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Data Science  with Python

You're reading from   Data Science with Python Combine Python with machine learning principles to discover hidden patterns in raw data

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Product type Paperback
Published in Jul 2019
Publisher Packt
ISBN-13 9781838552862
Length 426 pages
Edition 1st Edition
Languages
Tools
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Authors (3):
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Rohan Chopra Rohan Chopra
Author Profile Icon Rohan Chopra
Rohan Chopra
Mohamed Noordeen Alaudeen Mohamed Noordeen Alaudeen
Author Profile Icon Mohamed Noordeen Alaudeen
Mohamed Noordeen Alaudeen
Aaron England Aaron England
Author Profile Icon Aaron England
Aaron England
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Toc

Table of Contents (10) Chapters Close

About the Book 1. Introduction to Data Science and Data Pre-Processing FREE CHAPTER 2. Data Visualization 3. Introduction to Machine Learning via Scikit-Learn 4. Dimensionality Reduction and Unsupervised Learning 5. Mastering Structured Data 6. Decoding Images 7. Processing Human Language 8. Tips and Tricks of the Trade 1. Appendix

Useful Tools and Tips

In this section, you will first learn the importance of different splits of the dataset. After that, you learn some tips that will come handy when you start working on datasets that haven't been processed before. Then come tools such as pandas profiling and TensorBoard, which will make your life easier by providing easy access to information. We will take a look at AutoML and how it can be used to get high-performance models without much manual effort. Finally, we will visualize our Keras model and export the model diagram to a file.

Train, Development, and Test Datasets

We briefly talked about train, development, and test datasets in the previous chapters. Here, we will delve deeper into the topic.

The training, or train set is a sample from the dataset, and we use this to create our machine learning models. The development, or dev set (also known as validation set), is a sample that helps us tune the hyperparameters of the created model. The testing or test set...

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