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

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ASIN ‏ : ‎ B07KX3L3K2
Publisher ‏ : ‎ Packt Publishing; 1st edition (April 27, 2019)
Publication date ‏ : ‎ April 27, 2019
Language ‏ : ‎ English
File size ‏ : ‎ 22346 KB
Text-to-Speech ‏ : ‎ Enabled
Enhanced typesetting ‏ : ‎ Enabled
X-Ray ‏ : ‎ Not Enabled
Word Wise ‏ : ‎ Not Enabled
Print length ‏ : ‎ 406 pages

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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

In today’s data-driven world, being able to build predictive models is an essential skill for any data scientist or machine learning practitioner. With the help of scikit-learn, a powerful machine learning library in Python, you can easily build and train supervised learning models on real-world datasets.

In this post, we will explore how you can use scikit-learn to build predictive models from real-world datasets. By working through hands-on examples, you will learn how to preprocess data, select the right algorithm, train the model, and evaluate its performance.

Whether you are a beginner or an experienced data scientist, this post will help you sharpen your skills in applied supervised learning with Python. By mastering the concepts and techniques covered in this post, you will be better prepared for the future of machine learning and data science.

So, if you are ready to take your machine learning skills to the next level, stay tuned for our upcoming posts on applied supervised learning with Python using scikit-learn. Let’s dive into the exciting world of predictive modeling and unleash the power of machine learning in real-world applications.
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