Course Title:
AI Tools and ChatGPT: Practical Applications for Today’s World
Course Objective:
The goal of this course is to familiarize students with the tools and techniques needed to
effectively use AI tools, with a strong focus on ChatGPT. The course will cover the theory
behind these technologies, their practical applications, and provide hands-on experience using
the tools in real-world scenarios.
Week 1: Introduction to AI and AI Tools
Session 1: Introduction to Artificial Intelligence
o Definition and key concepts of AI
o Evolution of AI technologies
o AI’s impact on industries (e.g., healthcare, finance, marketing)
Session 2: Overview of AI Tools
o Introduction to popular AI tools
o Understanding AI capabilities: NLP, computer vision, data processing, etc.
o Tools used in the AI ecosystem (TensorFlow, OpenAI, Hugging Face, etc.)
Week 2: Natural Language Processing (NLP) and ChatGPT Basics
Session 3: Introduction to NLP and ChatGPT
o What is NLP and how does it relate to AI?
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o ChatGPT’s foundational architecture (GPT, transformers, etc.)
o Overview of different versions of ChatGPT and GPT models
Session 4: Setting up and Using ChatGPT for Beginners
o Signing up for OpenAI/ChatGPT access
o Basic interactions and prompt engineering techniques
o Hands-on: Create and test simple ChatGPT queries
Week 3: Advanced ChatGPT Features and Use Cases
Session 5: Advanced ChatGPT Features
o Fine-tuning ChatGPT responses with temperature and max tokens
o Context management in long conversations
o Roleplaying and persona-based interactions
Session 6: ChatGPT for Content Creation
o Using ChatGPT for writing blogs, articles, and marketing content
o ChatGPT for creative writing: poetry, stories, and brainstorming
o Hands-on: Generate content based on custom prompts
Week 4: Integrating AI Tools for Business Solutions
Session 7: AI in Business Automation and Productivity
o Using ChatGPT for customer service (chatbots, support systems)
o Automating workflows with AI tools (Zapier, Integromat)
o AI for decision-making and data analysis (AI-driven analytics tools)
Session 8: Building AI-Powered Applications
o Using APIs and third-party tools for integration (OpenAI API, GPT-3)
o Case study: Developing a chatbot for customer interaction
o Hands-on: Integrating AI into a basic application
Week 5: Ethics and Challenges of AI
Session 9: Ethical Considerations in AI
o Privacy concerns and data ethics
o Bias in AI algorithms and how to mitigate it
o Ensuring transparency and accountability in AI-driven decisions
Session 10: Limitations and Challenges in Using AI Tools
o Common challenges faced when using AI tools (accuracy, resource consumption)
o Practical tips for optimizing and troubleshooting
o Discussing the future of AI in various sectors
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Week 6: Hands-on Projects and Future Trends
Session 11: Practical Project: AI in Action
o Design a project that uses AI tools (ChatGPT or other tools)
o Presentation of projects and real-world use case development
o Peer review and feedback
Session 12: Exploring the Future of AI and ChatGPT
o Upcoming trends in AI: GPT-4, GPT-5, and beyond
o Innovations in AI tools and their potential impact
o Preparing for the future: Lifelong learning and staying up-to-date with AI
Assessments:
Quizzes: At the end of each week to reinforce learning.
Hands-on Projects: Real-world assignments like creating an AI-powered chatbot,
writing content with ChatGPT, or building an AI-driven app.
Final Project: A comprehensive project incorporating AI tools and ChatGPT,
showcasing your learning.
Learning Materials:
Readings: Articles, research papers, and AI-related news
Tools & Resources: Access to OpenAI’s GPT-3/ChatGPT, AI toolkits, API access
Supplementary: Video tutorials and demonstrations
Certification:
Upon successful completion of the course, participants will receive a Certificate of Completion.
Prerequisites:
Basic computer skills
Familiarity with the internet and digital tools
No programming experience required, though basic programming knowledge may be
helpful for more advanced use cases
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Course Title:
Cloud Computing with AWS: A Comprehensive Guide for Beginners and Professionals
Duration:
8 weeks (16 sessions)
Mode of Delivery:
Online/Hybrid
Course Level:
Beginner to Intermediate
Course Objective:
This course aims to provide participants with a deep understanding of cloud computing concepts,
AWS services, and how to leverage AWS for building scalable, reliable, and cost-effective
applications. By the end of the course, students will be equipped to deploy and manage
applications in AWS and be prepared for the AWS Certified Solutions Architect – Associate
exam.
Week 1: Introduction to Cloud Computing and AWS
Session 1: Introduction to Cloud Computing
o What is cloud computing?
o Cloud computing models: IaaS, PaaS, SaaS
o Benefits and challenges of cloud computing
o Cloud deployment models: Public, Private, Hybrid
Session 2: Introduction to AWS
o Overview of Amazon Web Services (AWS)
o AWS global infrastructure: Regions, Availability Zones, and Edge Locations
o Introduction to AWS Management Console
o Key AWS services overview
Week 2: AWS Core Services – Compute and Networking
Session 3: Amazon EC2 (Elastic Compute Cloud)
o Introduction to EC2 instances and types
o Launching and configuring EC2 instances
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o Security groups, key pairs, and SSH access
o EC2 instance lifecycle and cost management
Session 4: Virtual Private Cloud (VPC)
o Creating a VPC: Subnets, Route Tables, and Internet Gateway
o Security in VPC: NACLs, Security Groups, and VPNs
o Elastic Load Balancer (ELB) and Auto Scaling in VPC
o Hands-on: Setting up a basic VPC and EC2 instances
Week 3: AWS Storage Solutions
Session 5: Amazon S3 (Simple Storage Service)
o Introduction to S3 and its features
o Creating and managing S3 buckets
o S3 security: IAM roles, Bucket Policies, and Encryption
o Versioning and lifecycle management in S3
Session 6: Amazon EBS (Elastic Block Store) and Glacier
o Amazon EBS: Volumes, snapshots, and performance considerations
o Amazon Glacier for archival storage
o Hands-on: Configuring EBS volumes with EC2 and using S3
Week 4: AWS Database Solutions
Session 7: Amazon RDS (Relational Database Service)
o Introduction to RDS and supported database engines
o Setting up RDS instances and databases
o RDS backups, monitoring, and high availability
o Amazon Aurora: A high-performance relational database service
Session 8: NoSQL with DynamoDB
o Introduction to DynamoDB and its use cases
o Setting up DynamoDB tables and managing data
o Querying and indexing in DynamoDB
o Hands-on: Setting up RDS and DynamoDB for a sample project
Week 5: AWS Security and Identity Management
Session 9: IAM (Identity and Access Management)
o Introduction to IAM: Users, Groups, and Roles
o IAM policies and permissions (Managed Policies vs. Custom Policies)
o Multi-Factor Authentication (MFA)
o Hands-on: Setting up IAM users and roles with specific permissions
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Session 10: AWS Security Best Practices
o Best practices for securing AWS resources
o CloudTrail for auditing and CloudWatch for monitoring
o AWS Shield and WAF (Web Application Firewall)
o Managing data security and compliance
Week 6: Monitoring and Automation in AWS
Session 11: AWS Monitoring and Logging
o Introduction to CloudWatch for monitoring EC2 instances, VPC, and S3
o CloudWatch Logs and CloudWatch Alarms
o Setting up CloudWatch dashboards and metrics
o Hands-on: Creating CloudWatch alarms and custom logs
Session 12: Automation with AWS
o AWS Lambda and serverless computing
o AWS CloudFormation for Infrastructure as Code (IaC)
o Introduction to AWS Systems Manager and Automation
o Hands-on: Automating deployment with CloudFormation and Lambda
Week 7: AWS Architecting and High Availability
Session 13: Designing Scalable and Fault-Tolerant Architectures
o High availability vs. fault tolerance
o Designing for scalability: Load balancing, Auto Scaling, and Elastic IPs
o Multi-region and multi-AZ deployment strategies
o Best practices for building resilient AWS architectures
Session 14: AWS Cost Management and Optimization
o AWS pricing models: Pay-as-you-go, Reserved, and Spot Instances
o Cost Explorer and AWS Budgets
o Best practices for cost optimization and resource tagging
o Hands-on: Estimating costs with the AWS Pricing Calculator
Week 8: Advanced AWS Services and Final Project
Session 15: Advanced AWS Services Overview
o Introduction to AWS Elastic Beanstalk
o AWS AppSync for GraphQL and mobile apps
o Amazon Redshift for Data Warehousing
o AWS SageMaker for Machine Learning
Session 16: Final Project and Exam Preparation
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o Building an end-to-end solution with AWS services
o Hands-on project: Deploying a scalable web application
o Exam preparation: Key AWS certification exam topics and tips
Assessments:
Quizzes: At the end of each week to reinforce learning.
Hands-on Labs: Real-world exercises using AWS Management Console.
Final Project: Design and deploy a cloud-based application using AWS services.
Exam Preparation: Mock exam covering key AWS services and architecture best
practices.
Learning Materials:
AWS Documentation and whitepapers
Training Resources: AWS training videos and labs
Readings: Articles on cloud computing concepts and AWS best practices
Supplementary: Access to AWS Free Tier for hands-on experience
Certification:
Upon successful completion of the course and final project, participants will receive a
Certificate of Completion. Students will also be well-prepared for the AWS Certified
Solutions Architect – Associate exam.
Prerequisites:
Basic understanding of IT infrastructure (networking, storage, databases)
Familiarity with the internet, web applications, and services
No programming experience required, though knowledge of basic scripting can be
beneficial
Duration:
6 weeks (12 sessions)
Mode of Delivery:
Online/Hybrid
Course Level:
Intermediate/Beginner (Basic programming knowledge is helpful but not required)
