AI for Python

Section 1: What is AI?

Introduction to AI

• Definition and scope
• History and evolution of AI
• Applications of AI in various fields

Types of AI

• Narrow AI vs. General AI
• Strong AI vs. Weak AI

Section 2: From Machine Learning to AI

Understanding Machine Learning (ML)

• Definition and types of ML (Supervised, Unsupervised, Reinforcement Learning)
• Key concepts: Algorithms, Models, Training, and Testing

Transitioning from ML to AI

• Integrating ML models into AI systems
• Examples of ML applications in AI

Section 3: How AI Works

Core Concepts in AI

• Neural Networks and Deep Learning
• Natural Language Processing (NLP)
• Computer Vision

AI Methodologies and Approaches

• Rule-based systems
• Heuristic search
• Genetic algorithms

Section 4: Major Python Libraries for AI

Introduction to Key Libraries

• NumPy, Pandas, and Matplotlib for data manipulation and visualization
• Scikit-learn for machine learning
• TensorFlow and Keras for deep learning
• NLTK and SpaCy for natural language processing

Putting the Libraries into Action

• Installation
• Basic usage of these libraries

Section 5: How ChatGPT Works

Overview of ChatGPT

• The architecture of GPT models
• Training data and processes

Practical Applications of ChatGPT

• Uses in customer service, content creation, and more

Ethics

• Important ethical considerations and challenges

Section 6: Master Techniques Encompassing Text Preprocessing

Importance of Text Preprocessing in NLP

• Tokenization, stemming, and lemmatization
• Removing stop words and noise

Hands-On Exercises with Python Libraries

• NLTK and SpaCy

Section 7: Dive Deep Into the World of Reinforcement Learning

Fundamentals of Reinforcement Learning (RL)

• Key concepts: Agents, Environments, Rewards, and Policies
• Exploration vs. Exploitation dilemma

Algorithms in RL

• Q-Learning, Deep Q-Networks (DQN)
• Practical examples and coding exercises

Section 8: Build Your Own AI App

Overall Structure of the App and APIs

• Planning and designing an AI application
• Understanding and integrating APIs

Build Components of the Code

• Writing and organizing code for AI functionalities
• Implementing machine learning models

Gather Information for Reinforced Learning

• Data collection and preprocessing
• Setting up the RL environment

Gather Images Based on Results

• Using computer vision techniques to process images
• Integration of image processing results into the app

Add Features, Maps, Images, etc.

• Enhancing the app with additional features
• Utilizing external libraries for maps and image handling

How to Deploy the App

• Overview of deployment options
• Step-by-step guide to deploying the app on cloud platforms (e.g. AWS, Google Cloud, Heroku)
• Best practices for maintaining and scaling the app