Learn machine learning with Python through practical examples, real datasets, and complete workflows.
Machine learning becomes easier when you understand how the entire process works—from preparing data and exploring patterns to training models, evaluating results, improving performance, and building complete projects.
Machine Learning in Practice with Python is a practical guide designed to help you develop a strong foundation in machine learning while working with Python and widely used machine-learning tools.
Inside this book, you will learn how to:
- Understand the foundations of artificial intelligence and machine learning
- Use Python for practical machine-learning workflows
- Work with NumPy and Pandas for data preparation and analysis
- Clean, transform, and prepare datasets for machine learning
- Explore datasets using statistical analysis and visualization
- Build regression models for numerical prediction
- Build classification models for binary and multiclass problems
- Work with linear regression, logistic regression, K-nearest neighbors, decision trees, random forests, and boosting methods
- Apply unsupervised learning techniques such as K-Means and hierarchical clustering
- Use dimensionality reduction with Principal Component Analysis (PCA)
- Perform feature engineering to improve machine-learning models
- Apply encoding, scaling, transformations, and feature selection
- Understand and prevent data leakage
- Evaluate models using appropriate performance metrics
- Use accuracy, precision, recall, F1 score, MAE, MSE, RMSE, Rē, ROC analysis, and precision-recall analysis
- Apply cross-validation and hyperparameter tuning
- Compare different machine-learning models
- Analyze model errors and understand model limitations
- Save trained models and create reproducible workflows
- Build practical machine-learning projects using Python
- Work through complete workflows from problem definition and data preparation to model evaluation
The book focuses on practical machine-learning skills rather than memorizing algorithms. Concepts are introduced step by step and connected to Python code, datasets, model outputs, visualizations, evaluation techniques, and practical project workflows.
Whether you are beginning your journey into machine learning, strengthening your Python skills, studying data science, or looking for a practical reference for classical machine-learning projects, this book provides a structured path from fundamentals to real-world implementation.
Learn the concepts. Work with data. Train models. Evaluate results. Improve your workflow. Build practical machine-learning projects with Python.
