...Event-Driven Architecture Masterclass: Engineering a Robust, High-performance...
Event-Driven Architecture Masterclass: Engineering a Robust, High-performance...ScyllaDB
 
ERP Contender Series: Acumatica vs. Sage Intacct
ERP Contender Series: Acumatica vs. Sage IntacctERP Contender Series: Acumatica vs. Sage Intacct
ERP Contender Series: Acumatica vs. Sage IntacctBrainSell Technologies
 
Continuing Bonds Through AI: A Hermeneutic Reflection on Thanabots
Continuing Bonds Through AI: A Hermeneutic Reflection on ThanabotsContinuing Bonds Through AI: A Hermeneutic Reflection on Thanabots
Continuing Bonds Through AI: A Hermeneutic Reflection on ThanabotsLeah Henrickson
 
Choosing the Right FDO Deployment Model for Your Application _ Geoffrey at In...
Choosing the Right FDO Deployment Model for Your Application _ Geoffrey at In...Choosing the Right FDO Deployment Model for Your Application _ Geoffrey at In...
Choosing the Right FDO Deployment Model for Your Application _ Geoffrey at In...FIDO Alliance
 
Introduction to FDO and How It works Applications _ Richard at FIDO Alliance.pdf
Introduction to FDO and How It works Applications _ Richard at FIDO Alliance.pdfIntroduction to FDO and How It works Applications _ Richard at FIDO Alliance.pdf
Introduction to FDO and How It works Applications _ Richard at FIDO Alliance.pdfFIDO Alliance
 
The Value of Certifying Products for FDO _ Paul at FIDO Alliance.pdf
The Value of Certifying Products for FDO _ Paul at FIDO Alliance.pdfThe Value of Certifying Products for FDO _ Paul at FIDO Alliance.pdf
The Value of Certifying Products for FDO _ Paul at FIDO Alliance.pdfFIDO Alliance
 
Microsoft CSP Briefing Pre-Engagement - Questionnaire
Microsoft CSP Briefing Pre-Engagement - QuestionnaireMicrosoft CSP Briefing Pre-Engagement - Questionnaire
Microsoft CSP Briefing Pre-Engagement - QuestionnaireExakis Nelite
 
Event-Driven Architecture Masterclass: Integrating Distributed Data Stores Ac...
Event-Driven Architecture Masterclass: Integrating Distributed Data Stores Ac...Event-Driven Architecture Masterclass: Integrating Distributed Data Stores Ac...
Event-Driven Architecture Masterclass: Integrating Distributed Data Stores Ac...ScyllaDB
 

Recently uploaded (20)

1111 ChatGPT Prompts PDF Free Download - Prompts for ChatGPT
1111 ChatGPT Prompts PDF Free Download - Prompts for ChatGPT1111 ChatGPT Prompts PDF Free Download - Prompts for ChatGPT
1111 ChatGPT Prompts PDF Free Download - Prompts for ChatGPT
 
Tales from a Passkey Provider Progress from Awareness to Implementation.pptx
Tales from a Passkey Provider  Progress from Awareness to Implementation.pptxTales from a Passkey Provider  Progress from Awareness to Implementation.pptx
Tales from a Passkey Provider Progress from Awareness to Implementation.pptx
 
Intro to Passkeys and the State of Passwordless.pptx
Intro to Passkeys and the State of Passwordless.pptxIntro to Passkeys and the State of Passwordless.pptx
Intro to Passkeys and the State of Passwordless.pptx
 
Event-Driven Architecture Masterclass: Challenges in Stream Processing
Event-Driven Architecture Masterclass: Challenges in Stream ProcessingEvent-Driven Architecture Masterclass: Challenges in Stream Processing
Event-Driven Architecture Masterclass: Challenges in Stream Processing
 
Design Guidelines for Passkeys 2024.pptx
Design Guidelines for Passkeys 2024.pptxDesign Guidelines for Passkeys 2024.pptx
Design Guidelines for Passkeys 2024.pptx
 
Oauth 2.0 Introduction and Flows with MuleSoft
Oauth 2.0 Introduction and Flows with MuleSoftOauth 2.0 Introduction and Flows with MuleSoft
Oauth 2.0 Introduction and Flows with MuleSoft
 
Collecting & Temporal Analysis of Behavioral Web Data - Tales From The Inside
Collecting & Temporal Analysis of Behavioral Web Data - Tales From The InsideCollecting & Temporal Analysis of Behavioral Web Data - Tales From The Inside
Collecting & Temporal Analysis of Behavioral Web Data - Tales From The Inside
 
Overview of Hyperledger Foundation
Overview of Hyperledger FoundationOverview of Hyperledger Foundation
Overview of Hyperledger Foundation
 
(Explainable) Data-Centric AI: what are you explaininhg, and to whom?
(Explainable) Data-Centric AI: what are you explaininhg, and to whom?(Explainable) Data-Centric AI: what are you explaininhg, and to whom?
(Explainable) Data-Centric AI: what are you explaininhg, and to whom?
 
Portal Kombat : extension du réseau de propagande russe
Portal Kombat : extension du réseau de propagande russePortal Kombat : extension du réseau de propagande russe
Portal Kombat : extension du réseau de propagande russe
 
ADP Passwordless Journey Case Study.pptx
ADP Passwordless Journey Case Study.pptxADP Passwordless Journey Case Study.pptx
ADP Passwordless Journey Case Study.pptx
 
Google I/O Extended 2024 Warsaw
Google I/O Extended 2024 WarsawGoogle I/O Extended 2024 Warsaw
Google I/O Extended 2024 Warsaw
 
Event-Driven Architecture Masterclass: Engineering a Robust, High-performance...
Event-Driven Architecture Masterclass: Engineering a Robust, High-performance...Event-Driven Architecture Masterclass: Engineering a Robust, High-performance...
Event-Driven Architecture Masterclass: Engineering a Robust, High-performance...
 
ERP Contender Series: Acumatica vs. Sage Intacct
ERP Contender Series: Acumatica vs. Sage IntacctERP Contender Series: Acumatica vs. Sage Intacct
ERP Contender Series: Acumatica vs. Sage Intacct
 
Continuing Bonds Through AI: A Hermeneutic Reflection on Thanabots
Continuing Bonds Through AI: A Hermeneutic Reflection on ThanabotsContinuing Bonds Through AI: A Hermeneutic Reflection on Thanabots
Continuing Bonds Through AI: A Hermeneutic Reflection on Thanabots
 
Choosing the Right FDO Deployment Model for Your Application _ Geoffrey at In...
Choosing the Right FDO Deployment Model for Your Application _ Geoffrey at In...Choosing the Right FDO Deployment Model for Your Application _ Geoffrey at In...
Choosing the Right FDO Deployment Model for Your Application _ Geoffrey at In...
 
Introduction to FDO and How It works Applications _ Richard at FIDO Alliance.pdf
Introduction to FDO and How It works Applications _ Richard at FIDO Alliance.pdfIntroduction to FDO and How It works Applications _ Richard at FIDO Alliance.pdf
Introduction to FDO and How It works Applications _ Richard at FIDO Alliance.pdf
 
The Value of Certifying Products for FDO _ Paul at FIDO Alliance.pdf
The Value of Certifying Products for FDO _ Paul at FIDO Alliance.pdfThe Value of Certifying Products for FDO _ Paul at FIDO Alliance.pdf
The Value of Certifying Products for FDO _ Paul at FIDO Alliance.pdf
 
Microsoft CSP Briefing Pre-Engagement - Questionnaire
Microsoft CSP Briefing Pre-Engagement - QuestionnaireMicrosoft CSP Briefing Pre-Engagement - Questionnaire
Microsoft CSP Briefing Pre-Engagement - Questionnaire
 
Event-Driven Architecture Masterclass: Integrating Distributed Data Stores Ac...
Event-Driven Architecture Masterclass: Integrating Distributed Data Stores Ac...Event-Driven Architecture Masterclass: Integrating Distributed Data Stores Ac...
Event-Driven Architecture Masterclass: Integrating Distributed Data Stores Ac...
 

Introduction to Jupyter notebook and MS Azure Machine Learning Studio

  • 1. Introduction about Jupyter Notebook and Azure Machine Learning Studio Muralidharan Deenathayalan, Technical Architect, Quanticate 1
  • 2. What is Python? • Python is an interpreted language. • Python is an object-oriented, high-level programming language for general-purpose programming • Created by Guido van Rossum and first released in 1991 2
  • 3. Advantages of Python • Extensive Support Libraries • Integration Feature • Improved Programmer’s Productivity Ref : https://medium.com/@mindfiresolutions.usa/advantages-and-disadvantages-of-python-programming-language-fd0b394f2121 3
  • 4. What is R ? • R is a language and environment for statistical computing and graphics. • It is a GNU project which is similar to the S language and environment which was developed at Bell Laboratories (formerly AT&T, now Lucent Technologies) by John Chambers and colleagues. • R can be considered as a different implementation of S Ref : https://www.r-project.org/about.html 4
  • 5. Advantages of R • An effective data handling and storage facility. • Suite of operators for calculations on arrays, in particular matrices. • A large, coherent, integrated collection of intermediate tools for data analysis. • Graphical facilities for data analysis and display either on-screen or on hardcopy. • A well-developed, simple and effective programming language which includes conditionals, loops, user- defined recursive functions and input and output facilities Ref : https://www.r-project.org/about.html 5
  • 6. What is Julia? • Julia is a high-level, high-performance dynamic programming language for numerical computing. • Julia provides a sophisticated compiler, distributed parallel execution, numerical accuracy, and an extensive mathematical function library. • Julia’s Base library, largely written in Julia itself. • It integrates mature, best-of-breed open source C and Fortran libraries for linear algebra, random number generation, signal processing, and string processing. Ref :https://julialang.org/ 6
  • 7. Advantages of Julia • Multiple dispatch: providing the ability to define function behaviour across many combinations of argument types. • Good performance, approaching that of statically-compiled languages like C • Built-in package manager • Call Python functions: use the PyCall package • Call C functions directly: no wrappers or special APIs Ref :https://julialang.org/ 7
  • 8. Limitations of Julia • Not fully stabilized • Lesser scientific tools • Slower Ref : https://www.allerin.com/blog/big-data-python-r-or-julia 8
  • 9. What is iPython? • iPython – Interactive Python command shell. • It provides a rich toolkit to help you make the most of using Python interactively. • Its main components are: • A powerful interactive Python shell • A Jupyter kernel to work with Python code in Jupyter notebooks and other interactive frontends. Ref : https://ipython.readthedocs.io/en/stable/ 9
  • 10. Advantages of iPython • Comprehensive object introspection. • Input history, persistent across sessions. • Caching of output results during a session with automatically generated references. • Extensible tab completion, with support by default for completion of python variables and keywords, filenames and function keywords. • Extensible system of ‘magic’ commands for controlling the environment and performing many tasks related to iPython or the operating system. Ref : https://ipython.readthedocs.io/en/stable/ 10
  • 11. Limitations of iPython • No native code session save. • Unnatural keyboard shortcuts and no syntax debugger. • Code cell allows lines that are too long and has no wrapping / autoindent. • No easy drag and rearrange code cells. • No table of content to show where html headers are. • No easy hiding of code cells / code output. Ref : https://www.quora.com/What-are-the-limitations-of-IPython-Notebook 11
  • 12. What is Jupyter? • Ju(lia) + Py(thon) + (e)R • The Jupyter Notebook is an open-source web application that allows you to create and share documents. • This document contain live code, equations, visualizations and narrative text. Ref : https://www.oreilly.com/ideas/what-is-jupyter 12
  • 13. Advantages of Jupyter? • Useful for data cleaning and transformation, numerical simulation, statistical modelling, data visualization, machine learning, and much more. • Language of choice  40+ Languages • Notebooks can be shared with others using email, Dropbox, GitHub and the Jupyter Notebook Viewer. • Your code can produce rich, interactive output: HTML, images, videos, and custom MIME types. • Big data integration - Leverage big data tools, such as Apache Spark, from Python, R and Scala. Explore that same data with pandas, scikit-learn, ggplot2, TensorFlow. Ref : http://jupyter.org/ 13
  • 14. Limitations of Jupyter • It messes with your version control. • The Jupyter Notebook format is just a big JSON, which contains your code and the outputs of the code • Code can only be run in chunks. Ref : http://opiateforthemass.es/articles/why-i-dont-like-jupyter-fka-ipython-notebook/ 14
  • 15. History of Jupyter & iPython • Initial release : 2001; 17 years ago • In 2014, Fernando Pérez announced a spin-off project from IPython called Project Jupyter. • In 2015, GitHub and the Jupyter Project announced native rendering of Jupyter notebooks file format (.ipynb files) on the GitHub platform. Ref : https://en.wikipedia.org/wiki/IPython , https://en.wikipedia.org/wiki/Project_Jupyter#History 15
  • 16. How Jupyter works? Ref : https://en.wikipedia.org/wiki/IPython , https://en.wikipedia.org/wiki/Project_Jupyter#History 16
  • 17. What is kernel in Jupyter? • A notebook kernel is a “Computational Engine” that executes the code contained in a Notebook document. Ref : http://jupyter-notebook-beginner-guide.readthedocs.io/en/latest/what_is_jupyter.html 17
  • 18. List of available Jupyter kernels • There are 100+ kernels available (as of 22/11/2018) • Interesting kernels are, • IPyKernel • IRKernel • sas_kernel • Ijava • ICSharp Ref : https://github.com/jupyter/jupyter/wiki/Jupyter-kernels 18
  • 19. Installation of Jupyter Notebook • http://jupyter-notebook-beginner-guide.readthedocs.io/en/latest/install.html 19
  • 20. Jupyter Notebook on Cloud • Navigate to https://notebooks.azure.com/ • Click Samples to navigate to https://notebooks.azure.com/Microsoft/libraries/samples • Click anyone of the sample • Click Clone option (You may get login dialog (if you’re not signed in, use your Hotmail/outlook/skype) and login.) • Enter library name and click Clone button • Click on “Introduction to Python” sample and it launches, Jupyter notebook on Azure • Select the statements on starts with In[1] … and select click Run button in the toolbar. 20
  • 21. Sample Jupyter Notebook • A simple python code sample from Jupyter Notebook. 21
  • 22. Sample Jupyter Notebook • Fetching data from Azure Machine Learning Studio to Jupyter Notebook. 22
  • 23. What is Machine Learning(ML)? • Machine Learning is about using the data you already have to make predictions. • Machine Learning methods Supervised machine learning algorithms  Logistic Regression.  Linear regression.  Support vector machine (SVM) Unsupervised machine learning algorithms  K – means clustering  Hierarchical clustering  Hidden Markov models Semi-supervised machine learning algorithms Reinforcement machine learning algorithms Ref : https://news.codecademy.com/what-is-machine-learning/, https://www.expertsystem.com/machine-learning-definition/ , http://dataaspirant.com/2014/09/19/supervised-and- unsupervised-learning/ 23
  • 24. Microsoft Azure Machine Learning Studio • Navigate to https://studio.azureml.net/ (Sign- in, if not.) 24
  • 26. Python and Azure ML import pandas as pd def azureml_main(dataframe1): for index, row in dataframe1.iterrows(): row[0]="Hello " + row[0] +"!" # Return value must be of a sequence of pandas.DataFrame return dataframe1 26
  • 28. Python and Azure ML Demo Demo 28
  • 29. R and Azure ML 29
  • 30. R and Azure ML dataset1 <- maml.mapInputPort(1)#class: data.frame data.set <- data.frame(response=paste0("Hello ",dataset1$Names,"!")) maml.mapOutputPort("data.set"); 30
  • 31. R and Azure ML 31
  • 32. R and Azure ML Demo Demo 32
  • 33. Python, R and Azure ML 33
  • 34. Q & A Q & A 34
  • 35. Keep in touch Muralidharan Deenathayalan Blogs : www.codingfreaks.net Twitter : https://twitter.com/muralidharand GitHub : https://github.com/muralidharand LinkedIn : https://www.linkedin.com/in/muralidharand 35