Artificial Intelligence

Day 1: Introduction to Artificial Intelligence

• Define what is Artificial Intelligence
• Introduce types of AI (e.g. supervised learning, unsupervised learning, reinforcement learning, etc.)
• Discuss real-world applications of AI

Day 2: Introduction to Machine Learning

• Define Machine Learning
• Introduce types of machine learning (e.g. supervised learning, unsupervised learning, reinforcement learning, etc.)
• Discuss real-world applications of machine learning

Day 3: Understanding Data

• Explain what data is
• Discuss types of data (e.g. structured, unstructured)
• Introduce concepts such as data preprocessing, data cleaning, data visualization, etc.

Day 4: Supervised Learning

• Define Supervised Learning
• Introduce classification and regression problems
• Discuss algorithms such as linear regression, logistic regression, decision trees, and K-nearest neighbor

Day 5: Hands-on Supervised Learning with Teachable Machine

• Introduce Google's Teachable Machine
• Walk Students through the process of creating a simple image classification model

• Day 6: Unsupervised Learning

• Define Unsupervised Learning
• Introduce clustering and association problems
• Discuss algorithms such as K-means and Apriori

Day 7: Hands-on Unsupervised Learning with Teachable Machine

• Walk Students through the process of creating a simple clustering model

Day 8: Reinforcement Learning

• Define Reinforcement Learning
• ntroduce the concept of rewards and penalties
• Discuss real-world applications such as robotics and game-playing

Day 9: Hands-on Reinforcement Learning with Teachable Machine

• Walk Students through the process of creating a simple game-playing model

Day 10: Introduction to Neural Networks

• Define Neural Networks
• Introduce the concept of neurons and layers
• Discuss types of neural networks (e.g. feedforward, recurrent, convolutional)

Day 11: Convolutional Neural Networks

• Introduce Convolutional Neural Networks (CNNs) Discuss real-world applications such as image and video recognition

Day 12: Hands-on CNNs with Teachable Machine

• Walk Students through the process of creating a simple image recognition model using CNNs

Day 13: Recurrent Neural Networks

• Introduce Recurrent Neural Networks (RNNs)
• Discuss real-world applications such as natural language processing and speech recognition

Day 14: Hands-on RNNs with Teachable Machine

• Walk Students through the process of creating a simple text classification model using RNNs

Day 15: Ethics in AI

• Introduce the concept of ethical considerations in AI
• Discuss real-world examples of ethical dilemmas in AI
• Encourage discussion on the importance of ethical considerations in AI development

Day 16: Bias in AI

• Define Bias in AI
• Discuss real-world examples of bias in AI
• Encourage discussion on the importance of detecting and mitigating bias in AI models

Day 17: Introduction to Natural Language Processing

• Define Natural Language Processing (NLP)
• Introduce concepts such as tokenization, stemming, and lemmatization

Day 18: Sentiment Analysis

• Introduce Sentiment Analysis
• Discuss real-world applications such as customer feedback analysis

Day 19: Hands-on Sentiment Analysis with Teachable Machine

• Walk Students through the process of creating a simple sentiment analysis model

Day 20: Image Captioning

• Introduce Image Captioning
• Discuss real-world applications such as assisting visually impaired individuals

Day 21: Hands-on Image Captioning with Teachable Machine

• Walk Students through the process of creating a simple image captioning model

Day 22: Introduction to Reinforcement Learning with OpenAI Gym

Day 23: Hands-on Reinforcement Learning with OpenAI Gym

• Introduce OpenAI Gym
• Walk Students through the process of creating a simple reinforcement learning environment

Day 24: Deep Learning Frameworks

• Introduce popular deep learning frameworks such as TensorFlow and PyTorch
• Discuss their pros and co#### 25: Building Neural Networks with TensorFlow
• Introduce TensorFlow
• Walk Students through the process of building a simple neural network

Day 26: Building Neural Networks with PyTorch

• Introduce PyTorch
• Walk Students through the process of building a simple neural network

Day 27: Transfer Learning

• Define Transfer Learning
• Discuss real-world applications such as image recognition and natural language processing

Day 28: Hands-on Transfer Learning with Teachable Machine

• Walk Students through the process of creating a simple transfer learning model

Day 29: Final Project

• Encourage Students to apply what they have learned throughout the course to create their own AI project
• Provide guidance and support as needed

Day 30: Presentation Day

• Encourage Students to present their final project to the class
• Provide constructive feedback and celebrate their achievements