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!

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.
dschennai@outlook.com

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.