Applied Supervised Leaing with Python:Use scikit-lea to build predictive models from real-world datasets and prepare yourself for the future of machine leaing

by: Benjamin Johnston (Author),Ishita Mathur(Author)

Publisher: Packt Publishing

Publication Date: 2019/4/27

Language: English

Print Length: 404 pages

ISBN-10: 1789954924

ISBN-13: 9781789954920

Book Description

Explore the exciting world of machine leaing with the fastest growing technology in the worldKey FeaturesUnderstand various machine leaing concepts with real-world examplesImplement a supervised machine leaing pipeline from data ingestion to validationGain insights into how you can use machine leaing in everyday lifeBook DescriptionMachine leaing-the ability of a machine to give right answers based on input data-has revolutionized the way we do business. Applied Supervised Leaing with Python provides a rich understanding of how you can apply machine leaing techniques in your data science projects using Python. You'll explore Jupyter Notebooks, the technology used commonly in academic and commercial circles with in-line code running support.With the help of fun examples, you'll gain experience working on the Python machine leaing toolkit-from performing basic data cleaning and processing to working with a range of regression and classification algorithms. Once you've grasped the basics, you'll lea how to build and train your own models using advanced techniques such as decision trees, ensemble modeling, validation, and error metrics. You'll also lea data visualization techniques using powerful Python libraries such as Matplotlib and Seabo. This book also covers ensemble modeling and random forest classifiers along with other methods for combining results from multiple models, and concludes by delving into cross-validation to test your algorithm and check how well the model works on unseen data.By the end of this book, you'll be equipped to not only work with machine leaing algorithms, but also be able to create some of your own!What you will leaUnderstand the concept of supervised leaing and its applicationsImplement common supervised leaing algorithms using machine leaing Python librariesValidate models using the k-fold techniqueBuild your models with decision trees to get results effortlesslyUse ensemble modeling techniques to improve the performance of your modelApply a variety of metrics to compare machine leaing modelsWho this book is forApplied Supervised Leaing with Python is for you if you want to gain a solid understanding of machine leaing using Python. It'll help if you to have some experience in any functional or object-oriented language and a basic understanding of Python libraries and expressions, such as arrays and dictionaries.Table of ContentsPython Machine Leaing ToolkitExploratory Data Analysis and VisualizationRegression AnalysisClassificationEnsemble ModelingModel Evaluation

About the Author

Explore the exciting world of machine leaing with the fastest growing technology in the worldKey FeaturesUnderstand various machine leaing concepts with real-world examplesImplement a supervised machine leaing pipeline from data ingestion to validationGain insights into how you can use machine leaing in everyday lifeBook DescriptionMachine leaing-the ability of a machine to give right answers based on input data-has revolutionized the way we do business. Applied Supervised Leaing with Python provides a rich understanding of how you can apply machine leaing techniques in your data science projects using Python. You'll explore Jupyter Notebooks, the technology used commonly in academic and commercial circles with in-line code running support.With the help of fun examples, you'll gain experience working on the Python machine leaing toolkit-from performing basic data cleaning and processing to working with a range of regression and classification algorithms. Once you've grasped the basics, you'll lea how to build and train your own models using advanced techniques such as decision trees, ensemble modeling, validation, and error metrics. You'll also lea data visualization techniques using powerful Python libraries such as Matplotlib and Seabo. This book also covers ensemble modeling and random forest classifiers along with other methods for combining results from multiple models, and concludes by delving into cross-validation to test your algorithm and check how well the model works on unseen data.By the end of this book, you'll be equipped to not only work with machine leaing algorithms, but also be able to create some of your own!What you will leaUnderstand the concept of supervised leaing and its applicationsImplement common supervised leaing algorithms using machine leaing Python librariesValidate models using the k-fold techniqueBuild your models with decision trees to get results effortlesslyUse ensemble modeling techniques to improve the performance of your modelApply a variety of metrics to compare machine leaing modelsWho this book is forApplied Supervised Leaing with Python is for you if you want to gain a solid understanding of machine leaing using Python. It'll help if you to have some experience in any functional or object-oriented language and a basic understanding of Python libraries and expressions, such as arrays and dictionaries.Table of ContentsPython Machine Leaing ToolkitExploratory Data Analysis and VisualizationRegression AnalysisClassificationEnsemble ModelingModel Evaluation