Machine learning for bestt group - 20170714
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The document discusses the significance of machine learning and cognitive computing in decision-making processes for business leaders, emphasizing the challenges of trust and access to information. It outlines the principles of machine learning, including supervised and unsupervised models, as well as use cases such as automated loan underwriting and fraud detection. Additionally, it highlights IBM's contributions to machine learning technologies and platforms, which aim to facilitate data science and analytics.
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Machine learning for bestt group - 20170714
- 1. Machine Learning and Cognitive By Suwat Hongwiwat, Client Technology Architect, IBM Thailand 1
- 3. Business leaders frequently make decisions based on information they dont trust, or dont have1in3 83% of CIOs cited Business intelligence and analytics as part of their visionary plans to enhance competitiveness Business leaders say they dont have access to the information they need to do their jobs 1in2 of CEOs need to do a better job capturing and understanding information rapidly in order to make swift business decisions 60% and organizations need deeper insights Data is at the center of a new wave of opportunity 2.5 million items per minute 300,000 tweets per minute 200 million emails per minute 220,000 photos per minute 5 TB per flight > 1 PB per day gas turbines 1 ZB = 1 billion TB 3
- 4. Cloud Computing Easy to Access Computing Power Available of ready made as-a-Services 4
- 5. Artificial Intelligence Cognitive Computing Machine Learning Deep Learning Natural Language Processing 5
- 6. Definition Machine Learning is a type of Artificial Intelligence that provides the ability to learn without being explicitly programmed Artificial Intelligence is the ability of a machine to think and act - Mimics the capability of human brain in the areas of learning, problem solving etc. Cognitive computing is the simulation of human thought processes in a computerized model. Cognitive computing systems use machine learning algorithms and embedded NLP. Natural language processing (NLP) is the ability of a computer program to understand human speech / text as it is spoken. 6
- 7. What is Machine Learning? 7
- 8. Machine Learning is computer that can learn to solve problem without specific programming 8
- 9. Machine Learning have to works with a lot Data 9
- 10. Continuous learning is key for Machine Learning Continuous Monitor and Feedback 10
- 11. Step 1. Start with sample set of actual data with inputs and outputs Price 1. Number of Room 2. Area SQM 3. Location Set of Data Bed room SQM Location Price 1 33 City 3,700,000 2 48 City Edge 4,000,000 3 75 Outside 5,000,000 Input Output Sample Use case: Condo Seller 犖犖犢犖犢犖犖犖橿犖犖犖 犢犖犖犖橿権犖犖犖犢犖 犖犖萎犖園犖犖巌犢犖犖犖 犢犖 犖犖犖劇賢犖犖橿権 犖犖橿犖犖犖萎肩犖犖犖橿牽犖犢犖犖犖 犢犖犖 犖犢犖橿犖犖橿犖萎肩犖犖 machine 犢犖犢犖犖 犢犖犖犖橿権犖犖犖犢犖 犢犖犖橿犖萎犖橿犖犢犖犖∇犖橿犢犖? 11
- 12. Input 2 Objective of algorithm is to find the value of weight that come out as nearest output value from set of sample data Step 2. Feed Input data set to machine to calculate using Algorithm to get Output Output Input 1 Input n Machine with Algorithm Field 1 Field 2 Field 3 Weight1 Weight2 Weight3 Output Algorithm Calculation Sample Use case: Condo Seller Output Output 12
- 13. Step 3. Compare calculated output with actual output and adjust weight in algorithm, then go back to step 2 to get new closer output Field 1 Field 2 Field 3 Weight1 Weight2 Weight3 Calculated Output Actual OutputCompare Sample Use case: Condo Seller Stop iteration when get minimum different value between calculated and actual 13
- 14. There are a lot Algorithm being used in Machine Learning and Deep Learning is the popular one Regression Instance-based Regularization Decision Tree NL Clustering Association Deep Learning 14
- 15. Machine Learning algorithm can be grouped as 2 models Supervised model Learning by examples , training , target output Eg: Dad explains his child about different animals and its characteristics (Sound it makes, Apperance etc.) Implemented for - Tickets problem classification, Face recognition, Image recognition etc. Unsupervised model Learning by experience, no training , no target output Eg: Visiting a new country without knowing about their food, culture, language etc. Learning by experience. Implemented for Text analytics, Recommendations etc. 15
- 16. Machine Learning use cases will deal with large volume of data Use cases Explaination Automated loan underwriting Machine learning algorithms can be trained on millions of examples of consumer data (age, job, marital status, etc) and financial lending or insurance results (did this person default, pay back the loan on time, get in a car accident, etc). The underlying trends that can be assessed with algorithms, and continuously analyzed to detect trends that might influence lending and insuring into the future Fraud detection Machine Learning can learn and monitor users behavioral patterns to identify anomalies and warning signs of fraud attempts and occurrences, along with collection of evidence necessary for conviction are also becoming more commonplace in fighting crime. 16
- 17. People roles involve in Machine Learning Data Engineering Data Scienctist Business Analysis App Development Traditional Roles DBA Server Admin NW Engineer DC Specialist 17
- 18. What IBM do? 18
- 19. IBM Machine Learning Functionality for All! IBM Watson Machine Learning (on Bluemix) Data Science Experience with IBM Machine Learning IBM Machine Learning for z/OS (with DSX) Data ScientistApp Developer Data Scientist 19
- 20. IBM Watson Machine Learning as-a-Services 20
- 21. IBM Data Science Experience A L L Y O U R T O O L S I N O N E P L A C E IBM Data Science Experience is an environment that brings together everything that a Data Scientist needs. It includes the most popular Open Source tools and IBM unique value-add functionalities with community and social features, integrated as a first class citizen to make Data Scientists more successful. datascience.ibm.com Powered by IBM Watson Data Platform 21
- 22. IBM Machine Learning platform System z 22
- 23. IBM Machine Learning Platform - PowerAI Enabled by High Performance Computing Infrastructure Package of Pre-Compiled Major Deep Learning Frameworks Easy to install & get started with Deep Learning with Enterprise-Class Support Optimized for Performance To Take Advantage of NVLink 23
- 24. IBM Machine Learning Infrastructure S822LC for HPC: recommended configuration for PowerAI 2 Socket, 4 GPU System with NVLink Accelerated Servers and Infrastructure for Scaling Spectrum Scale: High-Speed Parallel File System Scale to Cloud Cluster of NVLink Servers 24
- 25. Useful Link What is Machine Learning ? https://www.youtube.com/watch?v=WXHM_i-fgGo Machine Learning Algorithms https://www.youtube.com/watch?v=02R-lZYccEY Natural language processing https://www.youtube.com/watch?v=jubBtD-C9rw https://www.youtube.com/watch?v=IKftaqRFyxE Types of Learning https://www.youtube.com/watch?v=gX4ORZ9geyc Supervised Vs Unsupervised Model Learning https://www.youtube.com/watch?v=nPFnlua2Y5Q What is Cognitive ? https://www.youtube.com/watch?v=h22n80aT2FY How IBM Watson Works https://www.youtube.com/watch?v=_Xcmh1LQB9I 25
- 26. Thank You 26