Description

Artificial Intelligence

Chapter 1: Introduction of AI

Chapter 2: The search Process

Chapter 3: Game Playing

Chapter 4: Knowledge Representation

Chapter 5: Robotics

Chapter 6: Natural Language Processing (NLP)

Chapter 7: Expert System

Chapter 8: Knowledge Engineering

Chapter 9: Successful Expert System

Chapter 1o: Logic Programming and Prolog

Chapter 11: AI Machines

Machine Learning

Chapter 1: Introduction to Machine Learning

• What is Machine Learning?
• Importance and Applications of Machine Learning
• Machine Learning vs. Traditional Programming

Chapter 2: Fundamentals of Python

• Python Basics
• Numbly and Pandas for Data Manipulation
• Matplotlib and Seaborn for Data Visualization

Chapter 3: Data Preprocessing

• Data Cleaning and Handling Missing Values
• Feature Scaling and Normalization
• Handling Categorical Data

Chapter 4: Supervised Learning

• Introduction to Supervised Learning
• Linear Regression
• Logistic Regression
• Decision Trees and Random Forests
• Support Vector Machines
• k-Nearest Neighbors (k-NN)

Chapter 5: Unsupervised Learning

• Introduction to Unsupervised Learning
• Clustering Algorithms (K-Means, Hierarchical Clustering, DBSCAN)
• Dimensionality Reduction (PCA)

Chapter 6: Natural Language Processing (NLP)

• Introduction to NLP
• Text Preprocessing
• Text Classification and Sentiment Analysis
• Named Entity Recognition (NER)
• Word Embeddings (Word2Vec, Glove)

Chapter 7: Case Studies and Practical Projects

• Real-world Machine Learning Projects
• Best Practices for Structuring and Documenting Projects

Chapter 8: Future Trends and Advanced Topics

• Current Trends in Machine Learning
• Advanced Topics (e.g., Generative Adversarial Networks, Autoencoders, Explainable AI)