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Azure machine learning indiandotnet

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Azure Machine Learning 101
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Azure machine learning indiandotnet

  2. 2. Agenda  What is Machine Learning ?  How does Machine Learning Help us ?  Different Type of Machine Learning  Different techniques or Algorithm to Solve Problem ?  Azure Machine Learning  Hands on Azure ML Studio
  3. 3. Machine Learning  “A breakthrough in machine learning would be worth ten Microsoft” - Bill Gates, Chairman, Microsoft  “Machine learning is the next Internet” - Tony Tether, former director , DARPA  “Machine learning is the hot new thing” - John Hennessy, President, Stanford  “Web rankings today are mostly a matter of machine learning” Prabhakar Raghavan, former Dir. Research, Yahoo  “Machine learning is going to result in a real revolution” – Greg Papadopoulos, former CTO, Sun
  4. 4. What is Machine Learning? Machine learning is a way to understand the data pattern , recognize it and predict accordingly for future. It helps in Data Mining, Language Processing, Image recognition and many other Artificial Intelligence And below is from Wikipedia Tom M. Mitchell provided a widely quoted, more formal definition: "A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P if its performance at tasks in T, as measured by P, improves with experience E."
  5. 5. How does Machine Learning Help us ?  The United States Postal Service processed over 150 billion pieces of mail in 2013—far too much for efficient human sorting.  But as recently as 1997, only 10% of hand-addressed mail was successfully sorted automatically.
  6. 6. Aap likhe khuda vache – Different hand writing Biggest challenge as handwriting may vary person to person from worst to best or best to worst.
  7. 7. How does Machine Learning Help ? Credit card Fraud detection Face detection Search recommendation
  8. 8. Weather forecasting
  9. 9. Machine Learning Although, Machine learning Is more than this. Here, we are showing some more example where machine learning can help  Determine SPAM emails  Provide customer like to switch to competitor  Free text when typing  Xbox gaming And many more examples.
  10. 10. “ ” Types of Machine Learning
  11. 11. Supervised Learning  The Supervised learning means the value you want to predict is already exist in training data. Means the data already exist in the computer so data is labeled. The accuracy is high in such case.  Used when you want to find unknown answers and have data with known answers
  12. 12. Car
  13. 13. Hey I am a car ?
  14. 14. Unsupervised Machine Learning  It is just opposite to Supervised Machine Learning. In this the predictive data not present in training data.  It is Used when you want to find unknown answers – mostly groupings – directly from data  No simple way to evaluate accuracy
  15. 15. “ ” Algorithm Class Different techniques for solving the problem
  16. 16. classification Binary classification (Two Class ) – yes/no, 1/0, male/female
  17. 17. Regression  Predict a real value – temperature, stock value...
  18. 18. Anomaly detection
  19. 19. Clustering  Partition items into group – twitter posts
  20. 20. “ ” Azure Machine Learning
  21. 21. Azure Machine Learning  Newest Azure service which reduces complexity of Machine learning  process and brings ML to broader audience  Possibility to develop and put into production ML models without  writing line of code.  Availability of many top of the class machine learning algorithms  (internally used at other Microsoft products)  Easy ML model deployment and usage using restful API  Easy collaboration on Azure Machine Learning projects  Support for open source framework R
  22. 22. Work Flow
  23. 23. “ ” First Look Of Azure Machine Learning Studio
  24. 24. Thank you Rajat Jaiswal (Microsoft MVP)

Notas del editor

  • Machine learning is not new in the market but nowadays  it is a buzz word everywhere. You might realize that there are lots of things happening in the Machine Learning.
    Many big companies like Microsoft, Oracle, IBM,SAP and many other working in this area. They have provided Azure Machine Learning,Oracle Advanced Analytics, IBM SPS, SAP Predictive Analysis tools to work on it.
  • Machine learning is the science of getting computers to act without being explicitly programmed
  • Why we need Machine Learning…a little context.

    In year 2015 the United States Postal Service processed 154.2 billion pieces of mail – far to much for efficient human sorting, but as recently as 1997, only 10% of all the hand-addressed mail was sorted automatically. Why?

  • Because this is a tough problem – the type of problem machine learning is designed to solve. It has taken so many years to automate the sorting of the mail because reading handwriting is hard due to all the variables involved. Even humans have trouble reading other humans’ handwriting, if you can imagine the thousands of ways someone can write a name or address, this is a huge machine learning problem to solve. How can we teach the machine to read the mail and how can the machine learn and get better over time?

  • It really comes down to Predictive Analytics, using your past data to provide data intelligence about the future. We’ve mentioned a few real world scenarios but there are many more.
    Fraud detection to flag orders or behaviors which are indicative of a scam and help you stay one step ahead of criminals.
    Face detection & expression detection service Cognitive service
  • Seven day forecasting in advance to do your vacation planning accordingly
  • Fruit example if you are not seen any food first time.
  • Classification’ - This is another algorithm type which help us to predict  answer like which Kabaddi team or cricket you will cheer or which political team you will vote.
    Multi-class classification – {A, B, C, D}, {1, 2}, {teacher, student}
  • This is one of the common prediction methods which everyone applies Smile sometimes. for example, in office, you can predict an engineer’s salary range depending upon last few engineer’s salary, prediction of property selling amount range Like this plot might be from 20 lac- 25 lac depending on last few years property price.
  • By the name, it is clear we need to find anomalies. for example, you have to determine from a group of  white cows and black cow you need to find out odd color cow means black color cow.
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