Advanced Artificial Intelligence and Big Data

Introduction:

The Advanced Artificial Intelligence and Big Data training program delves into cutting-edge techniques in AI and big data analytics. Participants explore advanced topics like machine learning algorithms and big data processing frameworks. Through theory and hands-on exercises, individuals develop expertise to derive insights and innovate across domains.

Program Objectives:

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

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

• Understand how to plan and analyze using logic to design Machine Learning-based applications.

• Explain how to imitate human in clustering and classification for AI applications.

• Identify the key products in the big data platforms and describe their functional role.

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

• 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:

• Database management system.

• Data structures, Systems architects, and Marketing managers.

• Chief Information Officer (CIO) / Chief Technology Officer (CTO).

• Finance, budget planners, decision-makers, and policymakers.

• Quality, safety, reliability, and security officers.

• Application-based programming with Python.

• Object-oriented programming using Java.

• Project managers and executive managers.

• Instrumentation, process, systems, electrical, and mechanical engineers.

• Programming for problem-solving.

Program 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.