Discuss about the Data and Ability Analysis Of IBM Watson.
As the ability to collect, store and analysis of data is increasing with the passage of time, the data which is being generated is becoming more and more diverse with an ever increasing frequency. Due to this the data science field is depicting a substantial and rapid growth as well. Henceforth the emergence of data analytics tools is being embraced (Zhang et al., 2017). This research work will focus on providing a brief overview of the data analytics tools like IBM Watson, Python and Graphlab. The key features of these tools will also be discussed accordingly. The use and efficiency of these tools in various aspects will also be traced out in this paper. The research work will also emphasize on how these three analytical tools can be used for solving the seen common problems of data analysis will also be discussed in this regard.
IBM Watson is the tools in turn arranges the data policies, data management, preparation of data and the capabilities of analysis into a single framework. The platform in turn amalgamates IBM Watson Knowledge Platform and IBM Watson Studio and the tools like Data Refinery and IBM Streams Designer as well. A wider range of IBM Cloud Services are also integrated within IBM Watson at the same time it also emphasizes on the connections to the cloud and data stores on premises (Xia et al., 2017). Through this tool data can be controlled, shared and discovered with the help of Watson Knowledge Catalogue and the refining and preparation of the data could be done through Data Refinery. Afterwards the resources can be organized so as to analyze the data with the help of Watson Studio. The IBM Watson application is effectively designed and integrated in such a way that the user interface and the frameworks remain the same which enables the user to pick up any tool which will be needed by the organization (Miyan, 2017). Watson Studio on the other hand, which provides the user with the environment so that the business problem can be solved through the analysis of data. Furthermore, data refinery is a tool for self-service data preparation which can be used by the data scientists and engineers. With the help of this tool significantly larger volume of data can be transformed into consumable quality information which is again ready for analytics (De Sa et al., 2016). Streams designing on the other hand, is again used by the individuals to analyze and assess massive changes in the data. Irrespective of the fact that whether the data is structured or unstructured. The data can be leveraged at scale
The key features of the IBM Watson is that it incorporates the Neural Network Modeler which again offers a graphical interface through which deep learning flows can be created. The neural network modeler of the IBM Watson supports almost 31 different types of layers and any of the architecture ca be designed with the help of different combinations of these 31 layers (Bagavathi & Tzacheva, 2017).
Multiple linear regression can also be carried out with the help of IBM Watson. This is a simple statistical technique which can be used through which different explanatory variables. The objective of the multiple linear regression model is to establish a relationship between explanatory variables and the response variable.
IBM Watson has introduced a new parallel SVM solver which is based on the Forgetron Algorithm. When compared to a previously proposed parallel SVM solver and with a single node solver as well (Peters et al., 2014). The findings of these comparisons provides results regarding the speed, accuracy as well as the capability to process larger datasets. On the basis of the results it has been observed that none of them performs best in terms of all the three metrics. They perform best any two of them and hence IBM Watson recommends that the most efficient SVM solver can be selected on the basis of the requirements.
The least square support vending machine which is a newer version of SVM can also be solved in the IBM Watson. This can be regarded as a major tool which can be used efficiently in the analytics.
Python is characterized as a high-level, interactive, interpreted and subject oriented language for scripting. The language is so designed that it is extremely readable. The main advantages of this is that it uses English keywords quite frequently while the other scripting languages use punctuations. It uses less syntactical constructions in comparison to the other languages as well. Presently Python has become very popular because of its easily understandable and apparent syntax, easy learning feature and portability as well (Salloum et al., 2016).
The programming language includes the feature of Java and C. The style of writing provided by the language is elegant like C and in the context of object oriented programming it easily offers objects and classes like programming language Java. Python was developed during the late eighties or the early nineties by Guido van Rossum in Netherlands at the National Research Institute for Mathematics and Computer Science. It is derived from various other languages which includes Modula- 3, C++, ABC, SmallTalk and Algol- 68 (Saltz, 2015). It is a copyrighted program like the Perl and can be obtained currently under the GNU General Public License. A core development team at the institute looks after the maintenance of Python.
Python is a very critical and complex programming language which is used in numerous applications. Over the passage of time the larger community around this open sources language has given rise to various tools which works efficiently with Python. Presently a significantly large number of tools have been developed specifically in the context of data science. Hence it can be stated that Analysis of data with the help of Python has never been a very easy task to perform. The key advantage of Python is that it is very easy to learn because of its simple structure and easily defined syntax as well (Khamis & Kamarudin, 2014). The code used in Python is very clearly visible and is well defined. The language supports the interactive mode which allows the user to test and debug the snippets of code in an interactive manner.
In order to carry out Artificial Neural Network in Python certain modular machine learning libraries could be used. In such a context it can be stated that PyBrain is a modular machine learning library which can be used with Python so as to design the Artificial Neural Network. The key feature that differentiates the PyBrain from the other machine learning libraries is that it is quite easy to use and the entry level students. However, despite being very easy to use it also provides better flexibility and algorithms which can effectively be used for the state of art research works (Mu et al., 2014). The PyBrain is constantly working on the algorithms so as to fasten the process and thereby improve its usability.
Multiple linear regression can be performed in mainly two ways in Python with the help of Statsmodels and scikit-learn. In the context of statsmodel it can be stated that it is a Python module which provides functions and classes for estimating different statistical models (Pow et al., 2014). Various learning algorithms can be used for multiple linear regression in the Scikit-Learn and this is considered as the Golden Standard when it comes about machine learning in Python.
The support vector machines are defined as the supervised methods of learning which can be used for regression, classification and outlier detection. The SVM in the scikit learn supports inputs in the form of both the dense and sparse sample vectors. However, in order to use the SVM to provide predictions in the context of sparse data, the data must be feed in such a format.
Decision tree can be characterized as one of the most powerful algorithm. It is considered under the category of supervised algorithms of learning. It works effectively for categorical as well as the continuous variables. In such a case it should be noted that when the decision tree is constructed taking into consideration the continuous variables it is teemed as the regression tree (Mullainathan & Spiess, 2017).
The k Nearest Neighbors is regarded as an easy algorithm which can be understood and implemented easily. The kNN can be implemented in Python quite easily, primarily it should identify the most similar dataset, generate instance, and examine the prediction’s accuracy and then tying it altogether.
The scikit learning is the most used library which is used in Python for implementing the algorithms of machine learning. SVM can be performed with the help of scikit learn library.
Graphlab Create is an efficient platform of machine learning developed by Turi especially for the data scientists and developers. The subsidiaries of Apple equips the users with the necessary algorithms which in turn enables them to design efficient applications in Python. With the help of GraphLab Create users generally ensure access to the toolkits which are especially designed for the simple and effective application development (Khamis & Kamarudin, 2014). Advanced machine learning applications are also provided by the GraphLab Create as well. These mainly emphasizes on text analytics, classification, anomaly detection and model optimization. These empowers the developers to develop the state of the art programs of machine learning.
GraphLab Create is provided free of cost for the purpose of academic use. The willing users can install the tool and thereby register in the tool and will get provided with a one year old subscription. Another key feature of GraphLab is that it allows the users to run the same code simultaneously on their desktop as well as in the distributed system as well (Mu et al., 2014). This is enabled by the platform through a Hadoop Yarn and may be through a Cluster EC2. The validity of the codes in different system is also tested with the help of GraphLab.
Neural Network is characterized as one of the most popular classical models in the field of artificial intelligence as well as machine learning. This helped to achieve a broader success in the computer oriented tasks such as object recognition (Peters et al., 2014). Artificial Neural Network can easily be designed with the help of the designated algorithm provided within the GraphLab Create.
In oredr to create the linear regression model in GraphLab Create the formula of “Graphlab.linear_regression.create ()” can be used. A significantly detailed list if the specified parameter options as well as the sample codes should be available in the documentation in order to create the function.
The support vector machine can also be used for predicting the binary target variable with the help of using different feature variables. The SVMClassifier model forecasts the binary target variable while one or more feature variable are provided. However, it is also necessary to mention that the model cannot be designed directly. Rather it uses of “graphlab.svm_classifier.cerate ()” for creating an instance of the model. Additional details regarding the options of the parameters and the samplers of code will be available in the documentation for the create function (Miyan, 2017).
The decision tree classifier which starts with the classification tasks. The prediction of decision tree is completely based in the collection of base learners, this is the algorithm which is considered as a special case of boosted trees regression with the number of trees set to 1.
Conclusion
On a concluding note it can be stated that the assignment has significantly outlined the three analytical tools which are IBM Watson, Python and Graphlab Create. There are namely seven attributes which are individually discussed in the context of these three tools as well. It has been observed that each of these tools possess different set of algorithms and commands for carrying out these specified functions. Each of the tools have also been identified to be equally efficient in all the regards.
Reference List
Bagavathi, A., & Tzacheva, A. A. (2017, June). Rule Based Systems in a Distributed Environment: Survey. In Proceedings of International Conference on Cloud Computing and Applications (CCA17), 3rd World Congress on Electrical Engineering and Computer Systems and Science (EECSS’17)(pp. 1-17).
De Sa, C., Ratner, A., Ré, C., Shin, J., Wang, F., Wu, S., & Zhang, C. (2016). Deepdive: Declarative knowledge base construction. ACM SIGMOD Record, 45(1), 60-67.
Khamis, A. B., & Kamarudin, N. K. K. B. (2014). Comparative Study On Estimate House Price Using Statistical And Neural Network Model. International Journal of Scientific & Technology Research, 3(12), 126-131.
Miyan, M. (2017). Applications of Data Mining in Banking Sector. International Journal of Advanced Research in Computer Science, 8(1).
Mu, J., Wu, F., & Zhang, A. (2014). Housing value forecasting based on machine learning methods. In Abstract and Applied Analysis (Vol. 2014). Hindawi.
Mullainathan, S., & Spiess, J. (2017). Machine learning: an applied econometric approach. Journal of Economic Perspectives, 31(2), 87-106.
Peters, S. E., Zhang, C., Livny, M., & Ré, C. (2014). A machine-compiled macroevolutionary history of Phanerozoic life. arXiv preprint arXiv:1406.2963.
Pow, N., Janulewicz, E., & Liu, L. (2014). Applied Machine Learning Project 4 Prediction of real estate property prices in Montréal.
Salloum, S., Dautov, R., Chen, X., Peng, P. X., & Huang, J. Z. (2016). Big data analytics on Apache Spark. International Journal of Data Science and Analytics, 1(3-4), 145-164.
Saltz, J. S. (2015, October). The need for new processes, methodologies and tools to support big data teams and improve big data project effectiveness. In Big Data (Big Data), 2015 IEEE International Conference on (pp. 2066-2071). IEEE.
Xia, Y., Liu, Y., Tan, W., Crawford, J., Watson, C. Y. L. I. T., & Tech, L. N. G. (2014). A Highly Efficient Runtime and Graph Library for Large Scale Graph Analytics. Network, 2, 3D.
Zhang, C., Ré, C., Cafarella, M., De Sa, C., Ratner, A., Shin, J., … & Wu, S. (2017). DeepDive: declarative knowledge base construction. Communications of the ACM, 60(5), 93-102.
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