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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