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.