This document provides an introduction to Jupyter Notebook and Azure Machine Learning Studio. It discusses popular programming languages like Python, R, and Julia that can be used with these tools. It also summarizes key features of Jupyter Notebook like code cells, kernels, and cloud deployment. Examples are given of using Python and R with Azure ML to fetch and transform data in Jupyter notebooks.

1. Introduction about Jupyter
Notebook and Azure Machine
Learning Studio
Muralidharan Deenathayalan,
Technical Architect, Quanticate
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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
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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
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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
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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
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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/
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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/
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8. Limitations of Julia
• Not fully stabilized
• Lesser scientific tools
• Slower
Ref : https://www.allerin.com/blog/big-data-python-r-or-julia
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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/
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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/
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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
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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
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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/
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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/
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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
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16. How Jupyter works?
Ref : https://en.wikipedia.org/wiki/IPython , https://en.wikipedia.org/wiki/Project_Jupyter#History
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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
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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
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19. Installation of Jupyter Notebook
• http://jupyter-notebook-beginner-guide.readthedocs.io/en/latest/install.html
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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.
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21. Sample Jupyter Notebook
• A simple python code sample from Jupyter Notebook.
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22. Sample Jupyter Notebook
• Fetching data from Azure Machine Learning Studio to Jupyter Notebook.
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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.)
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25. Python and Azure ML
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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
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27. Python and Azure ML
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28. Python and Azure ML Demo
Demo
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29. R and Azure ML
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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");
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31. R and Azure ML
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32. R and Azure ML Demo
Demo
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33. Python, R and Azure ML
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34. Q & A
Q & A
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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
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36. Thanks
Thank you !
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