Python Library with Artificial Intelligence is a practical and comprehensive guide to using powerful Python libraries for developing Artificial Intelligence applications. The book begins with Python programming and essential libraries and gradually progresses toward Machine Learning, Deep Learning, Natural Language Processing, and Generative AI.

Readers will learn how to use popular Python libraries such as NumPy, Pandas, Matplotlib, Seaborn, Scikit-Learn, TensorFlow, PyTorch, NLTK, spaCy, and Hugging Face to process data, build intelligent models, analyze text, and develop modern AI applications.

The book combines fundamental concepts with practical Python examples and real-world projects, making it suitable for students, beginners, Python developers, and aspiring AI professionals.

What You Will Learn
Python programming fundamentals for AI
Python modules, packages, and libraries
NumPy for numerical computing
Arrays, indexing, slicing, and broadcasting
Pandas for data analysis
Series and DataFrames
Data cleaning and preprocessing
CSV, Excel, JSON, and dataset handling
Matplotlib and Seaborn for data visualization
Introduction to Artificial Intelligence
AI concepts, applications, and workflows
Machine Learning fundamentals
Supervised and Unsupervised Learning
Data preprocessing and feature engineering
Scikit-Learn for Machine Learning
Linear and Logistic Regression
K-Nearest Neighbors
Decision Trees and Random Forest
Support Vector Machines
Naive Bayes
K-Means Clustering
Principal Component Analysis
Model evaluation and performance metrics
Cross-validation and hyperparameter tuning
Overfitting and underfitting
Introduction to Deep Learning
Neural Networks and Artificial Neural Networks
TensorFlow and Keras
PyTorch fundamentals
Activation functions and optimization
CNN, RNN, and LSTM fundamentals
Natural Language Processing with Python
NLTK and spaCy
Text preprocessing and text classification
Sentiment analysis
Introduction to Transformers
Hugging Face libraries and models
Large Language Models (LLMs)
Generative AI fundamentals
Prompt Engineering
AI-powered text generation
Embeddings and semantic search
Retrieval-Augmented Generation (RAG)
Introduction to AI Agents
Tool calling and AI automation
Working with AI APIs and Python libraries
Model saving, loading, and deployment
Building real-world AI applications
Practical Artificial Intelligence projects