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Advanced Artificial Intelligence and Big Data

Overview:

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.

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