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Building Data Science Solutions with Anaconda

Building Data Science Solutions with Anaconda

By : Meador
5 (12)
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Building Data Science Solutions with Anaconda

Building Data Science Solutions with Anaconda

5 (12)
By: Meador

Overview of this book

You might already know that there's a wealth of data science and machine learning resources available on the market, but what you might not know is how much is left out by most of these AI resources. This book not only covers everything you need to know about algorithm families but also ensures that you become an expert in everything, from the critical aspects of avoiding bias in data to model interpretability, which have now become must-have skills. In this book, you'll learn how using Anaconda as the easy button, can give you a complete view of the capabilities of tools such as conda, which includes how to specify new channels to pull in any package you want as well as discovering new open source tools at your disposal. You’ll also get a clear picture of how to evaluate which model to train and identify when they have become unusable due to drift. Finally, you’ll learn about the powerful yet simple techniques that you can use to explain how your model works. By the end of this book, you’ll feel confident using conda and Anaconda Navigator to manage dependencies and gain a thorough understanding of the end-to-end data science workflow.
Table of Contents (16 chapters)
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1
Part 1: The Data Science Landscape – Open Source to the Rescue
6
Part 2: Data Is the New Oil, Models Are the New Refineries
11
Part 3: Practical Examples and Applications

Summary

In this last chapter, we covered what is the final batch of skills you will need to get up to speed in becoming a data scientist using Anaconda as a base.

We started by seeing how scikit-learn pipelines let you take discrete parts of the data science workflow and create a cohesive unit in a much more elegant way by putting estimators together, like pieces of a puzzle. We also saw how these can include things such as your scalers and imputers, finally ending in an algorithm type.

We then understood that many of the arguments we have been using throughout this book, such as the depth of a random forest, are called hyperparameters and that they are a vital component to get right. Looking at GridSearchCV from sckit-learn, we put together a grid search over possible combinations, being careful to balance the speed of discovery with the best attributes.

Finally, we looked at the value of versioning our model with pickling and joblib. We packaged up our optimized model into...

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