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Building Data Science Solutions with Anaconda
Building Data Science Solutions with Anaconda

Building Data Science Solutions with Anaconda: A comprehensive starter guide to building robust and complete models

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Key benefits

  • Learn from an AI patent-holding engineering manager with deep experience in Anaconda tools and OSS
  • Get to grips with critical aspects of data science such as bias in datasets and interpretability of models
  • Gain a deeper understanding of the AI/ML landscape through real-world examples and practical analogies

Description

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.

Who is this book for?

If you’re a data analyst or data science professional looking to make the most of Anaconda’s capabilities and deepen your understanding of data science workflows, then this book is for you. You don’t need any prior experience with Anaconda, but a working knowledge of Python and data science basics is a must.

What you will learn

  • Install packages and create virtual environments using conda
  • Understand the landscape of open source software and assess new tools
  • Use scikit-learn to train and evaluate model approaches
  • Detect bias types in your data and what you can do to prevent it
  • Grow your skillset with tools such as NumPy, pandas, and Jupyter Notebooks
  • Solve common dataset issues, such as imbalanced and missing data
  • Use LIME and SHAP to interpret and explain black-box models

Product Details

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Publication date, Length, Edition, Language, ISBN-13
Publication date : May 27, 2022
Length: 330 pages
Edition : 1st
Language : English
ISBN-13 : 9781800568785
Category :
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What do you get with a Packt Subscription?

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Product feature icon Innovative learning tools, including AI book assistants, code context explainers, and text-to-speech.
Product feature icon Thousands of reference materials covering every tech concept you need to stay up to date.
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Product Details

Publication date : May 27, 2022
Length: 330 pages
Edition : 1st
Language : English
ISBN-13 : 9781800568785
Category :
Concepts :
Tools :

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Frequently bought together


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Total £ 116.97
Machine Learning with PyTorch and Scikit-Learn
£41.99
The Pandas Workshop
£38.99
Building Data Science Solutions with Anaconda
£35.99
Total £ 116.97 Stars icon
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Paul Burnett Dec 04, 2023
Full star icon Full star icon Full star icon Full star icon Full star icon 5
You books and videos are intelligent and cover key concepts. I tend to bounce around multiple authors on relevant ai themes. I look at the library modules tools and the power of the information your site gives me. 5 star to all you team. It been a pleasure learning with you. Paul burnett Biomedical eng. and data software programmer in ai.
Feefo Verified review Feefo
Yiqiao Yin Jul 28, 2022
Full star icon Full star icon Full star icon Full star icon Full star icon 5
It's great reading this project. I feel like I come from a unique place because I did not come from a place where I find downloading packages and create environments helpful. Hence, this book is really helping me to reshape some of my views. I also found it valuable that this book is able to provide some of the fundamental building block in conda.In conda, I typically create an environment I like and fire up a jupyter lab. Then I do my dev work in there. For me personally, this is a pretty efficient workflow. Hence, it's really a overview for me for the first 6 chapters to review some of these concepts. However, if you are intro level, this book is a great start. It depends on your level really.In addition, as my YouTube suggested. The book goes above and beyond to introduce something on top of machine learning. Coming from statistics background, I really appreciate that the author discusses biases and variances. Moreover, the later chapters discussed shap and lime value which is also something I investigated during my graduate program.Overall, I really enjoyed reading this book and I recommend to all others to read this book too!
Amazon Verified review Amazon
Jamie Vernon May 27, 2022
Full star icon Full star icon Full star icon Full star icon Full star icon 5
The authors really knocked it out of the park with this one. As someone with little experience with AI or Am, this was a great read. Can’t wait to get deeper into the space.. 5 stars all day
Amazon Verified review Amazon
Karl Weinmeister Jul 03, 2022
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Dan's book covers an end-to-end path from setting up your environment to building a model. Readers will see how to apply popular open-source packages for data science, in particular pandas and scikit-learn. The book also introduces conceptual topics such as how to select an appropriate ML model type. I would highly recommend this book for emerging data scientists who want to get up-to-speed quickly on common concepts and tools.
Amazon Verified review Amazon
Alex w. May 30, 2022
Full star icon Full star icon Full star icon Full star icon Full star icon 5
This book is really amazing if you are wanting to actually start with understanding and using the data science process. Each chapter builds on itself for different concepts but still seems like they can be self contained units that I could jump around if I was more interested in particular subjects. There was maybe one place that any math was shown, which I liked as I was looking for something more direct.
Amazon Verified review Amazon
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