Building Data Science Solutions with Anaconda

A complete guide to not only training models, but also working with practical tools and techniques to succeed in your data science career

Key Features

• Discover the modern data science stack, and how to quickly get up and running with it using Anaconda
• Understand the open source landscape and how to evaluate new tools that come along
• Understand what bias to look out for in your models and data and learn how to correct it

Book Description

Software is eating the world, and AI is eating software. Anaconda enables data scientists and developers to use the most popular tools for building robust models.

This book helps you explore data science tools used by the top companies in the world, along with covering how to use Anaconda Navigator to manage packages and grab data from various sources. You'll learn how to build, train, and deploy models to solve common use cases, and then focus on how to ensure that those models can be versioned and updated. The book will also guide you through everything, from extracting data and choosing a model, to exploring advanced concepts such as Explainable AI and building a recommendation engine.

By the end of this book, you'll have learned how to find and use the most popular and widely used OSS data science tools, integrate with multiple data sources to build and train models, and navigate the bottlenecks that arise with models in production.

What you will learn

• Use Conda to find packages and manage environments
• Understand how to use Navigator as an alternative graphical user interface (GUI)
• Integrate with multiple Python tools to build and train machine learning models
• Become well-versed with using different libraries such as NumPy, pandas, and so on
• Address common bottlenecks of AI-enabled models using practical examples
• Build and refine data science models and use version control to iterate

Who This Book Is For

This book is for data analysts and data science professionals looking to get well-versed with the powerful data science platform, Anaconda. Prior experience with Python and git and beginner-level knowledge of data science are expected.

Table of Contents

1. AI is Eating Software
2. Analyzing Open-Source Software
3. Using the Anaconda Distribution to Manage Packages
4. Working with Jupyter Notebooks and Numpy
5. Cleaning and Visualizing Data
6. Overcoming Bias in AI/ML
7. Choosing the Best AI Algorithm
8. Dealing with Common Data Problems
9. Building a Regression Model with scikit-learn and Visualizing with Bokeh
10. Interpretable AI - Using LIME and SHAP
11. Deploying and Versioning Your Model