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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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Building Data Science Solutions with Anaconda

Chapter 1: Understanding the AI/ML landscape

In this opening chapter, we'll give you a little appreciation and context to the why behind AI and machine learning (ML). The only data we have comes from the past, and using that will help us predict the future. We'll take a look at the massive amount of data that is coming into the world today and try to get a sense of the scale of what we have to work with.

The main goal of any type of software or algorithm is to solve business and real-world problems, so we'll also take a look at how the applications take shape. If we use a food analogy, data would be the ingredients, the algorithm would be the chef, and the meal created would be the model. You'll learn about the most commonly used types of models within the broader landscape and how to know what to use.

There are a huge number of tools that you could use as a data scientist, and so we will also touch on how you can use solutions such as those provided by Anaconda to be able to do the actual work you want to and be able to take action as your models grow stale (which they will). By the end of this chapter, you'll have an understanding of the value and landscape of AI and be able to jumpstart any project that you want to build.

AI is the most exciting technology of our age and, throughout this first chapter, these topics will give you the solid foundation that we'll build upon through the rest of the book. These are all key concepts that will be commonplace in your day-to-day journey, and which you'll find to be invaluable in accomplishing what you need to.

In this chapter, we're going to cover the following main topics:

  • Understanding the current state of AI and ML
  • Understanding the massive generation of new data
  • How to create business value with AI
  • Understanding the main types of ML models
  • Dealing with out-of-date models
  • Installing packages with Anaconda
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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 : May 27, 2022
Length: 330 pages
Edition : 1st
Language : English
ISBN-13 : 9781800561564
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Publication date : May 27, 2022
Length: 330 pages
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Language : English
ISBN-13 : 9781800561564
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Tools :

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Frequently bought together


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Table of Contents

15 Chapters
Part 1: The Data Science Landscape – Open Source to the Rescue Chevron down icon Chevron up icon
Chapter 1: Understanding the AI/ML landscape Chevron down icon Chevron up icon
Chapter 2: Analyzing Open Source Software Chevron down icon Chevron up icon
Chapter 3: Using the Anaconda Distribution to Manage Packages Chevron down icon Chevron up icon
Chapter 4: Working with Jupyter Notebooks and NumPy Chevron down icon Chevron up icon
Part 2: Data Is the New Oil, Models Are the New Refineries Chevron down icon Chevron up icon
Chapter 5: Cleaning and Visualizing Data Chevron down icon Chevron up icon
Chapter 6: Overcoming Bias in AI/ML Chevron down icon Chevron up icon
Chapter 7: Choosing the Best AI Algorithm Chevron down icon Chevron up icon
Chapter 8: Dealing with Common Data Problems Chevron down icon Chevron up icon
Part 3: Practical Examples and Applications Chevron down icon Chevron up icon
Chapter 9: Building a Regression Model with scikit-learn Chevron down icon Chevron up icon
Chapter 10: Explainable AI - Using LIME and SHAP Chevron down icon Chevron up icon
Chapter 11: Tuning Hyperparameters and Versioning Your Model Chevron down icon Chevron up icon
Other Books You May Enjoy Chevron down icon Chevron up 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
Jerimiah Jun 21, 2022
Full star icon Full star icon Full star icon Full star icon Full star icon 5
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!
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
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
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