“DEEP LEARNING PROJECT for Digital marketing” starts today. I present probability of visiting the store here

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At the beginning of this year,  I set up a new project of my company.  The project is called “Deep Learning project” because “Deep Learning” is used as a core calculation engine in the project. Now that I have set up the predictive system to predict customer response to a direct mailing campaign, I would like to start a sub-project called  “DEEP LEARNING PROJECT for Digital marketing”.  I think the results from the project can be applied across industries, such as healthcare, financial, retails, travels and hotels, food and beverage, entertainments and so on. First, I would like to explain how to obtain probability for each customer to visit the store in our project.

 

1. What is the progress of the project so far?

There are several progresses in the project.

  • Developing the model to obtain the probability of visiting the store
  • Developing the scoring process to assign the probability to each customer
  • Implement the predictive system by using Excel as an interface

Let me explain our predictive system. We constructed the predictive system on the platform of  Microsoft Azure Machine Learning Studio. The beauty of the platform is Excel, which is used by everyone, can be used as an interface to input and output data. This is our interface of the predictive system with on-line Excel. Logistic regression in MS Azure Machine Learning is used as our predictive model.

The second row (highlighted) is the window to input customer data.

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Once customer data are input, the probability for the customer to visit the store can be output. (See the red characters and number below). In this case (Sample data No.1) the customer is less likely to visit the store as Scored  Probabilities is very low (0.06)

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On the other hand,  In the case (Sample data No.5) the customer is likely to visit the store as Scored Probabilities is relatively high (0.28). If you want to know how it works, could you see the video?

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Azure ML 4

 

2. What is the next in our project?

Once we create the model and implement the predictive system, we are going to the next stage to reach more advanced topics

  • More marketing cases with variety of data
  • More accuracy by using many models including Deep Learning
  • How to implement data-driven management

 

Our predictive system should be more flexible and accurate. In order to achieve that, we will perform many experiments going forward.

 

3. What data is used in the project?

There are several data to be used for digital marketing. I would like to use this data for our project.

When we are satisfied with the results of our predictions by this data,  next data can be used for our project.

 

 

Digital marketing is getting more important to many industries from retail to financial.   I will update the article about our project on a monthly basis. Why don’t you join us and enjoy it!  When you have your comments or opinions, please do not hesitate to send us!

If you want to receive update of the project or want to know the predictive system more, could you sing up here?

 

 

 

Microsoft, Excel and AZURE are either registered trademarks or trademarks of Microsoft Corporation in the United States and/or other countries.

Notice: TOSHI STATS SDN. BHD. and I do not accept any responsibility or liability for loss or damage occasioned to any person or property through using materials, instructions, methods, algorithm or ideas contained herein, or acting or refraining from acting as a result of such use. TOSHI STATS SDN. BHD. and I expressly disclaim all implied warranties, including merchantability or fitness for any particular purpose. There will be no duty on TOSHI STATS SDN. BHD. and me to correct any errors or defects in the codes and the software.

What can computers do now ? It looks very smart !

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Lately I found that several companies such as Microsoft and IBM provide us services by machine learning. Let us see what is going on now.

These new services are based on the progress on Machine learning recently. For example, Machine translation services between English and Spanish are provided by Microsoft skype. It uses Natural Language Processing by Machine learning. Although it started at Dec 2014, the quality of the services is expected to be improved quickly as a lot of people use and computer can learn the data from such users.

 

It is beneficial for you to explain what computers can do lately so that you can imagine new services in future. First, computers can see the images and videos and identify what it is. This is image recognition. Second, it can listen to our speech and interpret what you mean. This is speech recognition. It can translate one language to another, as well. This is machine translation. Third, computers can research based on concepts rather than key words. Fourth, it can calculate best choice among the potential options. This is an optimization. In short computers can see, listen to, read, speak and think.

These functions are utilized in many products and services although you cannot notice it. For example, IBM Watson Analytics provides these functions through platform as a service to developers.

 

I expect these functions enable computers to behave just like us. At the initial phase, it may be not so good just like a baby. However, machine learning allows computers to learn from experience. It means that the computer will perform better than we do in many fields. As you know, Shogi, one of the popular Japanese board game, artificial machine players can beat human professional teams. This is amazing!

Proceeding forward, it is recommended that you understand how computers are progressing in terms of the functions above. Many companies such as Google, Facebook invest a great deal of money in this filed. Therefore, many services are anticipated to be released in near future. Some of new services can impact our jobs, education and society a lot. Some of them may arise new industries in future.

 

Some day, when you are in the room, the computer can identify you by computer vision. Then ask if you want to drink a cup of coffee. The computer holds a lot of data, such as temperature, weather, time, season, your preference in it and generates the best coffee for you. If you want to know how this coffee is generated, the computer provides you a detailed report about the coffee. All settings are done automatically. It is the ultimate coffee maker by using powerful computer algorithm. Do you want it for you?

 

 

Note:IBM, IBM Watson Analytics, the IBM logo are trademarks of International Business Machines Corporation, registered in many jurisdictions worldwide. 

I have started Nanodegrees in Udacity this week. Yes, I will develop my website by myself!

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I have started Front-End web developer course of Nanodegrees in Udacity this week.  I would like to obtain the skills of front-end web development, such as a website and mobile service because I would like to develop websites and mobile services, which are backed by machine learning.  So I am going to  set up the prototype website on Microsoft Azure and use visual studio online for writing codes of HTML, CSS and Java script.  When I learn methods to write the codes in Nanodegrees, I try to use these methods to develop the prototype website on Microsoft Azure.  I think it is good because I can learn the methods of wiring codes through Nanodegrees and develop my websites on Microsoft Azure at the same time.

As I said before, Nonodegrees focus on industries practices and applications for jobs.  It looks like an open training on the job.  It introduces a project based method, where participants should make several web sites by themselves according to instructions. I hope I can develop websites by writing HTML, CSS and Java script by the end of this course.

Actually, it is my first online course, which is required to pay for.  It costs 200 USD per month. I took more than 10 MOOCs (massive open online courses) in Coursera and Edex before.  Unlike Nanodegrees, these courses are free so I do not pay any fee at all. Most courses in Coursera and an Edex are provided by professors of the universities.  So Nanodegrees are contrasted to Coursera and Edex, which are major providers of MOOCs. I would like to explain what the difference is between Nanodegrees and other free courses going forward.

I want to make it a kind of parallel processing to develop websites and mobile services. When new methods of developing of websites and mobile services are provided through Nanodegrees,  I will deploy prototype websites on Microsoft Azure at the same time.  In addition to that,  the project to develop recommender engines is going on in my company and the prototype engine will be expected to be developed within this year.  This engine will be combined with the websites to enhance their services. I think it might be possible as Microsoft Azure has machine learning as a service.

This is a scheme to set up the platform to develop websites and mobile service backed by machine learning. Front-end developer course of Nanodegrees in Udacity might make it possible even for beginners like me. I hope this program keeps a high standard to provide skills and methods to participants so that everyone thinks it is worth paying fees to participate in this course.  I am sure Sebastian Thrun, CEO and cofounder of Udacity makes it happen.

 

I started Microsoft Azure ML. It is definitely amazing!

 

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Finally, I started MS (Microsoft) Azure ML (Machine Learning).  So in this blog,  I would like to report what it is and why it is amazing for not only data scientists but also businessmen/women. MS Azure ML is a kind of ML services on the cloud. It is easy to start data analysis by ML, even for beginners.  For data analysis, it is critically important to have seamless processes  1. Data 2. Models 3. Output. Unfortunately, most of ML services are provided as an independent one from other services, therefore users should gather data and inform results of data analysis of stakeholders and management, one by one, independently, outside ML services. However, when we see the portal of MS Azure, Machine Learning is built on as one of the functions in MS Azure.   So we can operate this ML as one of the processes in MS Azure. It is completely different from other ML services.  Then let me go to  MS Azure ML studio and look at major functions in details.

After creating ML working space, we can go to ML studio where experiments can be done by using Graphical User Interface.

1.  Data

More than 30 data sets,  for example census income data,  are set up in advance.  So beginners can start data analysis immediately for training.  It is good because they can concentrate on data analysis in MS Azure ML.  Data, which are analyzed, should be just dragged and dropped into experiment area.  So data can be handled with their intuition.  No need to read manuals in advance.

 

2. Predictive models

In ML studio, there are more than 10 predictive models for classification.  Logistic regression, neural network and SVM., etc. are available here.  Models for regression and clustering are also available. According to the documents,  more than 300 R packages, which are open source in R language, are  also available.  It is amazing that these models can be used by drug and drop in ML studio without writing code. So beginners can analyze data without coding the models.

 

3. Output

Once data analysis is completed and predictive models are developed,  it is easy to release it as web application services by clicking the buttons to deploy it in the web. It is usually difficult to explain how predictive models work  just by theory.  Web applications must be powerful tools to explain how the models work to stakeholders, managements and customers because web applications can show us the results based on inputs from users.

 

As I said before, it is critically important to have seamless process 1. Data  2.  Model  3. Output.  Microsoft Azure ML realizes this as a cloud service. I would like to develop interesting web services based on Machine Learning in the future. The current version of  MS Azure ML is a preview,  so functionalities might be changed or removed, added going forward. If you need more information about MS Azure ML, please refer to this web. Let us enjoy machine learning !

Is this a real game changer in data analysis, isn’t it ?

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Machine learning (ML) is considered to be difficult to be implemented in practice. However, it has not been difficult anymore because new service is available from Microsoft.  This service is called “Microsoft Azure ML“. It can change the way to use Machine Learning in business.  It can be a game changer in data analysis, too.

 

1.  ML is integrated in the seamless processes.

ML can not be exist independently. Data should be gathered and cleaned up, models should be generated and validated correctly and results of data analysis should be shared among corporate managements and used for making better business decisions. Microsoft Azure ML realizes this ideal environment. So it is a user-friendly tool and can be fit for beginners of data analysis. It can be a strategic management tool, too.

2.  We can use R.

R is a popular language in data analysis. Microsoft Azure supports the R language. Even though there is no need to write code  by ourselves to start analyzing data,  the code for the models written in R can be seen on the screen.  It is very good because you can do fine tune to the models if you have expertise on computer languages.  In addition to that, more than 350 packages written in R are available, too.  Therefore, we do not need to write the code of  models by ourselves.  We just use these packages, which are open source in order to analyze data.  If you try to develop your own models by using R, of course  it is possible.  So Microsoft Azure ML is also good for experts of R language.

3.  The cost is dramatically decreased to start data analysis by ML.

10 years ago,  proprietary software of data mining was too expensive for individuals and start-up.  But Microsoft Azure ML can be used with “Pay as you go”. So initial cost can be decreased dramatically.  It means that  application services can be developed even by individuals with less cost.  It must be wonderful for entrepreneurs who try to make the world better by data analysis.  I want to do that.

 

This service has just started as preview and only available in the US south central so far.  Whenever it is available in Asia, I would like to try this service.  When I write this blog,  I heard the news, which says Apple and IBM team up to expand cloud services in corporate business.  Therefore, this kind of services about data analysis based on the cloud may be released from competitors of Microsoft in the future.  It must be good for us as we can choose the ML services based on our needs.  In the near future,  I am sure ML dominates the digital services for customers to choose good products and services.  Let us start now!