Advanced Topics in AI: AI Automation and Beyond

Duration: 8-10 hours

Industry: Information Technology

Prerequisites for Advanced Topics in AI: AI Automation and Beyond

1. Basic AI and Chatbot Knowledge
Participants should have a general understanding of AI, machine learning, and chatbot concepts as the course builds on this foundation. Completing a basic AI/Chatbot or similar method beforehand is recommended, although optional.

2. Familiarity with No-code/Low-code Platforms
Experience with no-code/low-code platforms, tools, or projects is required, as these will be extensively used throughout the course.

3. Basic Knowledge of Statistics and Probability
Basic statistics and probability knowledge will help participants understand advanced AI algorithms or techniques covered in the course.

Course Objectives

The course aims to provide no-code/low-code users with advanced knowledge and hands-on experience in AI automation and other emerging AI technologies, preparing them to create sophisticated solutions with minimal coding.

Course Outline

1. Introduction to Advanced AI Topics

1.1. Review of AI Fundamentals and the Tip of the AI Iceberg
1.2. Market Trends in AI Solutions and Technology: Private vs OpenSource
1.3. Advanced AI Applications and Use Cases
1.4. Ethics and Social Impact of Advanced AI Technologies

2.Delving into AI Automation

2.1. Introduction to AI Automation
2.2. Understanding Robotic Process Automation (RPA)
2.3. Integrating AI with RPA
2.4. Benefits of AI Automation
2.5. AI Automation Use Cases and Industry Applications

3. Exploring AI for Natural Language Processing (NLP)

3.1. Overview of NLP
3.2. Applications of NLP in Business
3.3. NLP Techniques and Tools for No-code/Low-code Users
3.4. Practice: Text Analysis and Generation

4. Advanced AI Chatbots and Virtual Assistants

4.1. Transitioning from Basic to Advanced AI Chatbots
4.2. Building Context-aware and Multilingual Chatbots
4.3. Incorporating Advanced NLP and Sentiment Analysis
4.4. Practice: Advanced AI Chatbot Features

5. AI and Big Data Analytics

5.1. Big Data Concepts and Technologies
5.2. AI Techniques for Data Analytics

5.3. No-code/Low-code Tools for AI-driven Data Analysis
5.4. Practice: AI-powered Data Visualization

6.Advanced AI in Decision-Making and Optimization

6.1. Decision Support Systems and Optimization Techniques
6.2. Philosophy and Methodologies in AI-driven Decision Making
6.3. AI for Resource Allocation and Scheduling

6.4. Practice: AI-based Optimization Solutions