![REFERENCE
[1] Pedro Domingos, “The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our
World,” 22/09/2015.
[2] Andrew NgJ – Machine Learning Yearning 2016
[3] Machine Learning for Dummies - John Paul Mueller, Luca Massaron - May 2016
[4] Machine Learning Using R - A Comprehensive Guide to Machine Learning - Karthik Ramasubramanian Abhishek
Singh - 2017
[5] Understanding Machine Learning: From Theory to Algorithms by Shai Shalev-Shwartz and Shai Ben-David-
Published 2014 by Cambridge University Press.
[6] A Course in Machine Learning - Hal Daumé III
[7] coursera.org, ' Machine Learning', 2017. [Online]. Available: https://www.coursera.org/learn/machine-learning.
[Accessed: 23- Jun- 2017].
[8] Max Bramer “ Principles of Data Mining, Third Edition”, 2016
[9] Reinforcement Learning Edited by Cornelius Weber, Mark Elshaw and Norbert Michael Mayer – 2008
[10] Roberto Battiti Mauro Brunato “The LION Way Machine Learning Plus Intelligent Optimization” – 2014.](https://image.slidesharecdn.com/machinelearningwithaneffectivetoolsofdatavisualizationforbigdata-190610094820/85/Machine-learning-with-an-effective-tools-of-data-visualization-for-big-data-18-320.jpg)
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Machine learning with an effective tools of data visualization for big data
- 1. Machine Learning with an Effective tools of Data Visualization for Big Data Kannan Ramasamy, Research Scholar, Department of Computer Science, Rathinam College of Arts & Science, Coimbatore, Tamil Nadu, India. [email protected]
- 2. Machine Learning with an Effective tools of Data Visualization for Big Data Machine LearningIntroduction ML Algorithms ML vs DV Tools & Applications Conclusion 1 2 3 4 65
- 3. I.Introduction • A picture is worth a thousand words, but an interactive visualization can be worth even more. • Machine Learning + Data visualization. • Summarize and reduce the Big Data. • Machine Learning • Supervised Learning • Unsupervised Learning • Reinforcement Learning • Data Visualization • Software Tools • Applications • Machine Learning Vs Data Visualization Visualization of the result Data ML-Algorithm Knowledge Data Visualization Result
- 4. II.Machine Learning • Arthur Samuel (1959) : • "Field of study that gives computers the ability to learn without being explicitly programmed“ • Tom Mitchell (1998) : • “A computer program is said to learn from experience E with respect to some task T and some performance measure P, if its performance on T, as measured by P, improves with experience E”. • There are several ways to implement machine learning algorithms • Automating automation • Getting computers to program themselves • Writing software is the bottleneck • Let the data do the work instead!
- 5. II.Machine Learning Tradition Programming Vs Machine Learning
- 6. II. Machine Learning Algorithms
- 7. III (A). DECISION TREE • The form of tree structure and breaks down a dataset into smaller and smaller subsets. • A final point is reached and a prediction is made.
- 8. III (A). RANDOM FOREST • Ensemble classifier Which combines the results of many decision tree models .
- 9. III (B). CLUSTERING • Clustering means grouping of objects based on the information found in the data describing the objects or their relationship. • One group should be similar to each other but different from objects in another group.
- 10. III (C). TEXT ANALYTICS • Automating the reading process and providing a brief summary of compiled from possibly thousands of documents. • Heavily rely on probability theory and the occurrence of certain words, which can be used to predict the meanings and themes of the text such as • Sentiment Analysis • Naïve Bayes
- 11. III (D). NEURAL NETWORKS • Inspired by the way in which the brain performs a particular learning task. • Composed of many artificial neurons that are linked together according to a specific network architecture. • The objective is to transform the inputs into meaningful outputs. Inputs Output
- 12. III (E). LINK ANALYSIS • To find relationships and connections in this evermore-connected world. • Subset of mathematics called graph theory. • which represents the relationship between objects as edges and the objects themselves as nodes.
- 13. III (F). SURVIVAL ANALYSIS • Survival Analysis is referred to statistical methods for analyzing survival data • Survival data could be derived from laboratory studies of animals or from clinical and epidemiologic studies
- 14. IV. MACHINE LEARNING VS DATA VISUALIZATION • Machine Learning(ML) and Data Visualization (DV) methods are used to summarize and reduce the complex data to levels that can be understood by humans. output
- 15. V. VISUALIZATION SOFTWARE TOOLS AND MACHINE LEARNING • There are best tools and software’s are available for data visualization and machine learning applications. • Facets • Anaconda • R Programming • Tableau • D3 • Qlikview • Microsoft Power BI • Oracle Visual Analyzer
- 16. VI. APPLICATIONS • Below is a short list of intuitive Applications: • Emotional Intelligence • My Eye: Unparalleled Artificial Vision • Hair Coach • Autonomous Cars • Fraud Detection • Recommendations • Healthcare • etc
- 17. DISCUSSION AND CONCLUSION • ML algorithms and DV tools will help to analyze and scale the large datasets effectively. • The demand for better Visualization tools is increasing constantly which is only going to increase in future. • Also ML is an incredible breakthrough in the field of artificial intelligence.
- 18. REFERENCE [1] Pedro Domingos, “The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World,” 22/09/2015. [2] Andrew NgJ – Machine Learning Yearning 2016 [3] Machine Learning for Dummies - John Paul Mueller, Luca Massaron - May 2016 [4] Machine Learning Using R - A Comprehensive Guide to Machine Learning - Karthik Ramasubramanian Abhishek Singh - 2017 [5] Understanding Machine Learning: From Theory to Algorithms by Shai Shalev-Shwartz and Shai Ben-David- Published 2014 by Cambridge University Press. [6] A Course in Machine Learning - Hal Daumé III [7] coursera.org, ' Machine Learning', 2017. [Online]. Available: https://www.coursera.org/learn/machine-learning. [Accessed: 23- Jun- 2017]. [8] Max Bramer “ Principles of Data Mining, Third Edition”, 2016 [9] Reinforcement Learning Edited by Cornelius Weber, Mark Elshaw and Norbert Michael Mayer – 2008 [10] Roberto Battiti Mauro Brunato “The LION Way Machine Learning Plus Intelligent Optimization” – 2014.