Python & Python Library with Machine Learning is a practical and comprehensive guide designed to help readers learn Python programming, popular Python libraries, data analysis, and Machine Learning from the fundamentals to advanced concepts.

The book begins with Python programming fundamentals and gradually introduces essential libraries such as NumPy, Pandas, Matplotlib, Seaborn, and Scikit-Learn. Readers will learn how to work with real-world datasets, clean and preprocess data, perform exploratory data analysis, create meaningful visualizations, and develop Machine Learning models.

The book provides a step-by-step approach to Supervised and Unsupervised Learning, covering popular algorithms such as Linear Regression, Logistic Regression, KNN, Decision Trees, Random Forest, SVM, Naive Bayes, K-Means, and PCA. Practical examples and projects help readers understand how these concepts are applied to real-world problems.

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 in Python
File handling and JSON processing
Python libraries and package management
NumPy for numerical computing
NumPy arrays, indexing, slicing, and broadcasting
Mathematical and statistical operations with NumPy
Pandas Series and DataFrames
Reading CSV, Excel, JSON, and other datasets
Data cleaning and preprocessing with Pandas
Handling missing, duplicate, and inconsistent data
Data filtering, transformation, grouping, and merging
Exploratory Data Analysis (EDA)
Data visualization with Matplotlib
Advanced visualization with Seaborn
Introduction to Machine Learning
Types and workflow of Machine Learning
Data preprocessing for Machine Learning
Feature engineering and feature selection
Encoding and feature scaling
Training and testing datasets
Supervised Learning
Linear Regression
Multiple Linear Regression
Logistic Regression
K-Nearest Neighbors (KNN)
Decision Trees
Random Forest
Support Vector Machine (SVM)
Naive Bayes
Unsupervised Learning
K-Means Clustering
Hierarchical Clustering
Principal Component Analysis (PCA)
Model evaluation and performance metrics
Confusion Matrix
Accuracy, Precision, Recall, and F1-Score
MAE, MSE, RMSE, and R²
Cross-validation
Overfitting and underfitting
Hyperparameter tuning
Grid Search and Randomized Search
Ensemble Learning
Scikit-Learn for Machine Learning
Saving and loading trained models
Building predictive Machine Learning applications
Introduction to NLP and Deep Learning
Working with real-world datasets
Practical Machine Learning projects