Python & Python Library with Machine Learning by Aravind choudhary

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