Objectives

• Provide participants with a foundational understanding of Artificial Intelligence (AI) and Machine Learning (ML) concepts, techniques, and applications.● - Introduce participants to the three main types of AI applications: Computer Vision, Natural Language Processing (NLP), and Tabular Data Processing.● - Introduce participants to the types of Machine Learning, including Supervised, Unsupervised, and Reinforcement Learning, and their usage in real-world scenarios.● - Teach participants about Data Analysis techniques, including Descriptive, Inferential, and Predictive analysis, and how they are used in AI and Machine Learning.● - Introduce participants to No-code/Low-code development tools for building AI/ML models.● - Enable participants to identify potential use cases for AI and ML in their organization.● - Engage participants in practical exercises and activities to ensure they understand the concepts and techniques covered.● - Provide participants with hands-on experience wi

Outcomes

A foundational understanding of Artificial Intelligence (AI) and Machine Learning (ML) concepts, techniques, and applications.● - Familiarity with the three main types of AI applications and their practical uses: Computer Vision, Natural Language Processing (NLP), and Tabular Data Processing.● - Knowledge of the types of Machine Learning and their effectiveness in real-world scenarios: Supervised, Unsupervised, and Reinforcement Learning.● - Knowledge of Data Analysis techniques and how they are used in AI and Machine Learning: Descriptive, Inferential, and Predictive analysis.● - Familiarity with No-code/Low-code development tools for building AI/ML models.● - The ability to identify potential use cases for AI and ML in their organization.● - The ability to engage in practical exercises and activities to ensure understanding of the covered concepts and techniques.● - Hands-on experience with No-code/Low-code development tools for building AI/ML models.

Course Contents

Day 1 : ● Computer Vision Applications ● Natural Language Processing Applications● Tabular Data Processing Applications ● A.I Applications vs Conventional Programming.Day 2 : ● Introduction to Machine Learning● Types of Data Analysis ● Build Projects Without Code

Description

Data will be available soon!