Login

**AD-TEAM** · 6 hours ago

[Image: aecdad6276f9d876ce6d21fd33738c7a.jpg]

Building Intelligent Ai Tutors For Domain-Specific Knowledge
Published 1/2025
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz
Language: English | Size: 2.10 GB | Duration: 3h 15m

Master AI Tutors: From Data Preprocessing to QA Systems, Summarization, and Real-World Deployment Techniques

What you'll learn

Understand the process of designing AI tutors for specific knowledge domains.

Learn to preprocess and analyze domain-specific data.

Develop skills to build retrieval-based systems and enhance them with advanced summarization techniques.

Optimize and evaluate AI tutors for performance and effectiveness.

Prepare for practical applications of AI tutoring systems in real-world scenarios.

Requirements

Python programming language course

Machine Learning course

Description

This comprehensive course introduces participants to the end-to-end process of developing AI tutors tailored to specific knowledge domains. Designed for both beginners and experienced developers, it offers a structured learning path to create intelligent, domain-specific AI systems. Throughout the course, students will learn to design, implement, and optimize AI tutors using cutting-edge tools like Python, Hugging Face, TensorFlow, and Llama, gaining hands-on experience with real-world applications.The course begins with foundational concepts, including data preprocessing and extracting insights from domain-specific datasets. Students will explore techniques for generating knowledge representations using methods like TF-IDF and embeddings, setting the stage for building effective question-answering (QA) systems. Advanced modules cover the integration of pre-trained models like BERT and GPT, as well as leveraging Llama for extractive summarization, enabling AI tutors to provide precise and context-aware responses.Participants will also delve into critical aspects of deployment, including creating scalable, reliable, and secure AI systems using cloud and local infrastructures. Emphasis is placed on monitoring and continuously improving AI tutors through feedback, retraining, and performance optimization.With practical coding exercises, step-by-step guidance, and insights into real-world use cases in education, healthcare, and law, this course equips students with the skills to transform learning experiences through AI. By the end, participants will be ready to deploy their own customized AI tutors for specific domains.

Overview

Section 1: AI tutor development

Lecture 1 Introduction to AI Tutors

Lecture 2 Extracting and Processing Domain-Specific Data

Lecture 3 Generating Knowledge Representations

Lecture 4 Leveraging Pre-Trained Models

Lecture 5 Developing a Question-Answering System

Lecture 6 Building a Retrieval-Based Knowledge System

Lecture 7 Using Llama to Enhance Extractive Summarization

Lecture 8 Optimizing AI Tutors for Performance

Lecture 9 Evaluation Metrics for QA Systems

Lecture 10 Deploying AI Tutors

Lecture 11 Monitoring and Improving AI Tutors

Lecture 12 Customizing AI Tutors for Specific Domains

Educators: professors, lecturers, teachers,Machine learning specialists,Students,Specialists in any domain like medicine, law, natural sciences working with large volumes of information,Journalists

[Image: 5rJFVyUb_o.jpg]