Advanced of Artificial Intelligence and Big Data

Overview:

Introduction:

Artificial Intelligence is the ability to rationally act and think. Applied AI is achieved when one can think and act/react in daily business. Artificial Intelligence has invaded everywhere both in personal and business life. It achieves pleasing results without costing excess effort or money. It is a must now to know how wisely to manage business operations, and be able to deliver them in the most optimized and efficient fashion. In this course, the essential concepts of artificial intelligence are being demonstrated as well as how and when to employ them. Machine Learning (ML) is one of the most popular applications of Artificial Intelligence (AI), and it is currently sweeping the globe, allowing computers to do tasks formerly performed by humans.

It bridges the gap between market buzz about Big Data and business realities. It documents real-world usage and ROI of Big Data, delineates successes and failures of Big Data, and the reasons for both. It discusses the pros and cons of various organizational structures for Big Data and Analytics teams. In short, the course peels away the complexities surrounding Big Data, boiling it down to the essence that managers need to know to make optimal decisions about the use, resourcing, risks, and value of Big Data.

Course Objectives:

At the end of this course the participants will be able to:

• Develop necessary AI and Understand how to plan and analyze using logic

• Explain how to imitate human in clustering and classification

• Understand how to design a Machine Learning based applications

• Analysis and Design AI Applications and Identify the key products in the big data platforms and describe their functional role.

• Describe the concepts of big data and Identify the business implications of big data for an organization.

• Describe the role of Hadoop and its use in the Big Data platform and the concepts of big data.

• Identify the business implications of big data for an organization and describe their functional role.

• Develop a deeper understanding of what big data means to your organization.

• Walk away with more knowledge about the role of the platform and its components, including NoSQL Database, Hadoop Distributed File System, Data Mining, and Big Data Connectors.

Targeted Audience:

• Quality, Safety, Reliability and Security officers
• Project Managers and Executive Managers
• Marketing Managers
• Instrumentation, process, systems, electrical and mechanical Engineers
• Finance, Budget-planner, Decision takers and Policymakers
• Programming for problem-solving
• Application-based programming with Python.
• OOPS, using JAVA.
• Data Structures using C.
• Database Management System.
• Pattern Recognition.
• Computational learning theory.
• Business Analysts.
• CIO/CTO.
• Configuration Consultant.
• Data Center Manager.
• Database Administrators.
• IT Director.
• Systems Architects.

Course Outlines:

Unit 1: An Overview of Artificial Intelligence:

• Introduction to AI and Success Stories
• Human Intelligence vs Artificial Intelligence
• History of AI, Intelligent Agents and Their Roles
• Limits of Artificial Intelligence
• Intelligent Decision Making 

Unit 2: Intelligent Agents:

• Introduction to Agents
• Different Types of Agents
• Knowledge-base and Database
• Logic Reasoning
• Unification
• Deduction Processes 

Unit 3: Machine Learning

• Supervised and Unsupervised Learning
• Classification and Clustering
• Artificial Neural Networks
• Learn by Examples
• Object Recognition
• Features and Classes 

Unit 4: Fuzzy Logic

• Introduction to Fuzzy Thinking
• Fuzziness vs Probability
• Fuzzy set and Fuzzy Rules
• Importance of Fuzzy logic and A real example of Fuzzy Controllers
• Building a Tiny Machine Learning Application 

Unit 5: Genetic Algorithm

• Overview of Genetic Algorithms
• The Need for Optimization, Maximization, and Minimization
• How GA Work and Evolve
• Genetic Algorithm Chromosomes, Genes, Selection, Mutation, and Crossover
• Dimension to Use Genetic Algorithm
• Real Genetic Algorithm Examples to Optimize Business Processes

Unit 6: Big Data at Work:

• What is Big Data?
• Business Challenges and Getting Fast Answers to New Questions.
• Industry Examples.
• Building Your Big Data Strategy.

Unit 7: Building a Big Data System:

• A General Look at Big Data Systems.
• Big Data Solution.
• NoSQL Database Hadoop.
• Distributed File System.
• In-Database Analytics Platform.

Unit 8: Building a Big Data System Using NoSQL Database:

• What is a Key-Value Store?
• Why Would I Need a NoSQL Database?
• Using NoSQL Database to Run a Website.

Unit 9: Using Hadoop and Hive to Store and Transform Data:

• What is Hadoop?
• Interacting with HDFS.
• MapReduce.
• Using Hive to Transform Data.

Unit 10: Integrating Hadoop Data into:

• Big Data Connectors.
• Data Integrator Working on Hadoop Data and Transforming Data in ODI.
• Using Advanced Analytics to my Data:
• Mining Database Data with R Enterprise and Mining Hadoop Data with R Connector for Hadoop Creating.
• Real-Time Similarity Scores with Data Mining.