Master the most widely used Python library in machine learning with over 'N' practical recipes that cover core and advanced functions.
Key Features
- Solve complex business problems with data-driven approaches
- Master tools associated with developing predictive/prescriptive models
- Build robust ML pipelines for real-world applications
- Avoid common pitfalls in ML pipeline development
- Learn comprehensive, hands-on recipes tailored to Scikit-Learn version 1.5
- Master ML with real-world examples and Scikit-Learn 1.5 features
Book Description
Scikit-Learn is a powerful, open-source ML library for Python that provides simple and efficient tools for model development and deployment. Data scientists, ML engineers, and software developers learn Scikit-Learn because it offers a versatile, user-friendly framework for implementing a wide range of ML algorithms, enabling efficient development and deployment of predictive models in real-world applications. Scikit-learn Cookbook (3rd Edition) takes the reader on a journey from understanding the fundamentals of ML and data preprocessing, through implementing advanced algorithms and techniques, to deploying and optimizing ML models in production. Along the way, readers will explore practical, step-by-step recipes that cover everything from feature engineering and model selection to hyperparameter tuning and model evaluation, all using Scikit-Learn. By the end of this book, readers will have the knowledge and skills to confidently build, evaluate, and deploy sophisticated ML models using Scikit-Learn, enabling them to tackle a wide range of data-driven challenges.
What you will learn
- Implement a variety of ML algorithms, from basic classifiers to complex ensemble methods, using Scikit-Learn
- Perform data preprocessing, feature engineering, and model selection to prepare datasets for optimal model performance
- Optimize ML models through hyperparameter tuning and cross-validation techniques to improve accuracy and reliability
- Deploy ML models for scalable, maintainable real-world applications
- Evaluate and interpret models with advanced metrics and visualizations in Scikit-Learn
Who this book is for
Are you a data scientist, machine learning, or software development professional looking to deepen their understanding of advanced ML techniques? Then this book is for you! To get the most out of this book, you should have a proficiency in Python programming and familiarity with commonly used ML libraries (e.g., pandas, NumPy, matplotlib, sciPy, etc.) Additionally, an understanding of basic ML concepts, like linear regression, decision trees, and model evaluation metrics is helpful. Familiarity with mathematical concepts such as linear algebra, calculus, and probability is also invaluable.
