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

Mathur Ishita Johnston Benjamin

ISBN 10: 1789954924 / ISBN 13: 9781789954920

Verlag: Packt Publishing, Limited, 2019

Beschreibung

Print on Demand pp. 404.

Bestandsnummer des Verkäufers 393411534

Bibliografische Details

• Titel: Applied Supervised Learning with Python: Use scikit-learn to build predictive models from real-world datasets and prepare yourself for the future of machine learning
• Verlag: Packt Publishing, Limited
• Erscheinungsjahr: 2019
• Sprache: Englisch
• ISBN-10: 1789954924
• ISBN-13: 9781789954920
• Einband: Softcover
• Zustand: New

Über diesen Titel

Inhaltsangabe

Explore the exciting world of machine learning with the fastest growing technology in the world

Key Features

• Understand various machine learning concepts with real-world examples
• Implement a supervised machine learning pipeline from data ingestion to validation
• Gain insights into how you can use machine learning in everyday life

Book Description

Machine learning―the ability of a machine to give right answers based on input data―has revolutionized the way we do business. Applied Supervised Learning with Python provides a rich understanding of how you can apply machine learning 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 learning 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 learn how to build and train your own models using advanced techniques such as decision trees, ensemble modeling, validation, and error metrics. You'll also learn data visualization techniques using powerful Python libraries such as Matplotlib and Seaborn.

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 learning algorithms, but also be able to create some of your own!

What you will learn

• Understand the concept of supervised learning and its applications
• Implement common supervised learning algorithms using machine learning Python libraries
• Validate models using the k-fold technique
• Build your models with decision trees to get results effortlessly
• Use ensemble modeling techniques to improve the performance of your model
• Apply a variety of metrics to compare machine learning models

Who this book is for

Applied Supervised Learning with Python is for you if you want to gain a solid understanding of machine learning 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 Contents

1. Python Machine Learning Toolkit
2. Exploratory Data Analysis and Visualization
3. Regression Analysis
4. Classification
5. Ensemble Modeling
6. Model Evaluation

Über die Autorin bzw. den Autor

Benjamin Johnston is a senior data scientist for one of the world's leading data-driven medtech companies and is involved in the development of innovative digital solutions throughout the entire product development pathway, from problem definition to solution research and development, through to final deployment. He is currently completing his PhD in machine learning, specializing in image processing and deep convolutional neural networks. He has more than 10 years' experience in medical device design and development, working in a variety of technical roles, and holds first-class honors bachelor's degrees in both engineering and medical science from the University of Sydney, Australia.