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Introduction to Jupyter notebook and MS Azure Machine Learning Studio

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Introduction to Jupyter notebook and MS Azure Machine Learning Studio

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Introduction to Jupyter notebook and MS Azure Machine Learning Studio

  1. 1. Introduction about Jupyter Notebook and Azure Machine Learning Studio Muralidharan Deenathayalan, Technical Architect, Quanticate 1
  2. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 16. How Jupyter works? Ref : https://en.wikipedia.org/wiki/IPython , https://en.wikipedia.org/wiki/Project_Jupyter#History 16
  17. 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. 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. 19. Installation of Jupyter Notebook • http://jupyter-notebook-beginner-guide.readthedocs.io/en/latest/install.html 19
  20. 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. 21. Sample Jupyter Notebook • A simple python code sample from Jupyter Notebook. 21
  22. 22. Sample Jupyter Notebook • Fetching data from Azure Machine Learning Studio to Jupyter Notebook. 22
  23. 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. 24. Microsoft Azure Machine Learning Studio • Navigate to https://studio.azureml.net/ (Sign- in, if not.) 24
  25. 25. Python and Azure ML 25
  26. 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
  27. 27. Python and Azure ML 27
  28. 28. Python and Azure ML Demo Demo 28
  29. 29. R and Azure ML 29
  30. 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. 31. R and Azure ML 31
  32. 32. R and Azure ML Demo Demo 32
  33. 33. Python, R and Azure ML 33
  34. 34. Q & A Q & A 34
  35. 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
  36. 36. Thanks Thank you ! 36

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