Beginner's AI Course
This is a structured 2-week course designed for beginners to learn the basics of Artificial Intelligence (AI). This course introduces foundational concepts, explores different areas of AI, and provides hands-on experience with AI tools and techniques.
Beginner's AI Course
Duration:2 Weeks
Format: Daily Lessons with Assignments and Practical Exercises
Week 1: Introduction to Marketing
Day 1: Introduction to Artificial Intelligence
Lesson: Overview of Artificial Intelligence - Definition, History, and Applications
Reading: Introduction to AI (Chapter 1)
Assignment: Write a brief essay on how AI is impacting different industries (e.g., healthcare, finance, entertainment).
Day 2: Types of AI
Lesson: Narrow AI vs. General AI, and Different AI Categories (Reactive Machines, Limited Memory, Theory of Mind, Self-aware AI)
Reading: Understanding Different Types of AI
Exercise: Research and provide examples of narrow AI applications in daily life.
Day 3: Machine Learning Basics
Lesson: Introduction to Machine Learning - Key Concepts (Supervised, Unsupervised, Reinforcement Learning)
Reading: Basics of Machine Learning
Exercise: Use a simple online tool (e.g., Teachable Machine) to train a basic machine learning model.
Day 4: AI Algorithms and Techniques
Lesson: Overview of Common AI Algorithms (Decision Trees, Neural Networks, K-Means)
Reading: AI Algorithms Overview
Assignment: Explore a basic AI algorithm using a Python notebook or a visual tool (e.g., Google Colab).
Day 5: AI in Everyday Life
Lesson: AI in Daily Use - Virtual Assistants, Recommendation Systems, Image Recognition
Reading: AI in Everyday Applications
Exercise: Write a short report on how AI is used in a product or service you use regularly (e.g., Netflix recommendations, Google Photos).
Day 6: Ethical Considerations in AI
Lesson: Understanding AI Ethics - Bias, Privacy, and the Impact on Jobs
Reading: Ethical Challenges in AI
Exercise: Discuss a case study on AI ethics and propose solutions to address ethical concerns.
Day 7: Introduction to Natural Language Processing (NLP)
Lesson: Basics of NLP - How AI Understands and Processes Language
Reading: Introduction to NLP
Exercise: Experiment with a simple NLP tool (e.g., sentiment analysis using an online platform or basic coding).
Week 2: Hands-On AI and Applications
Day 8: AI Tools and Platforms
Lesson: Introduction to Popular AI Tools and Platforms (TensorFlow, IBM Watson, Google AI)
Reading: Overview of AI Development Platforms
Exercise: Explore a beginner-friendly AI tool or platform, setting up a basic project (e.g., Google Colab).
Day 9: Building AI Models
Lesson: Basic Steps in Building an AI Model - Data Collection, Training, Evaluation
Reading: How to Build Simple AI Models
Exercise: Use an AI tool to build and evaluate a simple model (e.g., training a basic image recognition model using a small dataset)
Day 10: Introduction to Robotics and AI
Lesson: Basics of Computer Vision - How AI Interprets Visual Data
Reading: SEO Fundamentals
Exercise: Perform keyword research using tools like Google Keyword Planner and optimize a webpage or blog post.
Day 11: Pay-Per-Click Advertising (PPC)
Lesson: Basics of PPC, Google Ads, and Ad Campaigns
Reading: Introduction to Computer Vision
Exercise: Experiment with a simple computer vision tool (e.g., using a pre-built model to recognize objects in images).
Day 12: AI in Business and Industry
Lesson: Applications of AI in Business - Automation, Data Analysis, Customer Service
Reading: AI in Business Applications
Exercise: Choose a specific industry and describe how AI is transforming that industry, providing real-world examples.
Day 13: Future of AI
Lesson: Emerging Trends in AI and Future Possibilities
Reading: The Future of AI
Assignment: Write a brief report on future AI trends that interest you, such as AI in healthcare, autonomous vehicles, or AI in creative fields.
Day 14: Review and Presentation
Lesson: RePrepare and present a simple AI project, demonstrating what you’ve learned over the course.
Activity: Prepare a presentation summarizing your marketing plan, research findings, and strategies.
Presentation: Showcase your project and receive feedback from peers or instructors.
Course Materials:
Access to AI tools and platforms (e.g., Google Colab, Teachable Machine, IBM Watson)
Articles, eBooks, and tutorials on AI concepts and techniques
Assessment:
Daily assignments and exercises
Final AI project presentation
This course provides a solid introduction to AI, blending theoretical understanding with hands-on experience, ensuring that beginners gain practical skills and insights into the world of AI.
