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

Publisher:Packt Publishing Limited

By: Benjamin Johnston and Ishita Mathur

Paid access

|Apr 2019

E-Book €27.99Institutions €120.95

Description

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.

Python Machine Learning Toolkit
Exploratory Data Analysis and Visualization
Regression Analysis
Classification
Ensemble Modeling
Model Evaluation

Table of contents

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PDF ISBN: 978-1-78995-583-5

Publisher: Packt Publishing Limited

Publication date: 2019

Language: English

Pages: 404

Related subjects:

Computer sciences,

Computer sciences, other

Applied Supervised Learning with Python

Key Features

Book Description

What you will learn

Who this book is for

Table of Contents

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