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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

What do you get with eBook?

Instant access to your Digital eBook purchase

Download this book in EPUB and PDF formats

Access this title in our online reader with advanced features

DRM FREE - Read whenever, wherever and however you want

AI Assistant (beta) to help accelerate your learning

Frequently bought together

€38.99

Building Data Science Solutions with Anaconda

€35.99

Machine Learning with PyTorch and Scikit-Learn

€41.99

Total € 116.97

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Paul Burnett Dec 04, 2023

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.

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Yiqiao Yin Jul 28, 2022

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!

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Jerimiah Jun 21, 2022

Whether you are someone wanting to get started with Data Science, or an experienced practitioner who has been away from the field for a while (like me), this book provides you everything you need to know. Each chapter builds on the last, but they are also relatively independent of each other, so if you need to quickly brush up on a specific subject/method (like versioning your ML models), it's easy to do so. The metaphors used throughout the book are vivid and memorable, to help the reader get a better intuition for complicated concepts. The projects and examples use realistic scenarios that do a great job of walking the reader through the code and steps to building an ML model in the same way they would do them in real life. I'll definitely be keeping this book handy as a reference for when I ever need to work on an ML project in the future!

Karl Weinmeister Jul 03, 2022

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.

Jamie Vernon May 27, 2022

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

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

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