Python & Python Library with Artificial Intelligence

Python & Python Library with Artificial Intelligence is a practical and comprehensive guide to learning Artificial Intelligence using Python and its powerful ecosystem of libraries. The book starts with Python programming fundamentals and gradually progresses through data processing, 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, develop intelligent models, analyze text, and build modern AI applications.

The book combines concepts with practical Python examples, exercises, and real-world projects, making it useful for beginners, students, Python developers, and aspiring Artificial Intelligence professionals.

What You Will Learn
Python programming fundamentals
Variables, data types, operators, and control statements
Functions, modules, packages, and exception handling
Lists, tuples, sets, and dictionaries
Object-Oriented Programming with Python
File handling and JSON processing
Python libraries and package management
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
Feature engineering and feature selection
Scikit-Learn for Machine Learning
Linear Regression
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
Artificial Neural Networks
TensorFlow and Keras
PyTorch fundamentals
Activation functions and optimization
CNN, RNN, and LSTM
Natural Language Processing with Python
NLTK and spaCy
Text preprocessing and 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 intelligent applications with Python
Practical Artificial Intelligence projects