Course Prerequisites:
Familiarity with data handling, including collection, cleaning, and preprocessing (beneficial but not mandatory).
No prior coding experience required (hands-on with no-code tools).
Basic knowledge of data science, algorithms, and decision-making principles (recommended).
Suitable for professionals or enthusiasts looking to expand their knowledge in AI agent technology and data-driven decision-making.
Modules:
Module 1: Introduction to AI Agents
1.1 What is an AI Agent?
1.2 Components of AI Agents
1.3 Types of AI Agents
1.4 Hands-on: No-Code AI and Machine Learning Models for Data Agents
Module 2: Data Agents and Their Role in AI Systems
2.1 AI Data Agents
2.2 AI vs. AI Data Agent
2.3 Components of AI Data Agents
2.4 Types of AI Data Agents
2.5 Existing AI Data Agents in Trend
Module 3: Data Collection and Acquisition for AI Data Agents
3.1 Steps in AI Data Collection- Structure & Pan
3.2 Methods Of Data Collection
Module 4: Data Pre-processing and Feature Engineering
4.1 Data Cleaning and Transformation
4.2 Feature Engineering for AI Models
4.3 No-Code AI Data Agent for Preprocessing & Feature Engineering
Module 5: AI and Machine Learning Models for Data Agents
5.1 Introduction to Machine Learning Models for Data Agents
5.2 Model Selection and Training
5.3 Hands on: No-Code AI and Machine Learning Models for Data Agents
Module 6: AI in Compliance & Ethics
6.1 Ethical Considerations in AI Data Agents
6.2 Security and Privacy Concerns
Module 7: Capstone Project
7.1 Problem Statement
7.2 Practical Implementation
7.3 Evaluation and Optimization
7.4 No-Code AI and Machine Learning Models for Data Agents