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This is the code repository for Building Data Science Solutions with Anaconda, published by Packt.
A comprehensive starter guide to building robust and complete models
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.
This book covers the following exciting features:
- 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
If you feel this book is for you, get your copy today!
All of the code is organized into folders.
The code will look like the following:
from sklearn.model_selection import train_test_split
training_data = cali_data.data
target_value = cali_data.target
X_train, X_test, y_train, y_test = train_test_ split(training_data, target_value, test_size = 0.2, random_state=5)
Following is what you need for this book: 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.
With the following software and hardware list you can run all code files present in the book (Chapter 1-11).
| Chapter | Software required | OS required |
|---|---|---|
| 1-11 | Anaconda Distribution (conda and Navigator) | Windows, Mac OS X, and Linux (Any) |
| 1-11 | Scikit-learn, NumPy, and pandas | Windows, Mac OS X, and Linux (Any) |
| 1-11 | SHAP and LIME | Windows, Mac OS X, and Linux (Any) |
We also provide a PDF file that has color images of the screenshots/diagrams used in this book. Click here to download it.
Dan Meador is an engineering manager at Anaconda leading the conda team and championing open source. He also holds a patent for his work on AI systems and has grown his experience in AI/ML by creating AutoML solutions. He has seen how the power of data can work in everything from startups to Fortune 10 companies.
If you have already purchased a print or Kindle version of this book, you can get a DRM-free PDF version at no cost.
Simply click on the link to claim your free PDF.